survivorship bias

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Quantitative Trading: How to Build Your Own Algorithmic Trading Business by Ernie Chan

algorithmic trading, asset allocation, automated trading system, backtesting, Black Swan, Brownian motion, business continuity plan, buy and hold, compound rate of return, Edward Thorp, Elliott wave, endowment effect, fixed income, general-purpose programming language, index fund, John Markoff, Long Term Capital Management, loss aversion, p-value, paper trading, price discovery process, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Ray Kurzweil, Renaissance Technologies, risk-adjusted returns, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, systematic trading, transaction costs

Despite my frequent admonitions here and elsewhere to beware of historical data with survivorship bias, when I first started I downloaded only the split-and-dividend-adjusted Yahoo! Finance data TABLE 2.2 How Capital Availability Affects Your Many Choices Low Capital High Capital Proprietary trading firm’s membership Futures, currencies, options Intraday Directional Small stock universe for intraday trading Daily historical data with survivorship bias Low-coverage or delayed news source No historical news database Retail brokerage account Everything, including stocks Both intra- and interday (overnight) Directional or market neutral Large stock universe for intraday trading High-frequency historical data, survivorship bias–free High-coverage, real-time news source Survivorship bias–free historical news database Survivorship bias–free historical fundamental data on stocks No historical fundamental data on stocks P1: JYS c02 JWBK321-Chan 16 September 24, 2008 13:47 Printer: Yet to come QUANTITATIVE TRADING using the download program from HQuotes.com (more on the different databases and tools in Chapter 3).

HQuotes.com CSIdata.com TrackData.com CRSP.com Low cost. Same data as finance.yahoo.com. Software enables download of multiple symbols. Low cost. Source of Yahoo! and Google’s historical data. Software enables download of multiple symbols. Low cost. Split/dividend adjusted. Software enables download of multiple symbols. Fundamental data available. Survivorship bias free. Has survivorship bias. Can download only one symbol at a time. Has survivorship bias. Split but not dividend adjusted. Has survivorship bias. Has survivorship bias. Expensive. Updated only once a month. Daily Futures Data Quotes-plus.com CSIdata.com Oanda.com Low cost. Software enables download of multiple symbols. (See above.) Daily Forex Data Free. Intraday Stock Data HQuotes.com (See above.) Short history for intraday data. Intraday Futures Data DTN.com Bid-ask data history available as part of NxCore product.

Are the Data Survivorship Bias Free? We already covered this issue in Chapter 2. Unfortunately, databases that are free from survivorship bias are quite expensive and may not be affordable for a start-up business. One way to overcome this problem is to start collecting point-in-time data yourself for the benefit of your future backtest. If you save the prices each day of all the stocks in your universe to a file, then you will have a point-in-time or survivorship-bias-free database to use in the future. Another way to lessen the impact of survivorship bias is to backtest your strategies on more recent data so that the results are not distorted by too many missing stocks. P1: JYS c03 JWBK321-Chan September 24, 2008 13:52 Printer: Yet to come Example 3.3: An Example of How Survivorship Bias Can Artificially Inflate a Strategy’s Performance Here is a toy “buy low-price stocks” strategy (Warning: This toy strategy is hazardous to your financial health!).


Commodity Trading Advisors: Risk, Performance Analysis, and Selection by Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah

Asian financial crisis, asset allocation, backtesting, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, compound rate of return, constrained optimization, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, discrete time, distributed generation, diversification, diversified portfolio, dividend-yielding stocks, fixed income, high net worth, implied volatility, index arbitrage, index fund, interest rate swap, iterative process, linear programming, London Interbank Offered Rate, Long Term Capital Management, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, p-value, Pareto efficiency, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, stochastic process, survivorship bias, systematic trading, technology bubble, transaction costs, value at risk, zero-sum game

There are nine negative coefficients in total representing 14 percent of the coefficients. SURVIVORSHIP BIAS Performance figures are subject to various biases. One of the most important is the survivorship bias that appears when only surviving funds are taken into account in a performance analysis study. The common practice among suppliers of CTA databases is to provide data on investable funds that are currently in operation. When only living funds4 are considered, the data suffer from survivorship bias because dissolved funds tend to have worse performance than surviving funds. Survivorship bias has already been studied. Fung and Hsieh (1997b) precisely analyzed this bias and estimated it at 3.4 percent per year. They also concluded that survivorship bias had little impact on the investment styles of CTA funds. Returns of both surviving and dissolved CTA funds have low correlation to the standard asset classes.

Returns of both surviving and dissolved CTA funds have low correlation to the standard asset classes. Survivorship Bias over Various Time Periods Here we analyze the presence of survivorship bias in CTAs returns over various long-term time periods. We first study the whole period covered before dividing it into subperiods. Table 4.4 reports the survivorship bias obtained from our database. Survivorship bias is calculated as the performance difference between surviving funds and all funds. All returns are monthly and net of all fees. The first part of the table indicates a survivorship bias of 5.4 percent per year for the entire period. This figure is higher than the one obtained in previous studies. Table 4.4 shows the bias was higher during the 1990 to 1994 period (7.3 percent) and during the 1995 to 1999 period (6.2 percent) but lower during the 2000 to 2003 period (4.4 percent). 4By “living funds” we mean funds still in operation at the moment of the analysis.

Table 4.4 shows the bias was higher during the 1990 to 1994 period (7.3 percent) and during the 1995 to 1999 period (6.2 percent) but lower during the 2000 to 2003 period (4.4 percent). 4By “living funds” we mean funds still in operation at the moment of the analysis. CTA Performance, Survivorship Bias, and Dissolution Frequencies 57 TABLE 4.4 Survivorship Bias Analysis over Different Periods Bias 1985–2003 Bias 1985–1989 Bias 1990–1994 Bias 1995–1999 Bias 2000–2003 0.5 5.4 0.5 5.5 0.6 7.3 0.5 6.2 0.4 4.4 per Month per Year per Month per Year per Month per Year per Month per Year per Month per Year Our database contains 1,899 CTAs (611 survived funds and 1,288 dissolved funds as of December 2002). Survivorship Bias over Time Figure 4.1 reports the evolution of the survivorship bias calculated on a three-year rolling period starting January 1985 to December 1987 and ending January 2000 to December 2002. It allows us to analyze more precisely how the survivorship evolves over time. 1.00 0.90 0.80 0.70 0.60 0.50 0.40 0.30 0.20 0.10 0.00 88 88 89 89 90 90 91 91 92 92 93 93 94 94 95 95 96 96 97 97 98 98 99 99 00 00 01 01 02 02 FIGURE 4.1 Evolution of the Survivorship Bias (3-year Rolling Period) Our database contains 1,899 CTAs (611 survived funds and 1,288 dissolved funds as of December 2002).


Triumph of the Optimists: 101 Years of Global Investment Returns by Elroy Dimson, Paul Marsh, Mike Staunton

asset allocation, banking crisis, Berlin Wall, Bretton Woods, British Empire, buy and hold, capital asset pricing model, capital controls, central bank independence, colonial rule, corporate governance, correlation coefficient, cuban missile crisis, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, European colonialism, fixed income, floating exchange rates, German hyperinflation, index fund, information asymmetry, joint-stock company, negative equity, new economy, oil shock, passive investing, purchasing power parity, random walk, risk tolerance, risk/return, selection bias, shareholder value, Sharpe ratio, stocks for the long run, survivorship bias, technology bubble, transaction costs, yield curve

In recent years, both practitioners and researchers have grown increasingly uneasy about how to interpret these widely cited estimates, largely because they seemed too high. Apart from biases in index construction—a possibility that had not previously been thought important, but which we saw in section 3.2 is material in relation to the UK figure—the finger of suspicion has pointed mainly at success and survivorship bias among countries. The concern over success bias is that inferences about risk premia worldwide were being heavily influenced by the US experience, yet the United States has been an unusually successful economy. The closely related worry over survivorship bias is that previous attempts to place the experience of other countries like the United Kingdom alongside that of the United States may still have overstated the risk premium since they have focused on just a few selected markets that have survived, typically with continuous trading, over a long period.

To learn more, it is revealing to look at the United Kingdom, and to research as long a period as possible. 10.2 Value and growth investing in the United Kingdom We now dig deeper into the relative performance of value and growth investing in the UK stock market. We start with the more recent period considered in chapter 9. Our analysis uses a database of balance sheets for all firms listed on the London Stock Exchange since 1953. This data, compiled by Nagel (2001), enables us to look at value effects across the entire population of UK listed stocks over nearly half a century. The source data is free of survivorship bias, and covers some one hundred thousand firm-years of accounting data. Nagel’s database is comparable, and in some ways superior, to the US Compustat data. For example, whereas Davis, Fama, and French (2000) have accounting information on 339 NYSE firms in 1929 and 834 firms in 1955, we have accounting data on some 3,500 UK companies in 1956, and use the UK accounting data in conjunction with the entire stock return series in the comprehensive London Share Price Database (LSPD), covering 1955–2000.

The closely related worry over survivorship bias is that previous attempts to place the experience of other countries like the United Kingdom alongside that of the United States may still have overstated the risk premium since they have focused on just a few selected markets that have survived, typically with continuous trading, over a long period. To provide better estimates of the equity risk premium we therefore need to focus on the experience of all countries, not just the United States and the United Kingdom. If we look at all markets, then survivorship bias ceases to be an issue. Our sample of sixteen countries is by no means comprehensive. However, it does represent a large proportion by value of the world’s stock markets in 1900. Fortunately, we are also able to compute total returns, including reinvested dividends, for this remarkably large sample of countries over a full 101year period. This has allowed us to estimate long-run risk premia over an extended and uniform research period, thus overcoming another important but overlooked factor in previous studies, namely, easy data bias (see section 3.4).


pages: 317 words: 106,130

The New Science of Asset Allocation: Risk Management in a Multi-Asset World by Thomas Schneeweis, Garry B. Crowder, Hossein Kazemi

asset allocation, backtesting, Bernie Madoff, Black Swan, business cycle, buy and hold, capital asset pricing model, collateralized debt obligation, commodity trading advisor, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index fund, interest rate swap, invisible hand, market microstructure, merger arbitrage, moral hazard, Myron Scholes, passive investing, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, statistical model, stocks for the long run, survivorship bias, systematic trading, technology bubble, the market place, Thomas Kuhn: the structure of scientific revolutions, transaction costs, value at risk, yield curve, zero-sum game

For example, research (Fung and Hsieh, 2006) has shown that, if the first year or so of performance is removed from a fund reporting to a database, the impact of backfill bias is removed dramatically. Similarly, most hedge fund indices do not contain survivorship bias or backfill bias, as managers reporting to the database at any one time are used. Historical index returns are not changed when these managers are removed from the database and therefore do not reflect survivorship bias. Likewise, as new managers are added to the database, the historical index returns are not changed in order to reflect those new managers and corresponding historical index returns. Hence, no backfill bias is contained in the many indices.3 The impact of survivorship bias and backfill bias, as well as the impact of the use of hedge fund indices to reflect the performance of individual hedge funds, is shown in Exhibit 8.13 for Equity Long/Short hedge funds (other strategies are not shown in this report but results for other strategies are similar and are available from the authors).

Academics have also addressed various aspects of concern including the degree to which various benchmarks may overestimate actual historical returns due to failure of the indices/benchmarks to correct for backfill bias (historical benchmark data includes current reporting managers); survival bias (managers who leave, generally due to poor performance, leave the database and the index is recalculated). Most indices, including most hedge fund and managed futures indices, are not recalculated when current managers leave or new managers enter (begin reporting to the data base) In brief, individuals should be aware of the actual construction issues relating to the return calculation for each benchmark used in the asset allocation process. 3. Note that the period before the data inception of an index may contain survivorship and backfill bias. For instance, if an index was started in 2002, returns pre-2002 would contain backfill bias and survivorship bias. CHAPTER 9 Risk Budgeting and Asset Allocation sset allocation and risk management are about finding the right balance of risk and return.

The often higher historical returns to funds listed in the current database are often reported to be due to several biases (Fung and Hsieh, 2006) in database construction such as (1) backfill bias/incubation bias (the historical returns of new funds reporting to the database are included in the database. Since, in most cases, only funds with superior historical returns report their returns to databases, the returns before their database entry date may be biased upward relative to all those funds that do not report) and (2) survivorship bias: Funds that used to exist historically in 192 THE NEW SCIENCE OF ASSET ALLOCATION the database are removed from it when they stop reporting. Often these funds stop reporting because of poor returns. The often lower returns of these funds are not contained in the live portion of most databases and one must ask for the dead fund databases in order to measure the actual returns to investment in funds that may have existed in the past.


pages: 111 words: 1

Fooled by Randomness: The Hidden Role of Chance in Life and in the Markets by Nassim Nicholas Taleb

Antoine Gombaud: Chevalier de Méré, availability heuristic, backtesting, Benoit Mandelbrot, Black Swan, commoditize, complexity theory, corporate governance, corporate raider, currency peg, Daniel Kahneman / Amos Tversky, discounted cash flows, diversified portfolio, endowment effect, equity premium, fixed income, global village, hedonic treadmill, hindsight bias, Kenneth Arrow, Long Term Capital Management, loss aversion, mandelbrot fractal, mental accounting, meta analysis, meta-analysis, Myron Scholes, Paul Samuelson, quantitative trading / quantitative finance, QWERTY keyboard, random walk, Richard Feynman, road to serfdom, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, Steven Pinker, stochastic process, survivorship bias, too big to fail, Turing test, Yogi Berra

How Victor Niederhoffer taught me empiricism; I added deduction. Why it is not scientific to take science seriously. Soros promotes Popper. That bookstore on Eighteenth Street and Fifth Avenue. Pascal’s wager. EIGHT: TOO MANY MILLIONAIRES NEXT DOOR Three illustrations of the survivorship bias. Why very few people should live on Park Avenue. The millionaire next door has very flimsy clothes. An overcrowding of experts. NINE: IT IS EASIER TO BUY AND SELL THAN FRY AN EGG Some technical extensions of the survivorship bias. On the distribution of “coincidences” in life. It is preferable to be lucky than competent (but you can be caught). The birthday paradox. More charlatans (and more journalists). How the researcher with work ethics can find just about anything in data. On dogs not barking. TEN: LOSER TAKES ALL—ON THE NONLINEARITIES OF LIFE The nonlinear viciousness of life.

Arguably, in expectation, a dentist is considerably richer than the rock musician who is driven in a pink Rolls Royce, the speculator who bids up the price of impressionist paintings, or the entrepreneur who collects private jets. For one cannot consider a profession without taking into account the average of the people who enter it, not the sample of those who have succeeded in it. We will examine the point later from the vantage point of the survivorship bias, but here, in Part I, we will look at it with respect to resistance to randomness. Consider two neighbors, John Doe A, a janitor who won the New Jersey lottery and moved to a wealthy neighborhood, compared to John Doe B, his next-door neighbor of more modest condition who has been drilling teeth eight hours a day over the past thirty-five years. Clearly one can say that, thanks to the dullness of his career, if John Doe B had to relive his life a few thousand times since graduation from dental school, the range of possible out-comes would be rather narrow (assuming he is properly insured).

He gave the appearance of someone I would trust with my savings—indeed he rose quite rapidly in the institution in spite of his lack of technical competence in financial derivatives (his firm’s claim to fame). But he was too much a no-nonsense person to make out my logic. He once blamed me for not being impressed with the successes of some of his traders who did well during the bull market for European bonds of 1993, whom I openly considered nothing better than random gunslingers. I tried presenting him with the notion of survivorship bias (Part II of this book) in vain. His traders have all exited the business since then “to pursue other interests” (including him). But he gave the appearance of being a calm, measured man, who spoke his mind and knew how to put the other person at ease during a conversation. He was articulate, extremely presentable thanks to his athletic looks, well measured in his speech, and endowed with the extremely rare quality of being an excellent listener.


pages: 490 words: 117,629

Unconventional Success: A Fundamental Approach to Personal Investment by David F. Swensen

asset allocation, asset-backed security, buy and hold, capital controls, cognitive dissonance, corporate governance, diversification, diversified portfolio, fixed income, index fund, law of one price, Long Term Capital Management, market bubble, market clearing, market fundamentalism, money market fund, passive investing, Paul Samuelson, pez dispenser, price mechanism, profit maximization, profit motive, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Silicon Valley, Steve Ballmer, stocks for the long run, survivorship bias, technology bubble, the market place, transaction costs, Vanguard fund, yield curve, zero-sum game

Assuming that active managers of hedge funds achieve success levels similar to active managers of traditional marketable securities, investors in hedge funds face dramatically higher levels of prospective failure due to the materially higher levels of fees. Survivorship Bias Statistics on past performance of hedge funds fail to provide much insight into the character of this relatively new segment of the investment world. Survivorship bias represents a pervasive problem for gatherers of historical return data. The fact that poorly performing firms fail at higher rates than well-performing firms causes data on manager returns to overstate past results, since compilations of data at any point in time from the current group of managers frequently lack complete performance numbers from firms that failed in the past. In the well-established, comprehensively documented world of traditional marketable securities, survivorship bias presents a significant, albeit quantifiable problem. In the less-well-established, less comprehensively documented arena of hedge fund investing, survivorship bias creates a much more substantial informational challenge.

Table A.2 Performance of the S&P 500 versus the Wilshire 5000 (Percent) Sources: Bloomberg; Wilshire Associates. Note: Data reflect periods ending December 31, 1998. In those parts of the Arnott study that present returns without survivorship bias, the measured underperformance of active managers likely overstates reality by a margin approximately equal to the performance differential between the S&P 500 and the Wilshire 5000. For example, because Arnott removed survivor bias from the data in Table 7.2 and Table 7.5, the data exaggerate the size of performance shortfalls measured against a fair benchmark. In those parts of the study that calculate the odds of winning and losing along with the average margins of victory and defeat, survivorship bias enters the picture. (This part of the study excludes funds that disappear, because the authors need a full-period record to calculate the results.)

Yale School of Management professor Roger Ibbotson produces a widely used set of capital market statistics that reflect a seventy-eight-year stock-and-bond return differential of 5.0 percent per annum.1 Wharton professor Jeremy Siegel’s two hundred years of data show a risk premium of 3.4 percent per annum.2 Regardless of the precise number, historical risk premiums indicate that equity owners enjoyed a substantial return advantage over bondholders.* The size of the risk premium proves critically important in the asset-allocation decision. While history provides a guide, careful investors interpret past results with care. Work on survivorship bias by Phillipe Jorion and William Goetzmann demonstrates the unusual nature of the U.S. equity market experience. The authors examine the experience of thirty-nine markets over a seventy-five-year period, noting that “major disruptions have afflicted nearly all of the markets in our sample, with the exception of a few such as the United States.”3 The more or less uninterrupted operation of the U.S. stock market in the nineteenth and twentieth centuries contributed to superior results.


pages: 345 words: 87,745

The Power of Passive Investing: More Wealth With Less Work by Richard A. Ferri

asset allocation, backtesting, Bernie Madoff, buy and hold, capital asset pricing model, cognitive dissonance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, endowment effect, estate planning, Eugene Fama: efficient market hypothesis, fixed income, implied volatility, index fund, intangible asset, Long Term Capital Management, money market fund, passive investing, Paul Samuelson, Ponzi scheme, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve, zero-sum game

Nonetheless, Cowles updated his study in 1944 using 11 of the original 24 forecasters that survived through July 1943. He concluded again that these 11 leading financial firms “failed to disclose evidence of ability to predict successfully the future course of the stock market.”3 Cowles reported that 6 of the 11 surviving forecasters outperformed a random sampling of stocks by 0.2 percent over the period. However, there was likely a strong survivorship bias in the data that resulted in a deceptively high average return for the remaining entities. Survivorship bias occurs in performance data when the entire return histories of non-surviving entities are deleted from the database as these entities cease to exist. Cowles didn’t report the partial period returns from the 13 forecasters who dropped out over the years because of poor performance. Had they been included in the years there was data available, the average return for all the forecasters would likely have been much lower than Cowles reported for only the surviving forecasters.

Closed and merged funds were not included; however, studies on closed and merged fund performance show that a large majority of these funds considerably underperformed their benchmarks in the years leading up to their demise. Chapter 6 provides performance data on terminated mutual funds prior to closing or merging. Including terminated fund performance in the data up to each fund’s termination date would eliminate the survivorship bias and change the outcome in Figure 3.1. Without survivorship bias, the Vanguard 500 Index Fund beat over 85 percent of actively managed funds during the 25 year period. The second strike against active funds in this sample is the small excess returns of the winning active funds relative to larger shortfalls from the losing funds. The excess return for picking a winning active fund is far below a fair payout given the high probability of selecting a losing fund and the average shortfall from losing funds.

The following is in his conclusion in the Journal of Finance: Overall, the evidence is consistent with market efficiency, interpretations of the size, book-to-market, and momentum factors notwithstanding. Although the top-decile mutual funds earned back their investment costs, most funds underperform by about the magnitude of their investment expenses. The bottom-decile funds, however, underperform by about twice their reported investment costs.16 One added benefit from Carhart’s exhaustive study on mutual fund performance was the creation of the first survivorship-bias-free mutual fund database. The database was initially funded by Eugene Fama and compiled by Carhart. Unlike other databases at the time, the CRSP Survivor-Bias-Free US Mutual Fund Database included the returns of closed and merged funds. This represented the true opportunity set that investors had to choose from over the years.b Fama and French also included a four-factor model in their recent study on mutual funds mentioned earlier.


pages: 363 words: 28,546

Portfolio Design: A Modern Approach to Asset Allocation by R. Marston

asset allocation, Bretton Woods, business cycle, capital asset pricing model, capital controls, carried interest, commodity trading advisor, correlation coefficient, diversification, diversified portfolio, equity premium, Eugene Fama: efficient market hypothesis, family office, financial innovation, fixed income, German hyperinflation, high net worth, hiring and firing, housing crisis, income per capita, index fund, inventory management, Long Term Capital Management, mortgage debt, passive investing, purchasing power parity, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sharpe ratio, Silicon Valley, stocks for the long run, superstar cities, survivorship bias, transaction costs, Vanguard fund

But it’s natural to ask the question: If the Malkiel-Saha estimates are measuring returns that are not truly backfilled, but merely previously missing from that database, why are those returns so much lower than the non back-filled returns? Survivorship bias is potentially quite large given the high rates of exit from the industry. Consider first how many funds survive over time. Malkiel and Saha (2005) use the TASS database to follow firms through time from the first date that they entered the database. (So no backfilled returns are used). They divide firms into the live firms that continued to exist at the end of the data set, December 2003, from the defunct firms that dropped out of the data set prior to that date. To determine the resulting survivorship bias, it’s necessary to compare the returns of the live firms with the live and defunct firms together. In Table 9.6, Malkiel and Saha estimate survivorship bias to be 4.4 percent. Fung and Hsieh (2006) measure survivorship bias using their three databases from 1994 to 2004.

They do this by P1: a/b c09 P2: c/d QC: e/f JWBT412-Marston T1: g December 20, 2010 17:1 Printer: Courier Westford 180 PORTFOLIO DESIGN TABLE 9.6 Biases in Hedge Fund Returns in TASS Database Malkeil and Saha (2005) Estimates Fung and Hsieh (2006) Estimates 1994–2003 1994–2004 Backfill Bias Backfilled Returns Non-Backfilled Bias 14.6% 7.3% 7.3% Backfill Bias All Funds Exclude 1st 14 Months Bias 12.0% 10.5% 1.5% Survivorship Bias Live Funds∗ Live and Defunct∗ Bias 13.7% 9.3% 4.4% Survivorship Bias Live Funds Live and Defunct Bias 14.4% 12.0% 2.4% ∗ The returns for the live and defunct funds exclude backfilled returns. The estimates of survivorship bias are for 1996 to 2003. examining the dropout rates for hedge funds in three databases, TASS, HFR, and CISDM. Using a data set of hedge funds over the period 1994-2004, they find that the highest dropout rate occurs at about 14 months. They then eliminate the first 14 months of returns for the hedge funds in the three databases.

This bias refers to the fact that managers of hedge funds that are failing are likely to stop reporting returns to a database before the final liquidation value of the fund is realized. For example, the funds that lost their capital in the Russian debt crisis of August 1998 did not report returns of –100 percent in that month. Instead, the returns ended in July 1998.17 Any attempts to adjust for survivorship bias will miss the liquidation bias when the fund closes down. Quantitative estimates of backfill bias range widely from one study to another. That’s because the methodology for determining the bias varies as well. Malkiel and Saha (2005) estimate backfill and survivorship bias using the TASS database from 1994 to 2003. The TASS database distinguishes between returns that have been backfilled into the TASS database from returns subsequently recorded by the same fund. And it keeps track of defunct funds as well as the funds still alive in each year.


Monte Carlo Simulation and Finance by Don L. McLeish

Black-Scholes formula, Brownian motion, capital asset pricing model, compound rate of return, discrete time, distributed generation, finite state, frictionless, frictionless market, implied volatility, incomplete markets, invention of the printing press, martingale, p-value, random walk, Sharpe ratio, short selling, stochastic process, stochastic volatility, survivorship bias, the market place, transaction costs, value at risk, Wiener process, zero-coupon bond, zero-sum game

From this it is easy to see that the conditional cumulative distribution function of L given C = u, H = b is given by on a · u · b (where −2φ0 (2b − u) is the joint p.d.f. of H, C) by F (a|b, u) = 1 + = ∂2 ∂b∂v P (a, b, u, v)| v=u 2φ0 (2b − u) (5.33) ∞ X −1 {−kφ0 [u + 2k(b − a)] + (1 + k)φ0 [2b − u + 2k(b − a)] 0 φ (2b − u) k=1 0 + kφ [u − 2k(b − a)] + (1 − k)φ0 [2b − u − 2k(b − a)]} This allows us to simulate both the high and the low, given the open and the close by first simulating the high and the close using −2φ0 (2b − u) as the joint p.d.f. of (H, C) and then simulating the low by inverse transform from the cumulative distribution function of the form (5.33). Survivorship Bias It is quite common for retrospective studies in finance, medicine and to be subject to what is often called “survivorship bias”. This is a bias due to the fact that only those members of a population that remained in a given class (for example the survivors) remain in the sampling frame for the duration of the study. In general, if we ignore the “drop-outs” from the study, we do so at risk of introducing substantial bias in our conclusions, and this bias is the survivorship bias. SURVIVORSHIP BIAS 291 Suppose for example we have hired a stable of portfolio managers for a large pension plan. These managers have a responsibility for a given portfolio over a period of time during which their performance is essentially under continuous review and they are subject to one of several possible decisions.

The intuitive reason for this dramatic increase is quite simple. For large values of σ the process fluctuates more, and only those SURVIVORSHIP BIAS 295 Figure 5.8: E[C|L ≥ 30] for various values of (µ, σ) chosen such that E(C) = 56.25. paths with very large values of C have abeen able to avoid the absorbing barrier at l = 30. Two comparable portfolios with unconditional return about 40% will show radically different apparent returns in the presence of an absorbing barrier. If σ = 20% then the survivor’s return will still average around 40%, but if σ = 0.8, the survivor’s returns average close to 150%. The practical implications are compelling. If there is any form of survivorship bias (as there usually is), no measure of performance should be applied to the returns from different investments, managers, or portfolios without an adjustment for the risk or volatility.

Note that if the mean µ of the unconditional density approaches the barrier (here at 30) , this region approaches a narrow band along the top of the curve and to the right of 30. Similarly if the unconditional standard deviation or volatility increases, the unshaded region stretches out to the right in a narrow band and the conditional mean increases. We arrive at the following seemingly paradoxical conclusions which make it imperative to adjust for survivorship bias: the conditional mean, conditional on survivorship, may increase as the volatility increases even if the unconditional SURVIVORSHIP BIAS 297 mean decreases. Let us return to the problem with both an upper and lower barrier and consider the distribution of returns conditional on the low never passing a barrier Oe−a and the high never crossing a barrier at Oeb ( representing a fund buyout, recruitment of manager by competitor or promotion of fund manager to Vice President).


Super Thinking: The Big Book of Mental Models by Gabriel Weinberg, Lauren McCann

affirmative action, Affordable Care Act / Obamacare, Airbnb, Albert Einstein, anti-pattern, Anton Chekhov, autonomous vehicles, bank run, barriers to entry, Bayesian statistics, Bernie Madoff, Bernie Sanders, Black Swan, Broken windows theory, business process, butterfly effect, Cal Newport, Clayton Christensen, cognitive dissonance, commoditize, correlation does not imply causation, crowdsourcing, Daniel Kahneman / Amos Tversky, David Attenborough, delayed gratification, deliberate practice, discounted cash flows, disruptive innovation, Donald Trump, Douglas Hofstadter, Edward Lorenz: Chaos theory, Edward Snowden, effective altruism, Elon Musk, en.wikipedia.org, experimental subject, fear of failure, feminist movement, Filter Bubble, framing effect, friendly fire, fundamental attribution error, Gödel, Escher, Bach, hindsight bias, housing crisis, Ignaz Semmelweis: hand washing, illegal immigration, income inequality, information asymmetry, Isaac Newton, Jeff Bezos, John Nash: game theory, lateral thinking, loss aversion, Louis Pasteur, Lyft, mail merge, Mark Zuckerberg, meta analysis, meta-analysis, Metcalfe’s law, Milgram experiment, minimum viable product, moral hazard, mutually assured destruction, Nash equilibrium, Network effects, nuclear winter, offshore financial centre, p-value, Parkinson's law, Paul Graham, peak oil, Peter Thiel, phenotype, Pierre-Simon Laplace, placebo effect, Potemkin village, prediction markets, premature optimization, price anchoring, principal–agent problem, publication bias, recommendation engine, remote working, replication crisis, Richard Feynman, Richard Feynman: Challenger O-ring, Richard Thaler, ride hailing / ride sharing, Robert Metcalfe, Ronald Coase, Ronald Reagan, school choice, Schrödinger's Cat, selection bias, Shai Danziger, side project, Silicon Valley, Silicon Valley startup, speech recognition, statistical model, Steve Jobs, Steve Wozniak, Steven Pinker, survivorship bias, The Present Situation in Quantum Mechanics, the scientific method, The Wisdom of Crowds, Thomas Kuhn: the structure of scientific revolutions, transaction costs, uber lyft, ultimatum game, uranium enrichment, urban planning, Vilfredo Pareto, wikimedia commons

Surveys like this also do not usually account for the opinions of former employees, which can create another bias in the results called survivorship bias. Unhappy employees may have chosen to leave the company, but you cannot capture their opinions when you survey only current employees. Results are therefore biased based on measuring just the population that survived, in this case the employees remaining at the company. Do these biases invalidate this survey methodology? Not necessarily. Almost every methodology has drawbacks, and bias of one form or another is often unavoidable. You should just be aware of all the potential issues in a study and consider them when drawing conclusions. For example, knowing about the survivorship bias in remaining employees, you could examine the data from exit interviews to see whether motivation issues were mentioned by departing employees.

For example, knowing about the survivorship bias in remaining employees, you could examine the data from exit interviews to see whether motivation issues were mentioned by departing employees. You could even try to survey them too. A few other examples can further illustrate how subtle survivorship bias can be. In World War II, naval researchers conducted a study of damaged aircraft that returned from missions, so that they could make suggestions as to how to bolster aircraft defenses for future missions. Looking at where these planes had been hit, they concluded that areas where they had taken the most damage should receive extra armor. However, statistician Abraham Wald noted that the study sampled only planes that had survived missions, and not the many planes that had been shot down. He therefore theorized the opposite conclusion, which turned out to be correct: that the areas with holes represented areas where aircraft could be shot and still return safely, whereas the areas without holes probably contained areas that, if hit, would cause the planes to go down.

However, you’d be thinking only of the people that “survived.” You’re missing all the dropouts who did not make it to the top. Architecture presents a more everyday example: Old buildings generally seem to be more beautiful than their modern counterparts. Those buildings, though, are the ones that have survived the ages; there were slews of ugly ones from those time periods that have already been torn down. Survivorship Bias When you critically evaluate a study (or conduct one yourself), you need to ask yourself: Who is missing from the sample population? What could be making this sample population nonrandom relative to the underlying population? For example, if you want to grow your company’s customer base, you shouldn’t just sample existing customers; that sample doesn’t account for the probably much larger population of potential customers.


pages: 297 words: 91,141

Market Sense and Nonsense by Jack D. Schwager

3Com Palm IPO, asset allocation, Bernie Madoff, Brownian motion, buy and hold, collateralized debt obligation, commodity trading advisor, computerized trading, conceptual framework, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, fixed income, high net worth, implied volatility, index arbitrage, index fund, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, negative equity, pattern recognition, performance metric, pets.com, Ponzi scheme, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, short selling, statistical arbitrage, statistical model, survivorship bias, transaction costs, two-sided market, value at risk, yield curve

The key point, however, is that, as has been detailed by academic researchers William Fung and David Hsieh, fund of funds indexes eliminate or significantly mitigate hedge fund index biases.2 We now reconsider the foregoing biases from a fund of funds perspective. Survivorship bias. This bias is eliminated for single funds at the fund of funds level because defunct fund results remain reflected in historical fund of funds data. If a fund of funds was invested in a fund that blew up, the fund may disappear from some databases, but the losses incurred by the fund will remain reflected in the fund of funds track record. Although there may still be a survivorship bias at the fund of funds level (that is, defunct fund of funds), the effect will be much more muted than for single funds because the difference between a defunct and an active fund of funds is much smaller.

If I had drawn samples that included defunct managers, perhaps monthly rebalancing, which effectively was equivalent to taking from the winners and giving to the losers, would not have been beneficial. Possibly, it might even have been detrimental. In short, my original analysis was subject to survivorship bias. There was no way of telling whether the apparent large benefit I found in rebalancing in my original analysis was sufficient to overcome this bias. To test the rebalancing concept in a new analysis that avoided survivorship bias, I obtained the complete CTA database from Stark & Company (www.starkresearch.com)—a data set that included both existing and defunct managers. I randomly selected 10 portfolios of 10 managers each from this complete list,2 assuming equal allocations and a January 1, 2005, start date.

Roughly speaking, fund of funds fees account for less than one-third of the historical performance gap vis-à-vis single-fund indexes (the exact percentage will vary by data vendor). So the question of whether fund of funds managers do worse than random in their fund selections remains. The real crux of the explanation for why indexes based on funds of funds underperform indexes constructed from single funds relates to hedge fund index biases, which are far more pronounced in single-fund indexes. These biases include: Survivorship bias. This effect is perhaps the best-known bias since it has been the subject of numerous academic articles written over many years. Essentially, if an index fails to retain defunct funds, it will tend to be upwardly biased because poorer-performing funds will have a greater tendency to cease operation. A number of indexes now correct for this bias, so while this bias is significant if present, it has become less important.


pages: 274 words: 60,596

Millionaire Teacher: The Nine Rules of Wealth You Should Have Learned in School by Andrew Hallam

Albert Einstein, asset allocation, Bernie Madoff, buy and hold, diversified portfolio, financial independence, George Gilder, index fund, Long Term Capital Management, new economy, passive investing, Paul Samuelson, Ponzi scheme, pre–internet, price stability, random walk, risk tolerance, Silicon Valley, South China Sea, stocks for the long run, survivorship bias, transaction costs, Vanguard fund, yield curve

If actively managed mutual funds didn’t cost money to run, and if advisers worked for free, investors’ odds of finding funds that would beat the broad-based index would be close to 50–50. In a 15-year-long U.S. study published in the Journal of Portfolio Management, actively managed stock market mutual funds were compared with the Standard & Poor’s 500 stock market index. The study concluded that 96 percent of actively managed mutual funds underperformed the U.S. market index after fees, taxes, and survivorship bias.11 What’s a survivorship bias? When a mutual fund performs terribly, it doesn’t typically attract new investors and many of its current customers flee the fund for healthier pastures. Often, the poorly performing fund is merged with another fund or it is shut down. In November 2009, I underwent bone-cancer surgery—where large pieces of three of my ribs were removed, as well as chunks of my spinal process.

Finally, the dismal track record of the Lindner Large-Cap Fund was erased when it was merged into the Hennessy Total Return Fund.14 You can read countless books on index-performance track records versus actively managed funds. Most say index funds have the advantage over 80 percent of actively managed funds over a period of 10 years or more. But they don’t typically account for survivorship bias (or taxes, which I’ll discuss later in this chapter) when making the comparisons. Doing so gives index funds an even larger advantage. When accounting for fees, survivorship bias, and taxes, most actively managed mutual funds dramatically underperform index funds. In taxable accounts, the average U.S. actively managed fund underperformed the U.S. Standard & Poor’s 500 stock market index by 4.8 percent annually from 1984 to 1999.15 Holes in the hulls of actively managed mutual funds There are five factors dragging down the returns of actively managed U.S. mutual funds: expense ratios, 12B1 fees, trading costs, sales commissions, and taxes.

Seventeen drop out before they finish, but your three remaining runners take the top three spots and you report in the school newspaper that your average runner finished second. Bizarre? Of course, but in the fantasy world of hedge fund data crunchers, it’s still “accurate.” As a result of such twilight-zone reporting, Malkiel and Ibbotson found during their study that the average returns reported in databases, were overstated by 7.3 percent annually. These results include survivorship bias (not counting those funds that don’t finish the race) and something called “back-fill bias.” Imagine 1,000 little hedge funds that are just starting out. As soon as they “open shop” they start selling to accredited investors. But they aren’t big enough or successful enough to add their performance figures to the hedge fund data crunchers—yet. After 10 years, assume that 75 percent of them go out of business, which is in line with Malkiel and Ibbotson’s findings.


pages: 407 words: 114,478

The Four Pillars of Investing: Lessons for Building a Winning Portfolio by William J. Bernstein

asset allocation, Bretton Woods, British Empire, business cycle, butter production in bangladesh, buy and hold, buy low sell high, carried interest, corporate governance, cuban missile crisis, Daniel Kahneman / Amos Tversky, Dava Sobel, diversification, diversified portfolio, Edmond Halley, equity premium, estate planning, Eugene Fama: efficient market hypothesis, financial independence, financial innovation, fixed income, George Santayana, German hyperinflation, high net worth, hindsight bias, Hyman Minsky, index fund, invention of the telegraph, Isaac Newton, John Harrison: Longitude, Long Term Capital Management, loss aversion, market bubble, mental accounting, money market fund, mortgage debt, new economy, pattern recognition, Paul Samuelson, quantitative easing, railway mania, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, South Sea Bubble, stocks for the long run, stocks for the long term, survivorship bias, The inhabitant of London could order by telephone, sipping his morning tea in bed, the various products of the whole earth, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

Note that for some of the periods, the previous best-performing funds did slightly better than average, and for some, worse than average. But in each instance, the previous winners underperformed the S&P 500 index going forward, sometimes by a large margin. This is classic Randomovian behavior; we are once again looking at chimps, not skilled operators. Actually, because of “survivorship bias,” these studies understate the case against active management. We’ve already come across survivorship bias in Chapter 1 when we discussed the differences in stock and bond returns among nations. In this case, when you look at the prior performance of all the funds in your daily newspaper, or even a sophisticated mutual fund database like Morningstar’s Principia Pro, you are not looking at the complete sample of funds; you’re looking only at those that have survived.

Few investors have the patience to leave the fruits of their labor untouched. And even if they did, their spendthrift heirs would likely make fast work of their fortune. But even allowing for this, Figure 1-1 is still highly deceptive. For starters, it ignores commissions and taxes, which would have shrunk returns by another percent or two, reducing a potential $23 million fortune to the above $3 million or $400,000. Even more importantly, it ignores “survivorship bias.” This term refers to the fact that only the best outcomes make it into the history books; those financial markets that failed do not. It is no accident that investors focus on the immense wealth generated by the economy and markets of the United States these past two centuries; the champion—our stock market—is the most easily visible, while less successful assets fade quickly from view. And yet the global investor in 1790 would have been hard pressed to pick out the United States as a success story.

But this record reflects only those societies that survived and prospered, since successful societies are much more likely to leave a record. Babylonian, Greek, and Roman investors did much better than those in the nations they vanquished—the citizens of Judea or Carthage had far bigger worries than their failing financial portfolios. This is not a trivial issue. At a very early stage in history we are encountering “survivorship bias”—the fact that only the best results tend to show up in the history books. In the twentieth century, for example, investors in the U.S., Canada, Sweden, and Switzerland did handsomely because they went largely untouched by the military and political disasters that befell most of the rest of the planet. Investors in tumultuous Germany, Japan, Argentina, and India were not so lucky; they obtained far smaller rewards.


pages: 301 words: 85,126

AIQ: How People and Machines Are Smarter Together by Nick Polson, James Scott

Air France Flight 447, Albert Einstein, Amazon Web Services, Atul Gawande, autonomous vehicles, availability heuristic, basic income, Bayesian statistics, business cycle, Cepheid variable, Checklist Manifesto, cloud computing, combinatorial explosion, computer age, computer vision, Daniel Kahneman / Amos Tversky, Donald Trump, Douglas Hofstadter, Edward Charles Pickering, Elon Musk, epigenetics, Flash crash, Grace Hopper, Gödel, Escher, Bach, Harvard Computers: women astronomers, index fund, Isaac Newton, John von Neumann, late fees, low earth orbit, Lyft, Magellanic Cloud, mass incarceration, Moneyball by Michael Lewis explains big data, Moravec's paradox, more computing power than Apollo, natural language processing, Netflix Prize, North Sea oil, p-value, pattern recognition, Pierre-Simon Laplace, ransomware, recommendation engine, Ronald Reagan, self-driving car, sentiment analysis, side project, Silicon Valley, Skype, smart cities, speech recognition, statistical model, survivorship bias, the scientific method, Thomas Bayes, Uber for X, uber lyft, universal basic income, Watson beat the top human players on Jeopardy!, young professional

But a naïve interpretation of the data initially seems to suggest otherwise: if the returning planes have taken damage on the fuselage, then by God, let’s put the armor there instead. Only a genius like Wald, the story goes, can see to the heart of the matter, leading us back to our initial, intuitive conclusion. Alas, as far as we can tell from the historical record, this account has little basis in fact. Worse still, this embellished version, in which the moral of the story is about survivorship bias, misses the truly important thing about Abraham Wald’s contribution to the Allied war effort. Survivorship bias in the data was obviously the problem, and everybody knew it. Otherwise there would have been no reason to call the Statistical Research Group in the first place; the navy didn’t need a bunch of math professors just to count bullet holes. Their question was more specific: how to estimate the conditional probability of an aircraft surviving an enemy hit in a particular spot, despite the fact that much of the relevant data was missing.

If that were true, then the navy’s data analysts would see hundreds of bombers coming back with harmless bullet holes on the fuselage—but not a single one coming back with holes around the engine, since every such plane would have crashed. Under this scenario, if you simply added armor where you saw the bullet holes—on the fuselage—then you’d actually be handicapping the bombers, adding weight that “protected” them from a nonexistent danger. This example illustrates an extreme case of survivorship bias. Although the real world is much less extreme—bullets to the engine are not 100% lethal, nor are bullets to the fuselage 100% harmless—the statistical point remains: the pattern of damage on the returning planes had to be analyzed carefully. At this juncture, we must pause to make two important side points. First, the internet bloody loves this story. Second, just about everyone who’s ever told it—with the notable exception of an obscure, highly technical paper published in the Journal of the American Statistical Association in 1984—gets it wrong.6 Try Googling “Abraham Wald” and “World War II” yourself and see what you find: one blog post after another about how a mathematical crusader named Wald prevented those navy blockheads from making a terrible blunder and slapping a bunch of unnecessary armor on the fuselages of airplanes.

The navy guys reached the obvious conclusion: put more armor on the fuselage. Nonetheless, they gave their data to Abraham Wald, just to double-check. Wald’s little gray cells went to work. And then a thunderbolt. “Wait!” Wald exclaims. “That’s wrong. We don’t see any damage to the engines because the planes that are hit in the engine never return. You need to add armor to the engine, not the fuselage.” Wald had pointed out the crucial flaw in the navy’s thinking: survivorship bias. His final, life-saving advice ran exactly counter to that of bthe other so-called experts: put the armor where you don’t see the bullet holes. We can see why this version of the story is so irresistible: the path of counterintuition eventually turns a full 360 degrees. Imagine asking any person off the street, “Where should we put extra armor on airplanes to help them survive enemy fire?”


pages: 1,088 words: 228,743

Expected Returns: An Investor's Guide to Harvesting Market Rewards by Antti Ilmanen

Andrei Shleifer, asset allocation, asset-backed security, availability heuristic, backtesting, balance sheet recession, bank run, banking crisis, barriers to entry, Bernie Madoff, Black Swan, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, capital controls, Carmen Reinhart, central bank independence, collateralized debt obligation, commoditize, commodity trading advisor, corporate governance, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, debt deflation, deglobalization, delta neutral, demand response, discounted cash flows, disintermediation, diversification, diversified portfolio, dividend-yielding stocks, equity premium, Eugene Fama: efficient market hypothesis, fiat currency, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, framing effect, frictionless, frictionless market, G4S, George Akerlof, global reserve currency, Google Earth, high net worth, hindsight bias, Hyman Minsky, implied volatility, income inequality, incomplete markets, index fund, inflation targeting, information asymmetry, interest rate swap, invisible hand, Kenneth Rogoff, laissez-faire capitalism, law of one price, London Interbank Offered Rate, Long Term Capital Management, loss aversion, margin call, market bubble, market clearing, market friction, market fundamentalism, market microstructure, mental accounting, merger arbitrage, mittelstand, moral hazard, Myron Scholes, negative equity, New Journalism, oil shock, p-value, passive investing, Paul Samuelson, performance metric, Ponzi scheme, prediction markets, price anchoring, price stability, principal–agent problem, private sector deleveraging, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, reserve currency, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Robert Shiller, Robert Shiller, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stochastic volatility, stocks for the long run, survivorship bias, systematic trading, The Great Moderation, The Myth of the Rational Market, too big to fail, transaction costs, tulip mania, value at risk, volatility arbitrage, volatility smile, working-age population, Y2K, yield curve, zero-coupon bond, zero-sum game

The main quantifiable biases in published hedge fund returns are survivorship bias and backfill bias. Underlying most biases is the voluntary nature of reporting to such databases and the flexibility that database providers give to reporting funds. Individual funds are likely to be motivated by marketing considerations because there is plenty of evidence that fund inflows are strongly (and unduly) influenced by past performance. These biases lead to overstated published returns—and understated risks—for individual funds and for the industry as a whole. Although numerous academic papers discuss and quantify these biases, they perhaps are not fully appreciated by HF investors. The list of biases is long and partly overlapping:• Survivorship bias. Funds leave the database when they die. There is strong evidence that extinct funds in the “graveyard” module of HF databases earned lower average returns than live (surviving) funds

• Expected returns may vary over time in a cyclical fashion, which makes extrapolation of multi-year performance particularly dangerous. Periods of high realized returns and rising asset valuations—think stock markets in the 1990s—are often associated with falling forward-looking returns. • For specific funds and strategies, the historical performance data that investors get to see are often upward biased. This bias is due to the voluntary nature of performance reporting and survivorship bias (so that poor performers are left out of databases or are not marketed by the fund manager). A similar caveat applies to simulated “paper” portfolios because backtests may be overfitted and trading costs ignored or understated. These concerns notwithstanding, this book presents extensive evidence of long-run realized returns, when possible covering 50-to-100-year histories. Several main findings are familiar to most readers:• Stock markets have outperformed fixed income markets during the past century in all countries studied.

Long-dated Treasuries are arguably a more natural riskless asset for long-horizon investors, given the uncertain reinvestment rate for short-dated bills. Pension funds match their liabilities best by buying long-dated real or nominal bonds. 4.6 BIASED RETURNS For many asset classes, returns may be positively or negatively biased over a given historical sample. For active asset managers with voluntary reporting, published returns are almost certainly upward biased. Section 11.4 reviews a host of selection biases such as survivorship bias and backfill bias in the context of hedge fund return databases, but similar caveats apply to the reported performance of other managers. Backtested results of active strategies also suffer from overfitting and data-mining biases, which also overstate published returns. Whenever we observe exceptionally attractive historical returns, it is healthy to adopt a skeptical approach. The financial industry has limited incentives to emphasize this needed skepticism beyond printing required disclaimers, while our innate tendencies for extrapolation and optimism make most of us too easy prey for the upbeat marketing of past performance. 4.7 NOTES [1] The distinction between realized (ex post) and expected (ex ante) returns should be crystal clear.


pages: 295 words: 66,824

A Mathematician Plays the Stock Market by John Allen Paulos

Benoit Mandelbrot, Black-Scholes formula, Brownian motion, business climate, business cycle, butter production in bangladesh, butterfly effect, capital asset pricing model, correlation coefficient, correlation does not imply causation, Daniel Kahneman / Amos Tversky, diversified portfolio, dogs of the Dow, Donald Trump, double entry bookkeeping, Elliott wave, endowment effect, Erdős number, Eugene Fama: efficient market hypothesis, four colour theorem, George Gilder, global village, greed is good, index fund, intangible asset, invisible hand, Isaac Newton, John Nash: game theory, Long Term Capital Management, loss aversion, Louis Bachelier, mandelbrot fractal, margin call, mental accounting, Myron Scholes, Nash equilibrium, Network effects, passive investing, Paul Erdős, Paul Samuelson, Ponzi scheme, price anchoring, Ralph Nelson Elliott, random walk, Richard Thaler, Robert Shiller, Robert Shiller, short selling, six sigma, Stephen Hawking, stocks for the long run, survivorship bias, transaction costs, ultimatum game, Vanguard fund, Yogi Berra

Philosophers have not convincingly shown what exactly is wrong with the terms grue and bleen, but they demonstrate that even the abrupt failure of a regularity to hold can be accommodated by the introduction of new weasel words and ad hoc qualifications. In their headlong efforts to discover associations, data miners are sometimes fooled by “survivorship bias.” In market usage this is the tendency for mutual funds that go out of business to be dropped from the average of all mutual funds. The average return of the surviving funds is higher than it would be if all funds were included. Some badly performing funds become defunct, while others are merged with better-performing cousins. In either case, this practice skews past returns upward and induces greater investor optimism about future returns. (Survivorship bias also applies to stocks, which come and go over time, only the surviving ones making the statistics on performance. WCOM, for example, was unceremoniously replaced on the S&P 500 after its steep decline in early 2002.)

WCOM, for example, was unceremoniously replaced on the S&P 500 after its steep decline in early 2002.) The situation is rather like that of schools that allow students to drop courses they’re failing. The grade point averages of schools with such a policy are, on average, higher than those of schools that do not allow such withdrawals. But these inflated GPAs are no longer a reliable guide to students’ performance. Finally, taking the meaning of the term literally, survivorship bias makes us all a bit more optimistic about facing crises. We tend to see only those people who survived similar crises. Those who haven’t are gone and therefore much less visible. Rumors and Online Chatrooms Online chatrooms are natural laboratories for the observation of illusions and distortions, although their psychology is more often brutally basic than subtly specious. While spellbound by WorldCom, I would spend many demoralizing, annoying, and engaging hours compulsively scouring the various WorldCom discussions at Yahoo!

Brian auditors Aumann, Robert availability error average values compared with distribution of incomes risk as variance from averages average return compared with median return average value compared with distribution of incomes buy-sell rules and outguessing average guess risk as variance from average value averaging down Bachelier, Louis Bak, Per Barabasi, Albert-Lazló Bartiromo, Maria bear markets investor self-descriptions and shorting and distorting strategy in Benford, Frank Benford’s Law applying to corporate fraud background of frequent occurrence of numbers governed by Bernoulli, Daniel Beta (B) values causes of variations in comparing market against individual stocks or funds strengths and weaknesses of technique for finding volatility and Big Bang billiards, as example of nonlinear system binary system biorhythm theory Black, Fischer Black-Scholes option formula blackjack strategies Blackledge, Todd “blow up,” investor blue chip companies, P/E ratio of Bogle, John bonds Greenspan’s impact on bond market history of stocks outperforming will not necessarily continue to be outperformed by stocks Bonds, Barry bookkeeping. see accounting practices bottom-line investing Brock, William brokers. see stock brokers Buffett, Warren bull markets investor self-descriptions and pump and dump strategy in Butterfly Economics (Ormerod) “butterfly effect,” of nonlinear systems buy-sell rules buying on the margin. see also margin investments calendar effects call options. see also stock options covering how they work selling strategies valuation tools campaign contributions Capital Asset Pricing Model capital gains vs. dividends Central Limit Theorem CEOs arrogance of benefits in manipulating stock prices remuneration compared with that of average employee volatility due to malfeasance of chain letters Chaitin, Gregory chance. see also whim trading strategies and as undeniable factor in market chaos theory. see also nonlinear systems charity Clayman, Michelle cognitive illusions availability error confirmation bias heuristics rules of thumb for saving time mental accounts status quo bias Cohen, Abby Joseph coin flipping common knowledge accounting scandals and definition and importance to investors dynamic with private knowledge insider trading and parable illustrating private information becoming companies/corporations adjusting results to meet expectations applying Benford’s Law to corporate fraud comparing corporate and personal accounting financial health and P/E ratio of blue chips competition vs. cooperation, prisoner’s dilemma complexity changing over time horizon of sequences (mathematics) of trading strategies compound interest as basis of wealth doubling time and formulas for future value and present value and confirmation bias definition of investments reflecting stock-picking and connectedness. see also networks European market causing reaction on Wall Street interactions based on whim interactions between technical traders and value traders irrational interactions between traders Wolfram model of interactions between traders Consumer Confidence Index (CCI) contrarian investing dogs of the Dow measures of excellence and rate of return and cooperation vs. competition, prisoner’s dilemma correlation coefficient. see also statistical correlations counter-intuitive investment counterproductive behavior, psychology of covariance calculation of portfolio diversification based on portfolio volatility and stock selection and Cramer, James crowd following or not herd-like nature of price movements dart throwing, stock-picking contest in the Wall Street Journal data mining illustrated by online chatrooms moving averages and survivorship bias and trading strategies and DeBondt, Werner Deciding What’s News (Gans) decimalization reforms decision making minimizing regret selling WCOM depression of derivatives trading, Enron despair and guilt over market losses deviation from the mean. see also mean value covariance standard deviation (d) variance dice, probability and Digex discounting process, present value of future money distribution of incomes distribution of wealth dynamic of concentration UN report on diversified portfolios. see stock portfolios, diversifying dividends earnings and proposals benefitting returns from Dodd, David dogs of the Dow strategy “dominance” principle, game theory dot com IPOs, as a pyramid scheme double-bottom trend reversal “double-dip” recession double entry bookkeeping doubling time, compound interest and Dow dogs of the Dow strategy percentages of gains and losses e (exponential growth) compound interest and higher mathematics and earnings anchoring effect and complications with determination of inflating (WCOM) P/E ratio and stock valuation and East, Steven H.


The Intelligent Asset Allocator: How to Build Your Portfolio to Maximize Returns and Minimize Risk by William J. Bernstein

asset allocation, backtesting, buy and hold, capital asset pricing model, commoditize, computer age, correlation coefficient, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, fixed income, index arbitrage, index fund, intangible asset, Long Term Capital Management, p-value, passive investing, prediction markets, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, South Sea Bubble, stocks for the long run, survivorship bias, the rule of 72, the scientific method, time value of money, transaction costs, Vanguard fund, Yogi Berra, zero-coupon bond

S&P 500/EAFE mixes, 1969–1998. 50 The Intelligent Asset Allocator Bibliography, be aware that the returns presentation is very confusing. Returns are reported in inflation-adjusted terms, with dividends not included. Thus, the U.S. return is reported at about 4%. To this must be added an average 4% dividend (for a total real return of 8%) plus another 3% for inflation, for a total nominal return of 11%. The main point of the Jorion-Goetzmann work is that the careful investor must be aware of so-called survivorship bias. That is, it is easy to look just at U.S. returns and conclude that long-term real returns will continue to be high. However, the United States has been the winner in the global equity sweepstakes; the returns in most other markets have not been nearly as high. Of course, there is no guarantee that the United States will continue winning. Moreover, it is easy to look at the S&P and EAFE and be encouraged by their high returns.

A small-cap indexing strategy would of necessity sell the most rapidly appreciating stocks as they grew beyond the index’s size borders, when in fact these are the companies with the highest returns going forward. As we’ll discuss further along in this chapter, small-cap growth stocks have poor long-term returns, and it is probably wise to avoid investing in this area, active or indexed. Survivorship Bias The deeper one delves, the worse things look for actively managed funds. Consider for a moment what happens when you open up the quarterly New York Times supplement and start sampling fund performance over the past 10 years. You might think that you’re getting a fairly accurate picture of historical fund performance. And you’d be wrong. That is because what you’re looking at is not the performance of all of the funds in existence over the past decade, but only the ones that survived.

Since only returns below the mean are a source of risk, semivariance is felt to be a better measure of risk. Spread: The difference between the bid and ask price of a security. The amount of spread is a measure of the liquidity of the security. 194 The Intelligent Asset Allocator Standard deviation (SD): A statistical measure of the scatter of a series of numbers. The SD of the returns of a security or portfolio is usually a good estimate of its risk. Survivorship bias: An upward bias in the estimation of aggregate security or investment company returns caused by the disappearance of the worst performing members of the group. Systematic risk: The risk of the market portfolio, which cannot be diversified away. Total return: Same as the return of a security or portfolio— includes price change, dividends, and other distributions. Treasury inflation-protected security (TIPS): A Treasury bond or note whose coupon and principal payment are indexed to inflation.


pages: 369 words: 128,349

Beyond the Random Walk: A Guide to Stock Market Anomalies and Low Risk Investing by Vijay Singal

3Com Palm IPO, Andrei Shleifer, asset allocation, buy and hold, capital asset pricing model, correlation coefficient, cross-subsidies, Daniel Kahneman / Amos Tversky, diversified portfolio, endowment effect, fixed income, index arbitrage, index fund, information asymmetry, liberal capitalism, locking in a profit, Long Term Capital Management, loss aversion, margin call, market friction, market microstructure, mental accounting, merger arbitrage, Myron Scholes, new economy, prediction markets, price stability, profit motive, random walk, Richard Thaler, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, survivorship bias, transaction costs, Vanguard fund

The results must hold for subperiods as well as for the whole period unless there is a valid reason for a change in the observed relationship. SURVIVORSHIP BIAS Another source of unreliability of an anomaly is survivorship bias, which exists whenever results are based on existing entities. For example, a simple study of existing mutual funds will find that mutual funds, on average, outperform their benchmarks. The problem with such a sample is survivorship. Only well-performing funds continue to survive, while the underperformers die. Thus, a sample of existing mutual funds will not contain funds that underperformed and died. If all funds, dead and alive, are included in the sample, then the funds, on average, do not outperform their benchmarks. The sample of existing mutual funds has a survivorship bias and will result in an overestimation of fund performance. Survivorship is important in market timing studies, as market timing newsletters or services use many strategies and frequently add new strategies and discontinue others.

Which ones does the market timer add? The ones that have shown great promise based on past trends. Which ones are discontinued? The ones that no longer show continuing profitability. The record displayed by the market 11 12 Beyond the Random Walk timer shows only the successful strategies and not the unsuccessful strategies, giving readers the false impression of market timing prowess where none exists. Survivorship bias is widespread in many spheres of the investment world. People with a good investment record are retained, while others are dumped. It seems as if all the investment firms have analysts who can predict the market. What about the guests on CNBC? Are they really good stock pickers, or are they simply lucky? SMALL SAMPLE BIAS Mispricings may be caused by a small sample bias. Usually the small sample refers to the period of observation.

. • Efficient markets are desirable for the society because prices determine allocation of resources. • Markets cannot be fully efficient because of the cost of collecting and analyzing information, cost of trading, and limits on the capital available to arbitrageurs. • All anomalies must be viewed with caution and skepticism, as spurious mispricings can surface for a variety of reasons, such as errors in defining normal return, data mining, survivorship bias, small sample bias, selection bias, nonsynchronous trading, and misestimation of risk. • Though anomalies should disappear in an efficient market, they may persist because they are not well understood, arbitrage is too costly, the profit potential is insufficient, trading restrictions exist, and behavioral biases exist. • Documented and valid anomalies may still be unprofitable because the evidence is based on averages (and may include a large fraction of losers), conditions responsible for the anomaly may change, and trading by informed investors may cause the anomaly to disappear.


The Permanent Portfolio by Craig Rowland, J. M. Lawson

Andrei Shleifer, asset allocation, automated trading system, backtesting, bank run, banking crisis, Bernie Madoff, buy and hold, capital controls, correlation does not imply causation, Credit Default Swap, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, high net worth, High speed trading, index fund, inflation targeting, margin call, market bubble, money market fund, new economy, passive investing, Ponzi scheme, prediction markets, risk tolerance, stocks for the long run, survivorship bias, technology bubble, transaction costs, Vanguard fund

They use a methodology that accounts for “survivorship bias.” Survivorship bias is what happens when the final results of a data series don't show all of those who dropped out before the end. Think of survivorship bias as going into your school records, erasing the courses you took where you received a bad grade, and then recalculating your GPA. It's the same thing, except when you do it you don't make millions a year from unsuspecting investors. A fund that is performing poorly will often be closed or merged with another fund in order to sweep poor results under the rug. This can make a group of funds look better over the years as the stinkers are quietly buried. The poor results are removed from the industry totals, boosting the overall averages of those that remain. Think of survivorship bias as going into your school records, erasing the courses you took where you received a bad grade, and then recalculating your GPA.

See also Retirement plans Performance: 25/75 portfolio 50/50 portfolio 60/40 portfolio 75/25 portfolio actively managed portfolio backtesting past performance bond cash costs impacting (see Costs; Taxes) diversification impacting (see Diversification) financial safety by nonreliance on past performance flexibility of expectations about gold growth of inflation-adjusted or real limited losses in over time performance chasing Permanent Portfolio (see also specific asset performance) Permanent Portfolio fund rebalancing increasing long-term SPIVA report on stability impacting stock survivorship bias in reports on volatility impacting (see Market volatility) Permanent Exchange Traded Fund, Global X Permanent Portfolio: asset allocation in (see Asset allocation; Bonds; Cash; Gold; Stocks) commercial Permanent Portfolio funds description of diversification in (see Diversification) flexibility of, to expect unexpected Golden Rules of financial safety for implementation of international investments in (see International investments) modification of passive investing through performance of (see Performance) rebalancing and maintenance of (see Rebalancing and maintenance) resources on simplicity approach to tax considerations for (see Taxes) Variable Portfolio vs.

See also Financial safety cash providing rebalancing creating successful investing through using volatility to create Standard and Poor's (S&P): credit rating by precious metals and mining index S&P 500 Index SPIVA report S&P MidCap 400 Index S&P SmallCap 600 Index State Street: State Street Gold ETF State Street S&P 500 SPDR ETF State Street SPDR S&P/ASX 200 Index State Street SPDR Treasury Bill ETF State Street Treasury Bill ETF Stocks: 25/75 portfolio including 50/50 portfolio including 60/40 portfolio strategy including 75/25 portfolio including actively vs. passively managed stock funds asset class correlations with benefits of company, warnings about costs associated with diversification of economic conditions impacting expectations about gold mining company implementation of strategy including international stock funds large cap long-term view of stock index funds owning performance of Permanent Portfolio including reasons to use stock index funds recommended stock index funds retirement plans including risks related to selecting best type of index fund for shorting small cap stock market crashes stock picking for commercial Permanent Portfolio fund tax considerations with volatility of STOXX 600 Index StreetTracks Gold Exchange Traded Fund Suchecki, Bon Survivorship bias Swiss American Advisors, AG (Sallfort Advisors) Switzerland, economy and investments in: Canton Bank of Zurich gold storage services in intermediaries for investing in safe deposit boxes in Swiss Bank gold Swiss francs Swiss Gold ETF Swiss Physical Gold ETF types of Swiss banks Taxes: active vs. passive investment asset allocation and bond-related capital gains cash-related collectible gains commercial Permanent Portfolio fund tax considerations dividend geographic diversification tax considerations gold-related institutional diversification considerations with interest international rebalancing considerations regarding retirement account sales simplicity approach to stock-related tax-avoidance strategies, avoiding tax-free savings vehicles tax-loss harvesting types of wash sales and Terrorism Tight money recessions.


Stocks for the Long Run, 4th Edition: The Definitive Guide to Financial Market Returns & Long Term Investment Strategies by Jeremy J. Siegel

addicted to oil, asset allocation, backtesting, Black-Scholes formula, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, cognitive dissonance, compound rate of return, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, Eugene Fama: efficient market hypothesis, Everybody Ought to Be Rich, fixed income, German hyperinflation, implied volatility, index arbitrage, index fund, Isaac Newton, joint-stock company, Long Term Capital Management, loss aversion, market bubble, mental accounting, Myron Scholes, new economy, oil shock, passive investing, Paul Samuelson, popular capitalism, prediction markets, price anchoring, price stability, purchasing power parity, random walk, Richard Thaler, risk tolerance, risk/return, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, South Sea Bubble, stocks for the long run, survivorship bias, technology bubble, The Great Moderation, The Wisdom of Crowds, transaction costs, tulip mania, Vanguard fund

Several economists emphasized the existence of a survivorship bias in international returns, a bias caused by the fact that long-term returns are intensively studied in successful equity markets, such as the United States, but ignored in countries, such as Russia or Argentina, where stocks have faltered or disappeared outright.17 This bias suggested that stock returns in the United States, a country that over the last 200 years has been transformed from a small British colony into the world’s greatest economic power, are unique and historical equity returns in other countries would be lower. Three U.K. economists subsequently examined the historical stock and bond returns from 16 countries over the past century and put to bed concerns about survivorship bias. Elroy Dimson and Paul Marsh, professors at the London Business School, and Mike Staunton, director of the London Share Price Database, published their research in a book entitled Triumph of the Optimists: 101 Years of Global Investment Returns.

Unfortunately, the past record of the vast majority of such actively managed funds does not support this contention. There are two ways to CHAPTER 20 Fund Performance, Indexing, and Beating the Market 343 measure long-term fund returns. One is to compute the returns of all funds that have survived over the period examined. But the long-term returns on these funds suffer from survivorship bias that overestimates the returns available to investors. This survivorship bias exists because poorly performing funds are often terminated, leaving only the more successful ones with long-term track records to be included in the data. The second, and more accurate, method is to compute, year by year, the average performance of all equity mutual funds in existence. Both of these computations are shown in Table 20-1. From January 1971 through December 2006, the average equity mutual fund returned 10.49 percent annually, 1.06 percentage points behind the Wilshire 5000 and 1.04 percentage points behind the S&P 500 Index.

This sum can be realized by an investor holding the broadest possible portfolio of stocks in proportion to their market value and is calculated to include those companies that do not survive.8 By extension, the above analysis indicates that $1 million invested and reinvested during these more than 200 years would have grown to the incredible sum of $12.7 trillion by the end of 2006, nearly threequarters the entire capitalization of the U.S. stock market! One million dollars in 1802 is equivalent to roughly $16.84 million in today’s purchasing power. This was certainly a large, though not 8 Analysis of survivorship bias issues in computing returns is discussed in Chapter 20. CHAPTER 1 Stock and Bond Returns Since 1802 7 overwhelming, sum of money to the industrialists and landholders of the early nineteenth century.9 But total wealth in the stock market, or in the economy for that matter, does not accumulate as fast as the total return index. This is because investors consume most of their dividends and capital gains, enjoying the fruits of their past saving.


pages: 357 words: 91,331

I Will Teach You To Be Rich by Sethi, Ramit

Albert Einstein, asset allocation, buy and hold, buy low sell high, diversification, diversified portfolio, index fund, late fees, money market fund, mortgage debt, mortgage tax deduction, prediction markets, random walk, risk tolerance, Robert Shiller, Robert Shiller, shareholder value, Silicon Valley, survivorship bias, the rule of 72, Vanguard fund

Suppose after a few years only three funds produce total returns better than the broad-market averages. The complex begins to market those successful funds aggressively, dropping the other seven and burying their records. —BURTON G. MALKIEL, A RANDOM WALK DOWN WALL STREET * * * Second, when it comes to fund ratings, companies rely on something called survivorship bias to obscure the picture of how well a company is doing. Survivorship bias exists because funds that fail are not included in any future studies of fund performance for the simple reason that they don’t exist anymore. For example, a company may start a hundred funds, but have only fifty left a couple of years later. The company can trumpet how effective their fifty funds are, but ignore the fifty funds that failed and have been erased from history.

The company can trumpet how effective their fifty funds are, but ignore the fifty funds that failed and have been erased from history. In other words, when you see “Best 10 Funds!” pages on mutual-fund websites and in magazines, it’s just as important to think about what you aren’t seeing as what you are: The funds on that page are the ones that didn’t close down. Out of that pool of already successful funds, of course there will be some five-star funds. Financial companies know very well about survivorship bias, but they care more about having a page full of funds with great performance numbers than revealing the whole truth. As a result, they’ve consciously created several ways to test funds quickly and market only the best-performing ones, thus ensuring their reputation as the brand with the “best” funds. How to Engineer a Perfect Stock-Picking Record * * * Since we know it’s almost impossible to beat the market over the long term, let’s turn to probability and luck to explain why some funds seem irresistibly compelling.

I also keep a public list of all my personal-finance bookmarks at http://delicious.com/ramitsethi/finance. These tricks are especially insidious because you’d never know to look out for them. When you see a page full of funds with 15 percent returns, you naturally assume they’ll keep giving you 15 percent returns in the future. And it’s even better if they have five-star ratings from a trusted company like Morningstar. But now that we know about survivorship bias and the fact that most ratings are meaningless, it’s easy to see that financial “experts” and companies are just looking to fatten their wallets, not ensure that you get the best return for your money. I Bet You Don’t Need a Financial Adviser You’ve heard my rants against the media hype surrounding investment and the poor performance of most professional investors. Now there’s one more category of financial professionals that I want to warn you about: financial advisers.


pages: 517 words: 139,477

Stocks for the Long Run 5/E: the Definitive Guide to Financial Market Returns & Long-Term Investment Strategies by Jeremy Siegel

Asian financial crisis, asset allocation, backtesting, banking crisis, Black-Scholes formula, break the buck, Bretton Woods, business cycle, buy and hold, buy low sell high, California gold rush, capital asset pricing model, carried interest, central bank independence, cognitive dissonance, compound rate of return, computer age, computerized trading, corporate governance, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, Deng Xiaoping, discounted cash flows, diversification, diversified portfolio, dividend-yielding stocks, dogs of the Dow, equity premium, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Financial Instability Hypothesis, fixed income, Flash crash, forward guidance, fundamental attribution error, housing crisis, Hyman Minsky, implied volatility, income inequality, index arbitrage, index fund, indoor plumbing, inflation targeting, invention of the printing press, Isaac Newton, joint-stock company, London Interbank Offered Rate, Long Term Capital Management, loss aversion, market bubble, mental accounting, money market fund, mortgage debt, Myron Scholes, new economy, Northern Rock, oil shock, passive investing, Paul Samuelson, Peter Thiel, Ponzi scheme, prediction markets, price anchoring, price stability, purchasing power parity, quantitative easing, random walk, Richard Thaler, risk tolerance, risk/return, Robert Gordon, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, stocks for the long run, survivorship bias, technology bubble, The Great Moderation, the payments system, The Wisdom of Crowds, transaction costs, tulip mania, Tyler Cowen: Great Stagnation, Vanguard fund

If there are enough poorly informed traders who consistently underperform the market, then it might be possible for informed investors or professionals who study stocks to outperform the market. Unfortunately, the past record of the vast majority of such actively managed funds does not support this contention. There are two ways to measure long-term fund returns. One is to compute the returns of all funds that have survived over the period examined. But the long-term returns on these funds suffer from survivorship bias that overestimates the returns available to investors. This survivorship bias exists because poorly performing funds are often terminated, leaving only the more successful ones with superior track records to be included in the data. The second, and more accurate, method is to compute, year by year, the average performance of all equity mutual funds that were available to investors in that year. Both of these computations are shown in Table 23-1.

These returns represent a capitalization-weighted index of all New York Stock Exchange stocks and, starting in 1962, all American and Nasdaq stocks. The behavior of stock and bond returns since 1925 has also been researched by Roger Ibbotson, who has published yearbooks that have become benchmarks for U.S. asset returns since 1972.6 All the stock and bond returns reported in this volume, including those from the early nineteenth century, are free from “survivorship bias,” a bias that arises from only using the returns from firms that have survived and ignoring the lower returns from firms that have disappeared over time. TOTAL ASSET RETURNS The story of these assets is told in Figure 5-1. It depicts the total nominal (not inflation adjusted) return indexes for stocks, long- and short-term government bonds, gold, and commodities from 1802 through 2012.

If forward-looking equity returns match their historical average, the forward-looking equity premium in 2013 could be 6 percent or more.15 WORLDWIDE EQUITY AND BOND RETURNS When I published Stocks for the Long Run in 1994, some economists questioned whether my conclusions, drawn from data from the United States, might overstate historical equity returns measured on a worldwide basis. They claimed that U.S. stock returns exhibited survivorship bias, a bias caused by the fact that returns are collected from successful equity markets, such as the United States, but ignored in countries where stocks have faltered or disappeared outright, such as in Russia or Argentina.16 This bias suggested that stock returns in the United States, a country that over the last 200 years has been transformed from a small British colony into the world’s greatest economic power, are unique and that historical equity returns in other countries would be lower.


pages: 263 words: 75,455

Quantitative Value: A Practitioner's Guide to Automating Intelligent Investment and Eliminating Behavioral Errors by Wesley R. Gray, Tobias E. Carlisle

activist fund / activist shareholder / activist investor, Albert Einstein, Andrei Shleifer, asset allocation, Atul Gawande, backtesting, beat the dealer, Black Swan, business cycle, butter production in bangladesh, buy and hold, capital asset pricing model, Checklist Manifesto, cognitive bias, compound rate of return, corporate governance, correlation coefficient, credit crunch, Daniel Kahneman / Amos Tversky, discounted cash flows, Edward Thorp, Eugene Fama: efficient market hypothesis, forensic accounting, hindsight bias, intangible asset, Louis Bachelier, p-value, passive investing, performance metric, quantitative hedge fund, random walk, Richard Thaler, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, statistical model, survivorship bias, systematic trading, The Myth of the Rational Market, time value of money, transaction costs

In this section, we discuss the common errors made when conducting investment simulations and the means to avoid them. Data Errors In his 1992 article, “Behind the Smoke and Mirrors: Gauging the Integrity of Investment Simulations,”14 John D. Freeman discusses several common errors made by researchers conducting investment simulations. Many are inadvertent and the result of faulty data. A good database can help us avoid most of these errors. One well-known error is caused by survivorship bias, so called because it stems from the inclusion of “survivors”—those stocks that are not delisted—to the exclusion of those that are delisted, which causes the study to be biased in favor of the survivors. Databases that don't include data on delisted stocks may cause returns to be overstated. Let's say, for example, that we test a strategy that buys stocks in financial distress. If the database does not include delisted stocks, our results will only include stocks that were in financial distress and survived.

This crystallization prevents the rules from being modified to accommodate stocks that don't meet the model's investment criteria, which might be tempting at market extremes. Finally, we have sought to avoid the common pitfalls of back-test results. We model our testing on the best academic and industry finance research. In all cases, our null hypothesis is that the market is efficient, and only in the face of overwhelming evidence do we reject the null hypothesis. We use comprehensive databases free of survivorship bias, containing historical corporate action and delisting data, and we have lagged the data to control for look-ahead bias. We use a relatively large market capitalization cutoff ($1.4 billion as of December 31, 2011). We also assume an annually rebalanced, market capitalization–weighted portfolio at inception. We seek only genuine, repeatable results, and do so by replicating in our investment simulations as conservatively and authentically as possible the investment conditions confronted by investors in the real world.

See Look-ahead bias Price ratios analysis of compound annual growth rates alpha and adjusted performance risk-adjusted performance and absolute measures of risk value premium and spread book-to-market composite formed from all metrics formed from the “best” price ratios top-performing earnings yield EBIT variation, outperformance by enterprise yield (EBITDA and EBIT variations) forward earnings estimate free cash flow yield gross profits yield long-term study methods of studying Princeton-Newport Partners PROBM model Procter & Gamble Profit margins growth maximum stability Pronovost, Peter Puthenpurackal, John Quality and Price, improving compared with Magic Formula finding Price finding Quality Quantitative value checklist Quantitative value strategy examining, results of analysis legend beating the market black box, looking inside man versus machine risk and return robustness Greenblatt's Magic Formula bargain price examination of findings good business Quality and Price, improving compared with Magic Formula finding Price finding Quality simplifying strategy implementation checklist tried-and-true value investing principles Quinn, Kevin The Random Character of Stock Market Prices (Bachelier) Random walk theory Regression analysis Representativeness heuristic “Returns to Trading Strategies Based on Price-to-Earnings and Price-to-Sales Ratios” (Nathan, Sivakumar, & Vijayakumar) Ridgeline Partners Risk-adjusted performance and absolute measures of risk R-squared Ruane, William Scaled net operating assets (SNOA) Scaled total accruals (STA) Schedule 13D Security Analysis (Graham & Dodd) See's Candies Self-attribution bias Sequoia Fund Sharpe, William Sharpe ratio Shiller, Robert Short selling Shumway, Tyler Simons, Jim Singleton, Henry Sloan, Richard Small sample bias “Some Insiders Are Indeed Smart Investors” (Giamouridis, Liodakis, & Moniz) Sortino ratio Stock buybacks, issuance, and announcements Stock market, predicting movements in sustainable alpha quantitative value strategy simplifying tried-and-true value investing principles model, testing benchmarking data errors historical data versus forward data size of portfolio and target stocks small sample bias transaction costs universe, parameters of Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart (Ayres) “The Superinvestors of Graham-and-Doddsville” (Buffett) Survivorship bias Sustainable alpha Taleb, Nassim Teledyne Tetlock, Philip Theory of Investment Value (Williams) Third Avenue Value Fund Thorp, Ed Total enterprise value (TEV) Transaction costs Tsai, Claire Tversky, Amos Value investors'errors Value portfolio Value premium and spread Wellman, Jay What Works on Wall Street (O'Shaughnessy) Whitman, Martin J. Williams, John Burr WorldCom Z-score Zur, Emanuel


pages: 416 words: 118,592

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing by Burton G. Malkiel

accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, backtesting, beat the dealer, Bernie Madoff, BRICs, butter production in bangladesh, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial innovation, fixed income, framing effect, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Long Term Capital Management, loss aversion, margin call, market bubble, money market fund, mortgage tax deduction, new economy, Own Your Own Home, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, stocks for the long run, survivorship bias, The Myth of the Rational Market, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond

Beta, the risk measure typically used in the studies that have found “excess” returns from small firms, may be an incomplete measure of risk. We cannot distinguish whether the abnormal returns are truly the result of inefficiencies or whether they result from inadequacies in our measure of risk. The higher returns for smaller companies may simply be the requisite reward owed to investors for assuming greater risk. Moreover, the small-firm effect found in some studies may simply flow from what is called survivorship bias. Today’s list of companies includes only small firms that have survived—not the small firms that later went bankrupt. Finally, the dependability of the small-firm effect’s continuing is open to considerable question. While stocks of smaller firms did very well during the first six years of the 2000s, there was little to gain from holding such stocks during the 1990s. Buying a portfolio of small firms is hardly a surefire technique to enable an investor to earn abnormally high returns.

They claim that funds that have been superior (inferior) performers in one period predictably perform better (or worse) in a subsequent period, at least over the near term. Thus, investors could earn significantly better returns by purchasing recently good-performing funds, apparently contradicting the efficient-market hypothesis. Naturally, I have followed this work with great interest. And I am convinced that many studies have been flawed by the phenomenon of “survivorship bias,” that is, including in their studies only the successful funds that survived over a long period of time, while excluding from the analysis all the unsuccessful funds that fell by the wayside. Commonly used data sets of mutual-fund returns typically show the past records of all funds currently in existence. Clearly, today’s investors are not interested in the records of funds that no longer exist.

Thus, there will be a tendency for only the more successful funds to survive, and measures of the returns of such funds will tend to overstate the success of mutual-fund management. Moreover, it may appear that high returns will tend to persist. The problem for investors is that at the beginning of any period they can’t be sure which funds will be successful and survive. Another little-known factor in the behavior of mutual-fund management companies also leads to the conclusion that survivorship bias may be quite severe. A number of mutual-fund management complexes employ the practice of starting “incubator” funds. A complex may start ten new small equity funds with different in-house managers and wait to see which ones are successful. Suppose after a few years only three funds produce total returns better than the broad-market averages. The complex begins to market those successful funds aggressively, dropping the other seven and burying their records.


pages: 120 words: 39,637

The Little Book That Still Beats the Market by Joel Greenblatt

backtesting, index fund, intangible asset, random walk, survivorship bias, transaction costs

But these studies have often been criticized on numerous grounds. These include: 1. The study beat the market because the data used to select stocks weren’t really available to investors at the time the selections took place (a.k.a. look-ahead bias). 2. The study was biased because the database used in the study had been “cleaned up” and excluded companies that later went bankrupt, making the study results look better than they really were (a.k.a. survivorship bias). 3. The study included very small companies that couldn’t have been purchased at the prices listed in the database and uncovered companies too small for professionals to buy. 4. The study did not outperform the market by a significant amount after factoring in transaction costs. 5. The study picked stocks that were in some way “riskier” than the market, and that’s why performance was better. 6.

A newly released database from Standard & Poor’s Compustat, called “Point in Time,” was used. This database contains the exact information that was available to Compustat customers on each date tested during the study period. The database spans 17 years, the time period selected for the magic formula study. By using only this special database, it was possible to ensure that no look-ahead or survivorship bias took place. Further, the magic formula worked for both small-and large-capitalization stocks, provided returns far superior to the market averages, and achieved those returns while taking on much lower risk than the overall market (no matter how that risk was measured). Consequently, small size, high transaction costs, and added risk do not appear to be reasonable grounds for questioning the validity of the magic formula results.


pages: 482 words: 121,672

A Random Walk Down Wall Street: The Time-Tested Strategy for Successful Investing (Eleventh Edition) by Burton G. Malkiel

accounting loophole / creative accounting, Albert Einstein, asset allocation, asset-backed security, beat the dealer, Bernie Madoff, bitcoin, butter production in bangladesh, buttonwood tree, buy and hold, capital asset pricing model, compound rate of return, correlation coefficient, Credit Default Swap, Daniel Kahneman / Amos Tversky, Detroit bankruptcy, diversification, diversified portfolio, dogs of the Dow, Edward Thorp, Elliott wave, Eugene Fama: efficient market hypothesis, experimental subject, feminist movement, financial innovation, financial repression, fixed income, framing effect, George Santayana, hindsight bias, Home mortgage interest deduction, index fund, invisible hand, Isaac Newton, Long Term Capital Management, loss aversion, margin call, market bubble, money market fund, mortgage tax deduction, new economy, Own Your Own Home, passive investing, Paul Samuelson, pets.com, Ponzi scheme, price stability, profit maximization, publish or perish, purchasing power parity, RAND corporation, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, short selling, Silicon Valley, South Sea Bubble, stocks for the long run, survivorship bias, the rule of 72, The Wisdom of Crowds, transaction costs, Vanguard fund, zero-coupon bond, zero-sum game

We can measure the long-term records of only 84 of those original funds because 274 of them no longer exist. Thus, the data in the exhibit suffer from “survivorship bias.” You can be sure that the surviving funds are the ones with the best records. There is a nasty secret in the mutual-fund industry that if you have a poorly performing fund, it does not reflect well on the managers of the mutual-fund complex. So the poorly performing funds tend to get merged into funds with better records, thereby killing off their embarrassing records. The surviving funds, measured in the exhibit, are the better-performing ones. But even with this survivorship bias in the data, observe how few of the original funds actually had superior records. You can count on the fingers of one hand the number of funds from the original 358 that actually beat the market index by 2 percentage points or more.

Beta, the risk measure typically used in the studies that have found “excess” returns from small firms, may be an incomplete measure of risk. We cannot distinguish whether the abnormal returns are truly the result of inefficiencies, or whether they result from inadequacies in our measure of risk. The higher returns for smaller companies may simply be the requisite reward owed to investors for assuming greater risk. Moreover, the small-firm effect found in some studies may simply flow from what is called survivorship bias. Today’s list of companies includes only small firms that have survived—not the small firms that later went bankrupt. Finally, the dependability of the small-firm effect and its likelihood to continue is open to considerable question. Buying a portfolio of small firms is hardly a surefire technique to enable an investor to earn abnormally high returns. Portfolio Examples. Investable instruments are available that contain portfolios of stocks skewed toward small-sized companies, that is, small-cap stocks.

Even though emerging markets are not likely to be as efficient as developed markets, they are costly to access and to trade. Expense ratios of active funds are far higher than is the case in developed markets. Moreover, liquidity is lower and trading costs are higher in emerging markets. Therefore, after all expenses are accounted for, indexing turns out to be an excellent investment strategy. Ten years ended December 31, 2013 (net for fees, including survivorship bias). Source: Morningstar. A Specific Index-Fund Portfolio The table on page 389 presents specific index-fund selections that investors can use to build their portfolios. The table shows the recommended percentages for those in their mid-fifties—the group I call the “aging boomers.” Those who are not in their mid-fifties can use exactly the same selections and simply change the weights to those appropriate for their specific age group.


pages: 356 words: 51,419

The Little Book of Common Sense Investing: The Only Way to Guarantee Your Fair Share of Stock Market Returns by John C. Bogle

asset allocation, backtesting, buy and hold, creative destruction, diversification, diversified portfolio, financial intermediation, fixed income, index fund, invention of the wheel, Isaac Newton, new economy, passive investing, Paul Samuelson, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, stocks for the long run, survivorship bias, transaction costs, Upton Sinclair, Vanguard fund, William of Occam, yield management, zero-sum game

Total Costs Net Return* Cumulative Return Risk** Risk- Adjusted Return One (lowest cost) 10.3% 0.71% 0.21% 0.91% 9.4% 855% 16.2% 8.9% Two 10.6 0.99 0.31 1.30 9.3 818 17.0 8.4 Three 10.5 1.01 0.61 1.62 8.9 740 17.5 7.8 Four (highest cost) 10.6 1.44 0.90 2.34 8.3 632 17.4 7.4 500 Index Fund 9.2% 0.04% 0.04% 0.08% 9.1% 783% 15.3% 9.1% *This analysis includes only funds that survived the full 25-year period. Thus, these data significantly overstate the results achieved by equity funds due to survivorship bias. **Annual standard deviation of returns. Costs matter! Exhibit 5.1 shows a 1.4 percent difference between the average expense ratio of funds in the highest-cost quartile and the lowest-cost funds. This cost differential largely explains the advantage in returns among the lowest-cost funds over the highest-cost funds. During the past 25 years: average net annual return of lowest-cost funds, 9.4 percent; net annual return of highest-cost funds, just 8.3 percent, an enhancement in return achieved simply by minimizing costs.

If the managers take nothing, the investors receive everything: the market’s return. Caution: The index fund’s annual risk-adjusted return of 9.1 percent over the past 25 years is all the more impressive since the returns of the active equity funds are overstated (as always) by the fact that only the funds that were good enough to survive the decade are included in the data. Adjusted for this “survivorship bias,” the return of the average equity fund would fall from 9.0 percent to an estimated 7.5 percent. What’s more, selecting the index fund eliminated the need to search for those rare needles in the market haystack represented by the very few active funds that have performed better than that haystack, in the often-vain hope that their winning ways will continue over decades yet to come. As Morningstar suggests, if investors could rely on only a single factor to select future superior performers and to avoid future inferior performers, that factor would be fund costs.


Systematic Trading: A Unique New Method for Designing Trading and Investing Systems by Robert Carver

asset allocation, automated trading system, backtesting, barriers to entry, Black Swan, buy and hold, cognitive bias, commodity trading advisor, Credit Default Swap, diversification, diversified portfolio, easy for humans, difficult for computers, Edward Thorp, Elliott wave, fixed income, implied volatility, index fund, interest rate swap, Long Term Capital Management, margin call, merger arbitrage, Nick Leeson, paper trading, performance metric, risk tolerance, risk-adjusted returns, risk/return, Sharpe ratio, short selling, survivorship bias, systematic trading, technology bubble, transaction costs, Y Combinator, yield curve

There are various reasons why a rule that was successful in back-test might fail in reality. It never really worked First there are rules which apparently work in a back-test, but wouldn’t have actually made money in the past. The rules might be over-fitted, which I will discuss at length in the next chapter. Alternatively they could rely on forward looking data that was published with a delay not present in the back-test. There may be also survivorship bias in the 29 Systematic Trading instruments you are considering.You might have underestimated trading costs or missed out an important element of the market structure at the time, such as constraints on short selling of equities. Finally your history could be too brief and miss out on a crucial period when the strategy would have blown up.23 The world changes You want strategies that worked in the past and will continue to do so.

Staunch Systems Trader A trader who uses one or more systematic trading rules and incorporates them into a systematic framework. See page x and page 245. Subsystem position The position nominally held by a single instrument’s trading subsystem, trading only one instrument with the entire trading capital, given a forecast of returns. Equal to the volatility scalar multiplied by forecast, divided by 10. See page 159. Survivorship bias A problem where assets that disappeared are missing from historic data sets, such as shares in firms which went bankrupt. Because we don’t see the losses from these instruments we are likely to overestimate how profitable investing really was in the past. Technical A kind of trading rule that uses only price data to predict prices, such as trend following; no fundamental data is used. See page 43.

Thank you for that, and for everything. 301 Index 2001: A Space Odyssey, 19f 2008 crash, 170 Active management, 3 AIG, 2 Algorithms, 175, 199 Alpha, 3, 37, 106, 136 Alternative beta, 3-4 Amateur investors, 4, 6, 16, 48, 177, 210 and lack of diversification, 20 and over-betting, 21 and leverage, 35 and minimum sizes, 102 as day traders, 188 Anchored fitting: see Back-testing, expanding out of sample Annual returns, 178-179 Annualised cash volatility target, 137, 139, 149, 151, 159, 161, 171, 230, 250 Asset allocating investors, 3, 7, 42, 69, 98, 116, 147, 188, 225-244, 259 and Sharpe ratios, 46 and modular frameworks, 96 and the ‘no-rule’ rule, 116, 167, 196, 225, 228 and forecasts, 122-123, 159 and instrument weights, 166, 175, 189, 198-199 and correlation, 170 and instrument diversification multiplier, 175 and rules of thumb, 186 and trading speeds, 190-191, 205 and diversification, 206 Asset classes, 246&f Automation, 18-19 Back-testing, 5, 13-15, 16, 18, 19&f, 28, 53, 64, 67, 87, 113, 122, 146, 170, 182f, 187, 197, 205 and overfitting, 20, 29, 53f, 54, 68, 129f, 136, 145, 187 and skew, 40 and short holding periods, 43 in sample, 54-56 out of sample, 54-56 expanding out of sample, 56-57, 66, 71f, 84, 89f, 167f, 193-194 rolling window, 57-58, 66, 129f and portfolio weights, 69-73 and handcrafting, 85 and correlations, 129, 167&f, 175 and cost of execution, 179 simple and sophisticated, 186 need for mistrust of, 259 See also: Bootstrapping Barclays Bank, 1-2, 11, 31, 114 Barings, 41 Barriers to entry, 36, 43 Behavioural finance, 12 Beta, 3 Bid-Offer spread, 179 Block value, 153-154, 161, 182-183, 214, 219 Bollinger bands, 109 303 Systematic Trading Bond ETFs, 226 Bootstrapping, 70, 75-77, 80, 85-86, 146, 167, 175, 193-194&f, 199, 230, 248, 250 and forecast weights, 127, 205 see also Appendix C BP, 12, 13 Braga, Leda, 26 Breakouts, 109 Buffett, Warren, 37, 42 Calibration, 52-53 Carry, 67, 119, 123, 126, 127-128, 132, 247 and Skew, 119 Koijen et al paper on, 119f Central banks, 36, 103 Checking account value, recommended frequency, 149 Clarke, Arthur C, 19f ‘Close to Open’, 120-121 Cognitive bias, 12, 16, 17, 19-20, 28, 64, 179 and skew, 35 Collective funds, 4, 106, 116, 225 and derivatives, 107 and costs, 181 Commitment mechanisms, 17, 18 Compounding of returns, 143&f Contango: see Carry Contracts for Difference, 106, 181 Contrarians, 45 Corn trading, 247f Correlation, 42, 59f, 63, 68, 70, 73, 104, 107, 122, 129, 131, 167-168, 171 and Sharpe ratios, 64 and trading subsystems, 170 and ETFs, 231 Cost of execution, 179-181, 183, 188, 199, 203 Cost of trading, 42, 68, 104, 107, 174, 178, 181, 230 Credit Default Swap derivatives, 105 Crowded trades, 45 Crude oil futures, 246f Curve fitting: see over-fitting Daily cash volatility target, 137, 151, 158, 159, 161, 162, 163, 172, 175, 217, 218, 233, 254, 262. 270, 271, 217 Data availability, 102, 107 Data mining, 19f, 26-28 Data sources, 43-44 Day trading, 188 Dead cat bounces, 114 Death spiral, 35 DeMiguel, Victor, 743f Derivatives, 35 versus cash assets, 106 Desired trade, 175 Diary of trading, for semi-automatic trader, 219-224 Diary of trading, for asset allocating investor, 234- 244 Diary of trading, for staunch systems trader, 255- 257 Diversification, 20, 42, 44, 73f, 104, 107, 165, 170, 206 and Sharpe ratios, 65f, 147, 165 of instruments rather than rules, 68 and forecasts, 113 Dow Jones stock index, 23 Education of a Speculator, 17 Einstein, 70 Elliot waves, 109 Emotions, 2-3 Equal portfolio weights, 72-73 Equity value strategies, 4, 29, 31 Equity volatility indices, 34, 246, 247 Eurex, 180 Euro Stoxx 50 Index Futures, 179-180, 181, 182, 187-188, 193, 198 Eurodollar, trading recommendation, 247 Exchange rate, 161, 185 Exchange traded funds (ETFs), 4, 106, 183-184, 189, 197, 200, 214, 225, 226-228 holding costs of, 230 daily regearing of, 230f correlations, 231 Exchanges, trading on, 105, 107 Exponentially Weighted Moving Average Crossover 304 Index (EWMAC), 117-123, 126, 127-128, 132, 247 see also Appendix B Human qualities of successful traders, 259-260 Hunt brothers, 17 Fannie Mae and Freddie Mac, 2 Fees, 3 Fibonacci, 37, 109 Forecasts, 110-115, 121-123, 159, 175, 196, 211 scaling of, 112-113, 115, 133 combined, 125-133, 196, 248, 251 weighted average of, 126 and risk, 137 and speed of trading, 178 and turnover, 185 not changing once bet open, 211 see also Appendix D Forecast diversification multiplier, 128-133, 193f, 196, 249, 251 see also Appendix D Foreign exchange carry trading, 36 Fortune’s Formula, 143f FTSE 100 futures, 183, 210 Futures contracts, 181 and block value, 154-155 ‘Ideas First’, 26-27, 52-54, 103, 146 Ilmanen, Antti, 30f Illiquid assets, 198 Index trackers, 106 Inflation, 67 Instrument blocks, 154-155, 175, 182-183, 185, 206 Instrument currency volatility, 182-183, 203, 214 and turnover, 185, 195, 198 Instrument diversification multiplier, 166, 169-170, 171, 173, 175, 201, 206, 215, 229, 232, 253 Instrument forecast, 161, 162 Instrument riskiness, 155, 182 Instrument subsystem position, 175, 233 Instrument weights, 166-167, 169, 173, 175, 189, 198, 201, 202, 203, 206, 215, 229, 253 and Sharpe ratios, 168 and asset allocating investors, 226 and crash of 2008, 244 Gambling, 15, 20 Gaussian normal distribution, 22, 32&f, 39, 111f, 113, 114, 139f German bond futures, 112, 155, 181, 198 Gold, 246f Google, 29 Gross Domestic Product, 1 ‘Handcrafting’, 78-85, 116, 167-168, 175, 194, 199, 230, 248, 259 and over-fitting, 84 and Sharpe ratios, 85-90 and forecast weights, 127, 205 worked example for portfolio weights, 231-232 and allocation for staunch systems traders, 253 Hedge funds, 3, 177 High frequency trading, 6, 16, 30, 36, 180 Holding costs, 181 Housekeeping, daily, 217 for staunch systems traders, 254 Japan, 36 Japanese government bonds, 102, 112, 114, 200 JP Morgan, 156f Kahn, Richard, 42 Kaufman, Perry, 117 Kelly, John, and Kelly Criterion, 143-146, 149, 151 ‘Half-Kelly’ 146-147, 148, 230, 260 Koijen, Ralph, 119 Law of active management, 41-42, 43, 44, 46, 129f and Sharpe ratios, 47 Leeson, Nick, 41 Lehman Brothers, 2, 237 Leverage, 4, 21&f, 35, 95f, 138f, 142-143 and skew, 44-45 and low-risk assets, 103 and derivatives, 106 and volatility targeting, 151 realised leverage, 229 Life expectancy of investor, and risk, 141 305 Systematic Trading Limit orders, 179 Liquidity, 35, 104-105, 107 Lo, Andrew, 60f, 63f Long Term Capital Management (LTCM), 41, 46 Sharpe ratio of, 47 Low volatility instruments, need to avoid, 143, 151, 210, 230, 260 Lowenstein, Roger, 41, 46f Luck, need for, 260 Lynch, Peter, 37 Markowitz, Harry, 70, 72 Maximum number of bets, 215 Mean reversion trading, 31, 43, 45, 52, 213f ‘Meddling’, 17, 18, 19, 21, 136, 260 and forecasts, 115 and volatility targets, 148 Merger arbitrage, 29 Mid-price, 179, 181 Minimum sizes, 102, 107 Modular frameworks, 93, 95-99 Modularity, 5 Momentum, 42, 67, 68, 117 Moving averages, 94, 195, 197 MSCI, 156f Narrative fallacy, 20, 27, 28, 64 NASDAQ futures, 188 Nervousness, need for, 260 New position opening, 218 Niederhoffer, Victor, 17 Odean, Terence, 13, 20f Odysseus, 17 Oil prices, standard deviation of, 211 O’Shea, Colm, 94f Online portfolio calculators, 129f Overbetting, 21 Over the counter (OTC) trading, 105, 106, 107, 183f Overconfidence, 6, 17, 19f, 54, 58, 136 and lack of diversification, 20 and overtrading, 179 306 Over-fitting, 19-20, 27-28, 48, 51-54, 58, 65, 68, 121f, 156, 259 and Sharpe ratios, 46f, 47, 146 avoiding fitting, 67-68 of portfolio weights, 68-69 possibility of in ‘handcrafting’, 84 Overtrading, 179 Panama method, 247&f Passive indexing, 3 Passive management, 3, 4 Paulson, John, 31, 41 Pension funds, 3 ‘Peso problem’, 30&f Position inertia, 173-174, 193f, 196, 198, 217 Position sizing, 94, 153-163, 214 Poundstone, William, 143f Price movements, reasons for, 103, 107 Portfolio instrument position, 173, 175, 218, 254, 256, 257 Portfolio optimization, 70-90, 167 Portfolio size, 44, 178 Portfolio weighted position, 97, 99, 101, 109, 125, 135, 153, 165, 167, 177, 267 and diversification, 170 Price-to-earnings (P/E) ratios 4 Prospect theory, 12-13, 37 and momentum, 117 Quant Quake, the, 46 Raspberry Pi micro computers, 4 Relative value, 30, 43, 44-46, 213f Retail stockbrokers, 4 Risk, 39, 137-148, 170 Risk targeting, 136 Natural risk and leverage, 142 Risk parity investing, 38, 116&f Risk premia, 31, 119 RiskMetrics (TM), 156&f Roll down: see Carry Rolling up profits and losses, 149 Rogue Trader, 41 Rounded target position, 173, 175, 218 Index Rules of thumb, 186, 230 see also Appendix C Rumsfeld, Donald, 39&f Safe haven assets, 34 Schwager, Jack, 94f Self-fulfilling prophecies, 37 Semi-automatic trading, 4, 7, 11f, 18, 19f, 37, 38, 98, 163, 169, 209-224, 259 and portfolio size, 44, 203 and Sharpe ratios, 47, 147-148 and modular frameworks, 95 and trading rules, 109 and forecasts, 114, 122-123, 159 and eyeballing charts, 155, 195, 197, 214 and diversification, 166, 206 and instrument weights, 166, 175, 189 and correlation, 169 and trading subsystems, 169 and instrument diversification multiplier, 171, 175 and rules of thumb, 186 and overconfidence, 188 and stop losses, 189, 192 and trading speeds, 190-192, 205 Sharpe ratios, 25, 31-32, 34, 35, 42, 43, 44, 46-48, 53, 58, 60f, 67, 72, 73, 112, 184, 189, 210, 214, 250, 259 and overconfidence, 54, 136, 151 and rule testing, 59-60, 65 and T-Test, 61-63 and skew, 62f, 66 and correlation, 64 and diversification, 65f difficulty in distinguishing, 74 and handcrafting, 85-90 and factors of pessimism, 90 and risk, 137f, 138 and volatility targets, 144-145, 151 and speed of trading, 178-179, 196, 204 need for conservative estimation of, 195 and asset allocating investors, 225 and crash of 2008, 240 Schatz futures: see German bond futures Shefrin, Hersh, 13&f Short option strategies, 41 Short selling, 30, 37 Single period optimisation, 71, 85, 89 Skew, positive and negative, 32-34, 40-41, 48, 105, 107, 136, 139-141, 247, 259 and liquidity, 36 and prospect theory, 37 and risk, 39, 138 and leverage, 44-45, 142 and Sharpe ratios, 47, 62f, 146 and trend following, 115, 117 and EWMAC, 119 and carry, 119 and V2TX, 250 ‘Social trading’, 4f Soros, George, and sterling, 36f Speed of trading, 41-43, 47, 48, 104, 122, 174f, 177-205, 248 speed limits, 187-189, 196, 198-199, 204, 213, 228, 251, 260 Spread betting, 6, 106, 181, 197, 214 and block value, 154-155 and UK tax, 183f oil example, 214 Spreadsheets, 218 Stamp duty, 181 Standardised cost estimates, 203-205, 210, 226, 230 Standard deviations, 21-22, 31-32, 38, 40, 70, 103, 107, 111f, 129 and skew, 105 and forecasts, 112, 114, 128 recent, 155-158 returns, 167 and standardised cost, 182, 188, 192 and stop losses, 211 Static and dynamic trading, 38, 43, 168, 188 Staunch systems trading, 4, 7, 51-68, 69, 98, 109, 117-123, 167, 245-257 and Sharpe ratios, 46, 146, 189 and forecasts, 110-114, 122-123, 189 and instrument forecast, 161 and instrument weights, 166, 175, 198-199 and correlation, 170 and rules of thumb, 186 and trading speeds, 191-192, 205 307 Systematic Trading and back-testing, 193 and diversification, 206 Stop losses, 94-5, 115, 121f, 137f, 189, 192, 214, 216f, 217, 218 and forecasts, 211-212 and different instruments, 213 and price volatility, 216 Survivorship bias, 29 Swiss franc, 36, 103, 105, 142-143 System parameters, 186 Systematica hedge fund, 26 Taking profits and losses, 13-15, 16-18, 58, 94-95, 149 and trend following, 37 see also Appendix B Taleb, Nassim, 39f, 41 Tax (UK), 106, 183f Technical analysis, 18, 29 Technology bubble of 1999, 35 Templeton, John, 37 The Black Swan, 39f The Greatest Trade Ever, 31f, 41 Thorpe, Ed, 146f Thriftiness, need for, 260 Timing, 2 Too much/little capital, 206, 246f Trading capital, 150-151, 158, 165, 167, 178, 192, 199-202 starting low, 148 reducing, 149 and turnover, 185 daily calculation of, 217 Trading rules, 3-4, 7, 16, 25-26, 78, 95, 97-98, 101, 109, 121, 125, 135, 159, 161, 187, 249, 259 need for small number of, 67-68, 193 Kaufman, Perry’s guide to, 117 and speed of trading, 178, 205 cost calculations for, 204 see also Appendix B Trading subsystems, 98-99, 116, 159, 162, 163, 165, 166, 167&f, 169, 171f, 172, 175-176, 185, 187, 230, 251-252, 260 and correlation, 170 308 and turnover, 196 cost calculations for, 204 Traditional portfolio allocation, 167 Trend following, 28, 30, 37, 45, 47f, 67, 117, 137f, 194f, 212f, 247 and skew, 105, 115, 117, 213 Turnover, 184-186, 195, 197, 198, 205, 228, 260 methods of calculation, 204 back-testing of, 247-248 Twitter, 29 V2TX index, 246, 247, 249 Value at risk, 137 VIX futures, 105 Volatility, 21, 103, 107, 116, 129, 150, 226, 229 and targets, 95, 98, 106, 158, 159, 185 unpredictability of, 45 price volatility, 155-158, 162-163, 189, 196, 197, 200, 205, 214, 228, Appendix D and crash of 2008, 240-244 instrument currency volatility, 158, 161 instrument value volatility, 161, 172, 250 scalars, 159-160, 162, 185, 201, 206, 215, 217, 218, 229 look-back period, 155, 195-197 and speed of trading, 178 Volatility standardisation, 40, 71, 72, 73, 167, 182, 185 and forecasts, 112, 121, 129 and block value, 155 Volatility standardized costs, 247 Volatility targeting, 135-151, 171f, 188, 192, 201f, 213-215, 230, 233, 250, 259 Walk forward fitting: see Back testing, rolling window Weekly rebalancing process, for asset allocating investors, 233 When Genius Failed, 40, 46f Women as makers of investment decisions, 17&f www.systematictrading.org, 234 Zuckerman, Gregory, 31f THANKS FOR READING!


pages: 387 words: 119,409

Work Rules!: Insights From Inside Google That Will Transform How You Live and Lead by Laszlo Bock

Airbnb, Albert Einstein, AltaVista, Atul Gawande, Black Swan, book scanning, Burning Man, call centre, Cass Sunstein, Checklist Manifesto, choice architecture, citizen journalism, clean water, correlation coefficient, crowdsourcing, Daniel Kahneman / Amos Tversky, deliberate practice, en.wikipedia.org, experimental subject, Frederick Winslow Taylor, future of work, Google Earth, Google Glasses, Google Hangouts, Google X / Alphabet X, Googley, helicopter parent, immigration reform, Internet Archive, longitudinal study, Menlo Park, mental accounting, meta analysis, meta-analysis, Moneyball by Michael Lewis explains big data, nudge unit, PageRank, Paul Buchheit, Ralph Waldo Emerson, Rana Plaza, random walk, Richard Thaler, Rubik’s Cube, self-driving car, shareholder value, side project, Silicon Valley, six sigma, statistical model, Steve Ballmer, Steve Jobs, Steven Levy, Steven Pinker, survivorship bias, TaskRabbit, The Wisdom of Crowds, Tony Hsieh, Turing machine, winner-take-all economy, Y2K

xxxviii Walking gets you wetter. xxxix This is also an example of survivorship bias, where you skew your analysis by considering only the survivors rather than the entire population. Analysts looking at the performance of start-up companies and hedge funds often make this error because they include only companies that are still around, and ignore those that fail or shut down along the way. This makes start-up and hedge-fund performance look rosier than it is. And, of course, relying too much on this book may also be an example of survivorship bias. There are certainly lessons to be learned from the illustrations in this book, but it’s important to consider the lessons of failed companies as well. In People Operations we try to avoid survivorship bias in our analyses where possible. For example, we’ve tested some of our hiring practices by—in a double-blind fashion—hiring rejected candidates to see how they perform.


pages: 1,164 words: 309,327

Trading and Exchanges: Market Microstructure for Practitioners by Larry Harris

active measures, Andrei Shleifer, asset allocation, automated trading system, barriers to entry, Bernie Madoff, business cycle, buttonwood tree, buy and hold, compound rate of return, computerized trading, corporate governance, correlation coefficient, data acquisition, diversified portfolio, fault tolerance, financial innovation, financial intermediation, fixed income, floating exchange rates, High speed trading, index arbitrage, index fund, information asymmetry, information retrieval, interest rate swap, invention of the telegraph, job automation, law of one price, London Interbank Offered Rate, Long Term Capital Management, margin call, market bubble, market clearing, market design, market fragmentation, market friction, market microstructure, money market fund, Myron Scholes, Nick Leeson, open economy, passive investing, pattern recognition, Ponzi scheme, post-materialism, price discovery process, price discrimination, principal–agent problem, profit motive, race to the bottom, random walk, rent-seeking, risk tolerance, risk-adjusted returns, selection bias, shareholder value, short selling, Small Order Execution System, speech recognition, statistical arbitrage, statistical model, survivorship bias, the market place, transaction costs, two-sided market, winner-take-all economy, yield curve, zero-coupon bond, zero-sum game

Determinants of portfolio performance II: An update. Financial Analysts Journal 47(3), 40–48. Brown, Stephen J., William Goetzmann, Roger G. Ibbotson, and Stephen A. Ross. 1992. Survivorship bias in performance studies. Review of Financial Studies 5(4), 553–580. Chen, Zhiwu, and Peter J. Knez. 1996. Portfolio performance measurement: Theory and applications. Review of Financial Studies 9(2), 511–555. Ellis, Charles D. 1975. The loser's game. Financial Analysts Journal 31(4), 19–20, 22, 24, 26; reprinted in Financial Analysts Journal 51(1), 95–100. Elton, Edwin J., Martin J. Gruber, and Christopher R. Blake. 1996. Survivorship bias and mutual fund performance. Review of Financial Studies 9(4), 1097–1120. Elton, Edwin J., Martin J. Gruber, and Joel C. Rentzler. 1987. Professionally managed, publicly traded commodity funds.

New public offerings, information, and investor rationality: The case of publicly offered commodity funds. Journal of Business 62(1), 1–16. French, Dan W., and Glenn V. Henderson, Jr. 1985. How well does performance evaluation perform? Journal of Portfolio Management 11(2), 15–18. Fung, William, and David A. Hsieh. 1997. Survivorship bias investment style in the returns of CTAs. Journal of Portfolio Management 24(1), 30–41. Garcia, C. B., and F. J. Gould. 1993. Survivorship bias. Journal of Portfolio Management 19(3), 52–56. Grinblatt, Mark, and Sheridan Titman. 1989. Portfolio performance evaluation: Old issues and new insights. Review of Financial Studies 2(3), 393–422. Hau, Harald. 2001. Location matters: An examination of trading profits. Jouranl of Finance 56(5), 1959–1983. Murphy, J. Michael. 1980.

Mutual fund companies may kill their losers because they become expensive to operate when they get small. They may also kill them because they do not want to report their performance. In either event, by killing poorly performing funds, they raise the computed average performance of the surviving funds. The average performance of all funds may be negative, but you could not know this without knowing about the other funds. This type of sample selection bias is called the survivorship bias. Some large mutual fund companies start many new mutual funds every year. They keep the ones that perform well and kill the ones that fail. In this way, they are able to create the winners that they need to market their funds. If you are unaware of this process, you may give too much significance to past returns. You may not realize that the fund which generated superior past performance came to your attention only because it was among the best-performing funds of a large group of funds. 22.6.2 Avoiding the Sample Selection Bias Sample selection biases may be responsible for more trading losses than any other cause.


pages: 348 words: 83,490

More Than You Know: Finding Financial Wisdom in Unconventional Places (Updated and Expanded) by Michael J. Mauboussin

Albert Einstein, Andrei Shleifer, Atul Gawande, availability heuristic, beat the dealer, Benoit Mandelbrot, Black Swan, Brownian motion, butter production in bangladesh, buy and hold, capital asset pricing model, Clayton Christensen, clockwork universe, complexity theory, corporate governance, creative destruction, Daniel Kahneman / Amos Tversky, deliberate practice, demographic transition, discounted cash flows, disruptive innovation, diversification, diversified portfolio, dogs of the Dow, Drosophila, Edward Thorp, en.wikipedia.org, equity premium, Eugene Fama: efficient market hypothesis, fixed income, framing effect, functional fixedness, hindsight bias, hiring and firing, Howard Rheingold, index fund, information asymmetry, intangible asset, invisible hand, Isaac Newton, Jeff Bezos, Kenneth Arrow, Laplace demon, Long Term Capital Management, loss aversion, mandelbrot fractal, margin call, market bubble, Menlo Park, mental accounting, Milgram experiment, Murray Gell-Mann, Nash equilibrium, new economy, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative finance, random walk, Richard Florida, Richard Thaler, Robert Shiller, Robert Shiller, shareholder value, statistical model, Steven Pinker, stocks for the long run, survivorship bias, The Wisdom of Crowds, transaction costs, traveling salesman, value at risk, wealth creators, women in the workforce, zero-sum game

While ten years is insufficient to complete the reversion-to-the-mean process, much of the progression is evident within that time frame. Consistent with theory, attrition plays a central role in the improvement of lowest-quartile returns. Just 60 percent of the lowest-quartile companies were active after five years, as many of the poor performers went bankrupt or were acquired. This attrition creates a survivorship bias, allowing returns to rise during the decade. In contrast, 85 percent of the firms in the highest-return quartile were active after five years. Attrition rates across all quartiles tend to average out after five years. One consistent feature across the many mean-reversion studies is that some companies (albeit not many) can and do earn persistently high returns. In the study of nearly 700 retailers from 1950 to 2001, 14 percent of the companies never earned below their cost of capital.8 Of the 1,700 technology companies in the sample from 1960 to 1996, 11 percent sustained an unblemished record of positive excess returns.

Shefrin, Hersh Shleifer, Andrei short-term focus Siegel, Jeremy Simpson, Lou simulation situations, evaluation of six degrees of separation skill skin-conductance-response machine Sklansky, David slime mold Slovic, Paul snowball effect social sciences Social Security social systems, power laws and social validation software Sornette, Didier Soros, George space shuttle catastrophe species distribution speculation Stalin, Josef stall point Steinhardt, Michael stock market: as complex adaptive system; crash of 1987, investor diversity and outperforming stocks parallels with insect colonies Stocks for the Long Run (Siegel) strategic options strategy. See competitive strategy streaks of success luck and skill and stress long-term focus required loss of control and predictability physical responses to Strogatz, Steven Sull, Don Sundahl, David Sunder, Shyam Surowiecki, James survivorship bias sustained recovery system 1 and 2 thinking systems Taleb, Nassim Nicholas. target prices tax rates Technology Review technology stocks television industry Thaler, Richard H. theory: attribute-based approach; building, steps of; falsifiability Thorp, Ed time horizons timing rules tipping point total return to shareholders (TRS) tracking error transaction costs traveling-salesman problem Treynor, Jack Tupperware parties Tversky, Amos Twain, Mark two-by-two matrix Ulysses uncertainty classifications expectations and U.S.


Investment: A History by Norton Reamer, Jesse Downing

activist fund / activist shareholder / activist investor, Albert Einstein, algorithmic trading, asset allocation, backtesting, banking crisis, Berlin Wall, Bernie Madoff, break the buck, Brownian motion, business cycle, buttonwood tree, buy and hold, California gold rush, capital asset pricing model, Carmen Reinhart, carried interest, colonial rule, credit crunch, Credit Default Swap, Daniel Kahneman / Amos Tversky, debt deflation, discounted cash flows, diversified portfolio, dogs of the Dow, equity premium, estate planning, Eugene Fama: efficient market hypothesis, Fall of the Berlin Wall, family office, Fellow of the Royal Society, financial innovation, fixed income, Gordon Gekko, Henri Poincaré, high net worth, index fund, information asymmetry, interest rate swap, invention of the telegraph, James Hargreaves, James Watt: steam engine, joint-stock company, Kenneth Rogoff, labor-force participation, land tenure, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, margin call, means of production, Menlo Park, merger arbitrage, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Network effects, new economy, Nick Leeson, Own Your Own Home, Paul Samuelson, pension reform, Ponzi scheme, price mechanism, principal–agent problem, profit maximization, quantitative easing, RAND corporation, random walk, Renaissance Technologies, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Sand Hill Road, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, spinning jenny, statistical arbitrage, survivorship bias, technology bubble, The Wealth of Nations by Adam Smith, time value of money, too big to fail, transaction costs, underbanked, Vanguard fund, working poor, yield curve

The equity premium puzzle was described first in a 1985 paper by Rajnish Mehra and Edward Prescott.47 The central “puzzle” is that while investors should be compensated more for holding riskier equities than holding the risk-free instrument (Treasury bills), the amount by which they are compensated seems extremely excessive historically. In other words, it seems that equity holders have been “overpaid” to take on this risk. This paper set off a flurry of responses in the years after publication. There were some who suggested that it was merely survivorship bias that explained this phenomenon; that is, there were stocks that went bankrupt or otherwise delisted and so this high premium was not real after all.48 Others suggested that there were frictions unaccounted for, such as transaction costs. The behavioral economists mounted a different set of explanations. One of the most cited and well-regarded explanations, put forth by Shlomo Benartzi and Richard Thaler in 1995, is “myopic loss aversion,” a notion that borrows heavily from the concepts developed in prospect theory, including the fact that individuals tend to exhibit loss aversion and that they care about changes in wealth more keenly than about absolute levels of wealth.

The second, to which adherents of the market efficiency movement belong, holds that active management in the long term is usually not fruitful and that in general the capital markets are quite efficient in terms of pricing. Because of this, it has been significantly cheaper and much more productive for investors to select indexed vehicles. Some in this camp believe the origins of some of the best track records of active managers are produced by survivorship bias; that is, there are many failed investors for every great investor, and what separates greatness from failure is often luck or fortunate market tailwinds. The fees on “tracker” or “passive” funds, which make no claims to beating the market and simply try to replicate it in most instances, are just a few basis points (hundredths of a percentage point)—a far cry from the “2 percent and 20 percent” being charged by active hedge fund and private equity managers in the independent investment management world (see figure 9.2).

See Standard & Poor’s 500 speculation: art, stamps, coins, and wine, 283; in derivatives, 221; excesses, 197; impacts of, 232; value and, 4–5 spinning jenny, 71 split-strike conversion, 151–52 sponsor, 286–87 Stabilizing an Unstable Economy (Minsky), 214 Stagecoach Corporate Stock Fund, 284–85 Standard & Poor’s 500 (S&P 500), 187, 228, 285, 305–6, 309 Stanford, Allen, 153–56 Stanford, Leland, 155 Stanford Financial Group, 154 Starbucks, 277 State Street Corporation, 299 State Street Global Advisors, 299 State Street Investment Trust, 141 statistical arbitrage, 267 steam engine, 71 steamships, 90 Stefanadis, Chris, 94 sterling, 65 stock company, 134 stock exchanges: national or international, 94; new, 96; regional, 94–95 stock market: dislocations, 205; in England, 86–87; in Paris, 85 stock ownership: age and, 93–94; direct and indirect, 91, 93; gender and, 93–94; regulations prohibiting too much, 123; study of, 96; in United States, 90–94, 97 stock ticker, 89–90; network, 95 stones (horoi), 27, 60 Strong, Benjamin, 200–203, 206, 226 strong-form efficiency, 249 Studebaker-Packard Corporation, 111 sub hasta (public auction), 50 subprime, 39 subprime-mortgage lending, 223 Suetonius, 59 sugar consumption, in England, 75, 77 Sumerian city-states, 15–16 supply curve, 229 Supreme Court, 108 survivorship bias, 252 swap spread, 266 Swensen, David, 296, 328 SWFs. See sovereign wealth funds tail risk, 240, 246–47 taksitum (total profit), 52 Tammany Hall, 179 TARP. See Troubled Assets Relief Program TASS database, 269, 271 434 Investment: A History tax (gun mada), 16 taxes: carried interest and, 308; cuts, 219; endowments and, 124; ETFs and, 286; foundations and, 126–27; pensions and, 109–10, 112; REITs and, 281 Tax Reform Act of 1969, 126 Technical Revolution, 70 technology: bubble, 187, 223–24, 246, 263, 276, 287; public markets and, 89–90; venture capital and, 277–79 tegata (promissory notes), 46 telegraph, 89 telephone, 90, 95 Tellier, Walter, 179–80 temple bank (Siku), 29 tenant farmers, 17 Texas Gulf Sulphur, 193 Textron, 275 Thaler, Richard, 252 Theory of Interest, The (Fisher), 231 Theory of Investment Value, The (Williams, J.), 4, 232 “Theory of Speculation” (Bachelier), 230 thrifts, 135 Tiger Fund, 263 Timaeus (Plato), 24 timber, 282, 332 “tipping,” 192 Tobin, James, 231, 241–42 Tokistes, or Usurer, The (Alexis and Nicostratus), 24 “too big to fail,” 216, 219–20 Total Fitness Center, 154 total profit (taksitum), 52 totorum bonurum provisions, 52 Townsend, Francis, 107–8 trade: China and, 48; cities and, 42–43; commerce and, 8, 40–50; in goods compared to securities, 84; in India, 48–49; in Japan, 44–46, 60; Mediterranean Sea and, 41–42; in Mesopotamia, 41; rice, 45–46; in West, 40–42 trade associations, 48–49 trade clearing system, 174 trading frauds: Kerviel and Société Générale, 172–74; Leeson and Barings Bank, 170–72 transatlantic cable, 89 transparency: lack of, 130; level of, 98, 126 Treasury: Great Recession and, 217–18, 225; policies of, 197 Treasury Board, 175 Troubled Assets Relief Program (TARP), 218 trusteeship, 24 trusts, 55 Truth in Securities Act, 211 tutorship, 58 Tversky, Amos, 251–52 Twain, Mark, 161 Tweed, William, 179 UAW.


pages: 513 words: 152,381

The Precipice: Existential Risk and the Future of Humanity by Toby Ord

3D printing, agricultural Revolution, Albert Einstein, artificial general intelligence, Asilomar, Asilomar Conference on Recombinant DNA, availability heuristic, Columbian Exchange, computer vision, cosmological constant, cuban missile crisis, decarbonisation, defense in depth, delayed gratification, demographic transition, Doomsday Clock, Drosophila, effective altruism, Elon Musk, Ernest Rutherford, global pandemic, Intergovernmental Panel on Climate Change (IPCC), Isaac Newton, James Watt: steam engine, Mark Zuckerberg, mass immigration, meta analysis, meta-analysis, Mikhail Gorbachev, mutually assured destruction, Nash equilibrium, Norbert Wiener, nuclear winter, p-value, Peter Singer: altruism, planetary scale, race to the bottom, RAND corporation, Ronald Reagan, self-driving car, Stanislav Petrov, Stephen Hawking, Steven Pinker, Stewart Brand, supervolcano, survivorship bias, the scientific method, uranium enrichment

These suggest a risk in the range of 0.001 to 0.01 percent per century—or lower if we think we are more robust than a typical species (see Table 3.5). Note that all these estimates of species lifespans include other causes of extinction in addition to catastrophes, for example being slowly outcompeted by a new species that branches off from one’s own. So they will somewhat overestimate the risk of catastrophic extinction.65 SURVIVORSHIP BIAS There is a special difficulty that comes with investigating the likelihood of an event which would have prevented that very investigation. No matter how likely it was, we cannot help but find that the event didn’t occur. This comes up when we look at the extinction history of Homo sapiens, and it has the potential to bias our estimates.66 Imagine if there were a hundred planets just like our own.

So they couldn’t use the mere fact that they survived to estimate the fraction of planets where humans survive. This makes us realize that we too can’t deduce much about our future survival just from the fact that we have survived so far. However, we can make use of the length of time we have survived (as we do in this chapter), since there is more than one value that could be observed and we are less likely to see long lifespans in worlds with high risk. But a full accounting for this form of survivorship bias may still modify these risk estimates.67 Fortunately, estimating risk by analyzing the survival of other species is more robust to these effects and, reassuringly, it provides similar answers. Species: Homo neanderthalensis Years: 200,000 Best Guess: 0.05% Species: Homo heidelbergensis Years: 400,000 Best Guess: 0.025% Species: Homo habilis Years: 600,000 Best Guess: 0.02% Species: Homo erectus Years: 1,700,000 Best Guess: 0.006% Species: Mammals Years: 1,000,000 Best Guess: 0.01% Species: All species Years: 1,000,000–10,000,000 Best Guess: 0.01–0.001% TABLE 3.5 Estimates of total natural extinction risk per century based on the survival time of related species.

Such a systematic change in extinction risk over time would affect my analysis. However, it appears that species lifespans within each family are indeed fairly well approximated by a constant hazard rate (Van Valen, 1973; Alroy, 1996; Foote & Raup, 1996). 65 This could also be said of the previous method, as Homo sapiens is arguably a successful continuation of the species before it. 66 This special form of survivorship bias is sometimes known as anthropic bias or an observation selection effect. 67 My colleagues and I have shown how we can address these possibilities when it comes to estimating natural existential risk via how long humanity has survived so far (Snyder-Beattie, Ord & Bonsall, 2019). We found that the most biologically plausible models for how anthropic bias could affect the situation would cause only a small change to the estimated probabilities of natural risk. 68 All dates given are for when the event ended.


Learn Algorithmic Trading by Sebastien Donadio

active measures, algorithmic trading, automated trading system, backtesting, Bayesian statistics, buy and hold, buy low sell high, cryptocurrency, DevOps, en.wikipedia.org, fixed income, Flash crash, Guido van Rossum, latency arbitrage, locking in a profit, market fundamentalism, market microstructure, martingale, natural language processing, p-value, paper trading, performance metric, prediction markets, quantitative trading / quantitative finance, random walk, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, sorting algorithm, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, type inference, WebSocket, zero-sum game

However, there are rules to be followed in order to be as close as possible to the real market: Training/testing data: As with any models, you should not test your model with the data you use to create this model. You need to validate your data on unseen data to limit overfitting. When we use machine learning techniques, it is easy to overfit a model; that's why it is capital to use cross-validation to improve the accuracy of your model. Survivorship-bias free data: If your strategy is a long-term position strategy, it is important to use the survivorship-bias free data. This will prevent you from focusing on winners alone without considering the losers. Look-ahead data: When you build a strategy, you should not look ahead to make a trading decision. Sometimes, it is easy to make this mistake by using numbers calculated using the whole sample. This may be the case with an average that could potentially be calculated within all the data; data that you shouldn't have since you calculate the average using just the prices you get before placing an order.


pages: 836 words: 158,284

The 4-Hour Body: An Uncommon Guide to Rapid Fat-Loss, Incredible Sex, and Becoming Superhuman by Timothy Ferriss

23andMe, airport security, Albert Einstein, Black Swan, Buckminster Fuller, carbon footprint, cognitive dissonance, Columbine, correlation does not imply causation, Dean Kamen, game design, Gary Taubes, index card, Kevin Kelly, knowledge economy, life extension, lifelogging, Mahatma Gandhi, microbiome, p-value, Parkinson's law, Paul Buchheit, placebo effect, Productivity paradox, publish or perish, Ralph Waldo Emerson, Ray Kurzweil, Richard Feynman, selective serotonin reuptake inhibitor (SSRI), Silicon Valley, Silicon Valley startup, Skype, stem cell, Steve Jobs, survivorship bias, Thorstein Veblen, Vilfredo Pareto, wage slave, William of Occam

The challenge of the missing dropouts belies a common weakness with questionnaires that are open to the public: those most likely to respond are often those who have had positive results.8 This is a form of survivorship bias, a concept well worth understanding. Looking at average mutual fund returns from last year to pick a winner? Don’t forget that you are asking the survivors. The casualties—what Nassim Taleb refers to as “silent evidence”—aren’t around to be polled. The “average” returns are less impressive if you can include the people who bet the farm and lost. Finding those dead bodies is hard, especially in finances, when there is so much incentive to cover them up. In practical terms, does this mean our diet results are bogus? Not at all. The possibility of survivorship bias isn’t proof that the numbers aren’t representative. Two things to keep in mind: 1. Based on all available empirical reports on the diet, the failure rate shouldn’t exceed 5%.

Perhaps they were 250–300 pounds, making it easier to rack up total pounds lost, even though the weight lost as a percentage of body mass was more impressive for other smaller people. The vast majority of the total (144), those who averaged 19–20 pounds lost, ate three or four times per day, as recommended. COUNTING CALORIES SEEMS LIKE A NO-BRAINER, BUT IT’S NOT. 27 pounds lost vs. 20—again, the conclusion may seem obvious: calorie counting helps. Alas, it just ain’t that simple. First, more than in any other cohort in these data, this is where I suspect survivorship bias applies. 35 of 194 respondents counted calories. How many tried to count calories, which I do not recommend, and quit the diet altogether after finding counting tedious, impossible, or inconvenient? Second, do calorie counters really lose more weight because of counting calories? Or is it because they’re more attentive to the tracking in general and hold themselves more accountable? I suspect these calorie counters did a better job, on average, in more important areas like tracking protein intake and recording exercise progression.


pages: 337 words: 89,075

Understanding Asset Allocation: An Intuitive Approach to Maximizing Your Portfolio by Victor A. Canto

accounting loophole / creative accounting, airline deregulation, Andrei Shleifer, asset allocation, Bretton Woods, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, corporate governance, discounted cash flows, diversification, diversified portfolio, fixed income, frictionless, high net worth, index fund, inflation targeting, invisible hand, John Meriwether, law of one price, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low cost airline, market bubble, merger arbitrage, money market fund, new economy, passive investing, Paul Samuelson, price mechanism, purchasing power parity, risk tolerance, risk-adjusted returns, risk/return, Ronald Reagan, selection bias, shareholder value, Sharpe ratio, short selling, statistical arbitrage, stocks for the long run, survivorship bias, the market place, transaction costs, Y2K, yield curve, zero-sum game

Oil and Gas Journal 80, No. 2 (January 11, 1982): 92–101. Bova, Anthony, and Martin Leibowitz. “The Efficient Frontier Using Alpha ‘ Cores.’” Morgan Stanley Equity Research North America (January 7, 2005). Brown, Stephen J., William N. Goetzmann, and Roger G. Ibbotson. “Offshore Hedge Funds: Survival and Performance 1989–95.” Journal of Business 72, No. 1 (January 1999): 91–117. ———, ———, ———, and Stephen A. Ross. “Survivorship Bias in Performance Studies.” Review of Financial Studies 5, No. 4 (Winter 1992): 553–80. Campbell, John .,Y and Robert J. Shille r. “Valuation Ratios and the Long-Run Stock Market Outlook.” Journal of Portfolio Management 24, No. 2 (1998): 11–26. Bibliography 293 ——— and Tuomo Vuolteenaho. “Bad Beta, Good Beta.” The American Economic Review (December 2004): 1249–1275. Canto, Victor. “Deconstructing Market Returns.”

“Rates of Return on Investments in Common Stocks.” Journal of Business 37 (January 1964): 1–21. ——— and ———. “Rates of Return on Investments in Common Stocks: The ear-by-Y Y ear Record, 1926–1965.” Journal of Business 41 (July 1968): 291–316. Freiman, Eckhard. “Economic Integration and Country Allocation in Europe.” Financial Analyst Journal 54, No. 5 (September/October 1998): 32–41. Fung, William, and David Hsieh. “Survivorship Bias and Investment Style in the Returns of CTAs.” Journal of Portfolio Management 24, No. 1 (Fall 1997): 30–41. Goetzmann, William N., and Roger G. Ibbotson. “Do Winners Repeat? Patterns in Mutual Fund Performance.” Journal of Portfolio Management 20, No. 2 (Winter 1994): 9–18. Good, Walter R. “When Are Price/Earnings Ratios Too High-or Too Low?” Financial Analysts Journal 47, No. 4 (July/August 1991): 9–12, 25.


pages: 571 words: 105,054

Advances in Financial Machine Learning by Marcos Lopez de Prado

algorithmic trading, Amazon Web Services, asset allocation, backtesting, bioinformatics, Brownian motion, business process, Claude Shannon: information theory, cloud computing, complexity theory, correlation coefficient, correlation does not imply causation, diversification, diversified portfolio, en.wikipedia.org, fixed income, Flash crash, G4S, implied volatility, information asymmetry, latency arbitrage, margin call, market fragmentation, market microstructure, martingale, NP-complete, P = NP, p-value, paper trading, pattern recognition, performance metric, profit maximization, quantitative trading / quantitative finance, RAND corporation, random walk, risk-adjusted returns, risk/return, selection bias, Sharpe ratio, short selling, Silicon Valley, smart cities, smart meter, statistical arbitrage, statistical model, stochastic process, survivorship bias, transaction costs, traveling salesman

It is a sanity check on a number of variables, including bet sizing, turnover, resilience to costs, and behavior under a given scenario. A good backtest can be extremely helpful, but backtesting well is extremely hard. In 2014 a team of quants at Deutsche Bank, led by Yin Luo, published a study under the title “Seven Sins of Quantitative Investing” (Luo et al. [2014]). It is a very graphic and accessible piece that I would advise everyone in this business to read carefully. In it, this team mentions the usual suspects: Survivorship bias: Using as investment universe the current one, hence ignoring that some companies went bankrupt and securities were delisted along the way. Look-ahead bias: Using information that was not public at the moment the simulated decision would have been made. Be certain about the timestamp for each data point. Take into account release dates, distribution delays, and backfill corrections. Storytelling: Making up a story ex-post to justify some random pattern.

See Scikit-learn Stacked feature importance Standard bars (table rows) dollar bars purpose of tick bars time bars volume bars Stationarity data transformation method to ensure fractional differentiation applied to fractional differentiation implementation methods for integer transformation for maximum memory preservation for memory loss dilemma and Stop-loss, and investment strategy exit Stop-loss limits asymmetric payoff dilemma and cases with negative long-run equilibrium and cases with positive long-run equilibrium and cases with zero long-run equilibrium and daily volatility computation and fixed-time horizon labeling method and investment strategies using learning side and size and optimal trading rule (OTR) algorithm for strategy risk and triple-barrier labeling method for Storytelling Strategists Strategy risk asymmetric payouts and calculating implied betting frequency and implied precision and investment strategies and understanding of portfolio risk differentiated from probabilistic Sharpe ratio (PSR) similarity to strategy failure probability and symmetric payouts and Structural breaks CUSUM tests in explosiveness tests in sub- and super-martingale tests in types of tests in Sub- and super-martingale tests Supernova research Support vector machines (SVMs) Supremum augmented Dickey-Fuller (SADF) test conditional ADF implementation of quantile ADF Survivorship bias SymPy Live Synthetic data backtesting using experimental results using simulation combinations with optimal trading rule (OTR) framework using Tick bars Tick imbalance bars (TIBs) Tick rule Tick runs bars (TRBs) Time bars description of fixed-time horizon labeling method using Time-decay factors, and sample weights Time period, in backtesting Time series fractional differentiation applied to integer transformation for stationarity in stationarity vs. memory loss dilemma in Time under water (TuW) definition of deriving example of run measurements using Time-weighted average price (TWAP) Time-weighted rate of returns (TWRR) Trading rules investment strategies and algorithms in optimal trading rule (OTR) framework for overfitting in Transaction costs, in quantitative investing Tree clustering approaches, in asset allocation Triple-barrier labeling method Turnover costs Variance boosting to reduce causes of ensemble methods to reduce random forest (RF) method for Vectorization Volume bars Volume imbalance bars (VIBs) Volume runs bars (VRBs) Volume-synchronized probability of informed trading (VPIN) Walk-forward (WF) method backtesting using overfitting in pitfalls of Sharpe ratio estimation in two key advantages of Walk-forward timefolds method Weighted Kendall's tau Weights.


pages: 362 words: 99,063

The Education of Millionaires: It's Not What You Think and It's Not Too Late by Michael Ellsberg

affirmative action, Black Swan, Burning Man, corporate governance, creative destruction, financial independence, follow your passion, future of work, hiring and firing, job automation, knowledge worker, lateral thinking, Lean Startup, Mark Zuckerberg, means of production, mega-rich, meta analysis, meta-analysis, new economy, Norman Mailer, Peter Thiel, profit motive, race to the bottom, Sand Hill Road, shareholder value, side project, Silicon Valley, Skype, social intelligence, Steve Ballmer, survivorship bias, telemarketer, Tony Hsieh

Indeed, if there’s one single trait that sets all the self-educated millionaires I interviewed for this book apart from other people, it’s their relationship to risk. Critics of my book will likely say that what sets them apart is they simply took bigger risks than others: the people I interviewed were simply the winners at the roulette wheel, and I failed to talk about all the people who played at the wheel and got wiped out. (This line of critique would charge me with a fallacy of statistical reasoning known as “survivorship bias”: making assertions about some process based on conclusions drawn only via looking at the “winners” of that process, without taking into account the experience of the—usually much larger—sample of losers.5 ) And yet, I don’t believe the people I feature in this book simply took a bigger bet than everyone else and happened to get lucky and win. Rather, I’ve seen that they have systematically and intentionally developed a style of working that allows them to take lots of small bets—bet after bet after bet after bet—all the while making sure that they don’t get wiped out of the game if one or many of them go south.

Later in his book, he argues that more hours in school training hard in academic subjects, not fewer hours, is essential for inner-city kids’ success. 6 Shapiro, p. 782. 7 Williams, accessed January 15, 2010. 8 Kleinfield, accessed October 9, 2009. 9 Pink (2001), locations 197–199 on Kindle edition. 10 Pink, locations 810–819 on Kindle edition. 11 Pink, location 843 on Kindle edition. ■ SUCCESS SKILL #1 1 Johnson, location 1035 on Kindle edition. 2 Johnson, locations 2696–2714 on Kindle edition. 3 Komisar, p. 154 4 Komisar, pp. 65–66. 5 For a detailed and brilliant exposition of survivorship bias, see Taleb. 6 Godin (2010 A), accessed April 3, 2010. 7 Moskovitz, accessed March 22, 2010. ■ SUCCESS SKILL #2 1 Cohen, accessed December 13, 2010. 2 Bertoni, accessed December 13, 2010. 3 One of his product lines, the David DeAngelo series of trainings for men, is quite controversial. The early trainings in the series focus on helping men “pick up” women through pickup lines, tricks, and cocky attitudes.


All About Asset Allocation, Second Edition by Richard Ferri

activist fund / activist shareholder / activist investor, asset allocation, asset-backed security, barriers to entry, Bernie Madoff, buy and hold, capital controls, commoditize, commodity trading advisor, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, equity premium, estate planning, financial independence, fixed income, full employment, high net worth, Home mortgage interest deduction, implied volatility, index fund, intangible asset, Long Term Capital Management, Mason jar, money market fund, mortgage tax deduction, passive income, pattern recognition, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, Sharpe ratio, stocks for the long run, survivorship bias, too big to fail, transaction costs, Vanguard fund, yield curve

There are several companies that monitor the performance of hedge funds, although those published numbers are often biased. Some monitoring companies are paid by the hedge funds to promote the funds they report on. Other monitoring companies use flawed data collection methodologies. For instance, they do not include the performance of funds that have closed or merged. This produces an upward survivorship bias in the hedge fund indexes. When a hedge fund has a bad quarter, the managers may simply choose not to report the results. This leads to a selection bias in the index performance. Most monitoring companies allow a newly reporting fund to “backfill” performance with simulated historic returns that no investor actually earned. That creates a backfill bias in the indexes. Finally, most monitoring companies allow the hedge fund managers to price their own illiquid securities, thus introducing a pricing bias into the indexes.

., 88–89 “your age in bonds,” 243–244, 266–270, 295–296 (See also specific bond types) Bulletin-board stocks, 104, 104t C Canadian stocks, 138–139 Cash/cash-type investments, 10 Changing allocation, 291–299 guidelines for, 298–299 just before retirement, 294–295 and periodic market data, 296–297, 297f reasons for, 292 when goals are within reach, 293–294, 293f when investing for others, 295–296 Collectibles, 211–214, 212f, 213f Commercial real estate investments, 173–175 331 332 Commodities, 30, 189–195, 193f, 195f, 200–206, 201f, 202f, 204t, 224 indexes for, 194–195, 200–201 in portfolios, 201–203 real return on, 94 and supply and demand, 192–194 Commodity funds, 89 Commodity futures, 196–201, 197f, 200f, 203 Computer simulations, 81–82 Corporate bonds, 72–75, 151, 157–159, 229t, 230f Correlation, 47–53, 51t inconsistency of, 57–61, 95 low or varying, 95–97 measuring, 50 negative and positive, 48, 95 for real estate investments, 179–183 with U.S. stocks and bonds, 89 in well-diversified portfolios, 62 Costs of investing (see Fees and costs) Credit risk, 152–155, 153t, 154f, 155f Currency risk, 128–129, 128f, 133t D Default risk (bonds), 158–159 Deflation, 239 Developed markets, 130, 163 Developed-market indexes, 132–134 Diversification, 41, 43f, 55f, 59f, 60f, 60t, 62, 90 within funds, 97 with microcap stocks, 110, 113 rebalancing for, 45 with small-cap value stocks, 121–125 (See also Multi-asset-class investing) Dividends, 235–238 Dollar cost averaging, 309–311 E EAFE Index, 132–138, 133t, 134f, 136f, 138t, 140f Early savers, 244, 247–251, 250f, 250t, 251t Economic factor forecasting, 233–235 Efficient frontier, 54, 54f, 58–59, 66, 123, 124 Index Efficient market theory (EMT), 43 Emerging markets, 98–99, 130–131, 139–141, 140f, 140t, 141f, 163 Equity REITs, 176–183 Exchange-traded funds (ETFs), 311–313 of alternative assets, 214 capital gains on, 305–306 costs and fees with, 303, 304 for global diversification, 99 international, 144 in investment plan, 11–13, 21–22 low cost of, 97 Expectations for returns, 219 (See also Forecasting) F Factor performance analysis: in forecasting, 233–235 international equities, 142–143 U.S. equities, 109–121 Fad investing, 13–14 Fear of regret, 274–275 Federal Reserve, 235 Fees and costs, 301–315, 302t comparing fund expenses, 303–305 cost of taxation, 305–308 index funds and ETFs, 304f, 311–313 low-fee advisors, 313–315 and performance, 302–303 and tax swaps, 308–311 Fixed-income investments, 147–169, 150f, 156t, 167f, 167t, 168t, 223f bond market structure, 148–149 corporate bonds, 72–75 credit risk, 152–155 example of, 166–167 forecasting returns, 238–240 foreign market debt, 163–165 high-yield corporate bonds, 157–159 investment-grade bonds, 155–157 list of, 167–168 maturity structure, 151–152 risk and return with, 149–151 tax-exempt municipal bonds, 165–166 TIPS, 28, 29, 159–163 (See also specific investments) Index Forecasting, 13, 219–242, 234f, 236f, 241t creating forecasts, 240–241 and dividends, 235–238 economic factor, 233–235 Federal Reserve and GDP growth, 235 fixed income returns, 238–240 and inflation, 225–226 market returns, 220–221 risk-adjusted returns, 221–225 stacking risk premiums, 226–232 Foreign market debt, 163–165 Foreign stocks (see International equity investments) Frontier markets, 132 Fund expenses, 303–305 Fundamental differences, 90–93 G Global markets, 98–99, 98f, 129–132, 131f, 135f, 137f Government bonds, 151, 164f, 165f (See also Treasury bonds) Gross domestic product (GDP), 234f, 235 Growth stocks, 108, 114–116, 119–121 H Hedge funds, 190–191, 206–211, 208t High-yield corporate bonds, 157–159, 158f Home ownership, 173, 183–186, 259 I Index funds, 21–22, 304f, 311–313 commodities, 203–206 costs and fees with, 303–304 for global diversification, 99 low cost of, 97 U.S. equity, 125–126 value vs. growth, 92–93 Indexes, 106, 116–121, 118t, 119f, 120f bond, 155–157, 163–164 collectibles, 212–214 commodities, 194–195, 200–201 developed-market, 132–134 EAFE, 132–138 333 emerging market, 139–141 hedge fund, 209–210 international, 68–70 microcap, 111–113 midcap, 111 REIT, 177–178, 180–182 U.S. equities, 105 Inflation, 103, 221 and forecasting, 225–226 and interest rates, 238–240, 239f and real expected return, 94 and rental properties, 173 Inflation-protected securities, 28, 29, 162–163 [See also Treasury Inflation-Protected Securities (TIPS)] International equity investments, 127–145, 142t, 144t allocation of, 137–138, 143 Canadian stocks, 138–139 currency risk, 128–129, 128f, 133t developed-market indexes, 132–134 EAFE Index, 132–138, 133t, 134f, 136f, 138t, 140f emerging markets, 139–141, 140f, 140t, 141f global markets, 129–132, 131f, 135f, 137f list of, 143–144 in multi-asset-class investing, 68–72 size and value factors, 142–143 Investment plan, 3–23 academics’ views of, 19–20 asset allocation in, 15–16 asset classes in, 18–19 avoiding bad advice, 14–15 characteristics of, 4–6 and fad investing, 13–14 monitoring and adjusting, 16–18 mutual funds and ETFs in, 11–13 and overanalysis of market data, 22 and professional advice, 8 selection of investments, 21–22 and shortcuts, 6–7 types of assets in, 9–11 Investment policy statement (IPS), xiii, 5 334 Investment pyramid, 9–11, 9f, 245–247, 246f Investment risk, 25–39 defining, 29–31 and myth of risk-free investments, 26–29 as running out of money in retirement, 31–32 volatility as, 32–38 Investment styles, 19 Investment-grade bonds, 152–157 L Large-cap stocks, 107–109, 117t, 119 Large-cap style indexes, 117 Liability matching, 253–254 Life-cycle investing, 17–18, 243–270 early savers, 247–251, 250f, 250t, 251t investment pyramid in, 245–247, 246f and life phases, 244–245 mature retirees, 263–266, 265f, 265t, 266t midlife accumulators, 252–256, 255f, 255t, 256t modified “your age in bonds” for, 266–270 transitional retirees, 256–262, 261f, 262t Limited partnerships (real estate), 174 Long-term investments, 10 Low-cost asset classes, 97–98 Low-fee advisors, 313–315 M Market data, 22, 296–297 Market returns, forecasting, 220–221 Market risk factor, 116, 272 Markets: bear, 276–277, 294–295 bond, 148–149 developed-market indexes, 132–134 and dividends, 235–238 emerging, 139–141 foreign market debt, 163–165 global, 98–99, 129–132 Index overanalysis of market data, 22 periodic market data, 296–297 stock, 103–105 (See also specific markets) Markowitz, Harry, 41–43 Mature retirees, 244–245, 263–266, 265f, 265t, 266t Microcap stocks, 107–113, 110t, 111f, 124f Midcap stocks, 107–109, 111–113, 111f Midlife accumulators, 244, 252–256, 255f, 255t, 256t Modern portfolio theory (MPT), viii, 43–44, 79, 171, 189, 271 Morningstar classifications, 106–109, 107f, 108t, 109f Morningstar ratings, 14 Multi-asset-class investing, 65–83, 67f corporate bonds, 72–75, 73t, 74f example of, 75–79, 76f, 76t, 78–79f for expanding the envelope, 66–67 international stocks, 68–72, 69t, 70f, 71f Municipal bonds, tax-exempt, 148, 165–166 Mutual funds, 30, 92f, 93f, 148 of alternative assets, 214 capital gains on, 305–306 commodities, 203–206 costs and fees with, 303–305 emerging market, 131 global equity, 130 high-yield bonds, 159 international, 144 in investment plan, 11–13 in late 1990s, 92–93 low-cost fixed-income, 167–168 no-load actively managed, 97 REIT, 174, 175, 187 swapping, 308–309 (See also Index funds) N Nasdaq, 103, 104, 104t New York Stock Exchange (NYSE), 103, 104, 104t No-load actively managed funds, 97 Index Noncorrelation, 48–53, 51f Northwest quadrant, 54, 55f, 66, 80 P Passive funds, 21 Pension plans, 30, 258–259 Performance: factor performance analysis, 109–121 and fees, 302–303 and future results, 14 and investment cost, 302–303 long-term, 16 (See also Forecasting; Returns) Portfolio building (see Investment plan; Life-cycle investing) Portfolio risk, 26, 275 Price-to-earnings (P/E) ratio, 236–238, 237f Pricing bias, 209 Primary market, 103 Professional advisor(s), 8, 14–15, 313–315 R Real estate investment trusts (REITs), 174–182f, 186–187, 187t Real estate investments, 171–187, 172t commercial, 173–175 correlation analysis, 179–183 home ownership, 183–186, 259 list of, 186–187 REITs, 174–182f, 186–187, 187t Real return, 161 on commodities, 94 on U.S. stocks and bonds, 102–103 Rebalancing, 44–47, 46t, 55, 59, 67, 284–285 Regression to the mean, 45 Retirement: bear markets just before, 294–295 and life-cycle investing, 256–266 running out of money in, 31–32 Returns, 35–38, 35t, 56t, 222t and asset allocation, 20 expectations for (see Forecasting) fixed-income, 149–151, 238–240 on international investments, 68–70 335 market, 220–221 with multi-asset-class investing, 75–77 real, 94, 161 on real estate investments, 171–173 on REITs, 180–183 and risk, 35, 53–57, 61f, 223f risk-adjusted, 221–225, 221t on U.S. equity investments, 102–103, 102t Risk: credit, 152–155, 153t, 154f, 155f currency, 128–129, 128f, 133t default, 158–159 with fixed-income investments, 149–151 investment, 25–39 perceived, 26 rebalancing, 284–285 with REITs, 180–183 and return, 35, 53–57, 61f, 221–225, 223f with small-cap value stocks, 121–125 volatility as, 32–38 Risk avoidance, 285–286, 286t Risk diversification, 90, 121–125 Risk premiums, stacking, 226–232, 232t Risk tolerance, 17, 275 Risk tolerance questionnaires, 16–17, 277–278, 287–289 Risk-adjusted returns, 221–225, 221t Risk-free investments, myth of, 26–29 Rolling correlations, 58f, 96, 96f S Secondary market, 103 Selecting investments, 21–22, 87–100 four-step process for, 88 with fundamental differences, 90–93 in global markets, 98–99 guidelines for, 89–98 with low or varying correlation, 95–97 with low-cost availability, 97–98 with real expected return, 94 U.S. stocks and bonds, 88–89 Index 336 Selection bias, 209 Size factor: international equity investments, 142–143 U.S. equity investments, 106, 107 Size risk factor, 116 Small-cap stocks, 107–109, 118t, 121–125, 122t, 123f, 124f, 230–231, 231f Small-cap style indexes, 117–118 Social Security, 10, 11, 258–259 Speculative capital, 11 Stacking risk premiums, 226–232, 232t Standard deviation, 33–38, 34f, 37t, 38t Stock markets, 105 1987 crash, 30–31 in 1990s, 276 in 2007–2009, 276–277 during crises, 89 Stocks, 19, 89–90, 229–230 Canadian, 138–139 international, 68–72 (See also International equity investments) small-cap value, 121–125 U.S., 88–89 (See also U.S. equity investments) Style factor, 107–109 Survivorship bias, 209 T Tax swaps, 308–311, 309f, 310t Tax-deferred accounts, 306–307 Taxes, 19 and after-inflation returns, 225–226 on bonds, 165–166 on commodity funds, 205–206 as investment expense, 305–308 on T-bill returns, 27–28 Tax-exempt municipal bonds, 148, 165–166 Total risk, xi–xii, 43f Transitional retirees, 244, 256–262, 261f, 262t Treasury bills (T-bills), 26–28, 27f, 28f, 151–152, 152f, 225–227, 226f Treasury bonds, 72–75, 151–152, 160–163, 161f, 229t Treasury iBonds, 162–163 Treasury Inflation-Protected Securities (TIPS), 28, 29, 156, 159–163, 161f, 162f, 227–228, 228f, 240 Treasury notes, 152f Two-asset-class model, 53 U Unit investment trusts (IUTs), 97 U.S. bond investments, 88–89 U.S. equity investments, 101–126, 125t, 140f, 141f, 230f and broad stock market, 105 and currency risk, 128–129 factor performance analysis, 109–121 history of returns on, 102–103 list of, 125–126 and market structure, 103–104 Morningstar classification methods, 106–109 selecting, 88–89 sizes and styles of, 106–109 small-cap value and risk diversification, 121–125 V Value risk factor, 116, 142–143 Value stocks, 108, 114–116, 119–125, 231f Volatility, 222, 224, 225f of commodity prices, 94 of foreign stocks, 127–128 of international stocks, 71, 72 as investment risk, 29, 32–38 measuring, 32–34, 33f, 34f price, 29–30 Y Yield spread, 74 “Your age in bonds” approach, 243–244, 266–270, 295–296


pages: 319 words: 106,772

Irrational Exuberance: With a New Preface by the Author by Robert J. Shiller

Andrei Shleifer, asset allocation, banking crisis, Benoit Mandelbrot, business cycle, buy and hold, computer age, correlation does not imply causation, Daniel Kahneman / Amos Tversky, demographic transition, diversification, diversified portfolio, equity premium, Everybody Ought to Be Rich, experimental subject, hindsight bias, income per capita, index fund, Intergovernmental Panel on Climate Change (IPCC), Joseph Schumpeter, Long Term Capital Management, loss aversion, mandelbrot fractal, market bubble, market design, market fundamentalism, Mexican peso crisis / tequila crisis, Milgram experiment, money market fund, moral hazard, new economy, open economy, pattern recognition, Ponzi scheme, price anchoring, random walk, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Small Order Execution System, spice trade, statistical model, stocks for the long run, survivorship bias, the market place, Tobin tax, transaction costs, tulip mania, urban decay, Y2K

Glassman and Kevin Hassett, Dow 36,000: The New Strategy for Profiting from the Coming Rise in the Stock Market (New York: Times Business/Random House, 1999), p. 140. 13. See, for example, William Goetzmann and Roger Ibbotson, “Do Winners Repeat? Patterns in Mutual Fund Performance,” Journal of Portfolio Management, 20 (1994): 9–17; Edwin J. Elton, Martin Gruber, and Christopher R. Blake, “Survivorship Bias and Mutual Fund Performance,” Review of Financial Studies, 9(4) (1996): 1097–120; and “The Persistence of Risk-Adjusted Mutual Fund Performance,” Journal of Business, 69 (1996): 133–37. 14. To the extent that mutual funds make better diversification possible for individual investors, they lower the riskiness of stocks, and therefore the proliferation of mutual funds may lower the risk premium that investors require.

Golden Fetters: The Gold Standard and the Great Depression: 1919–1939. New York: Oxford University Press, 1992. Eichengreen, Barry, James Tobin, and Charles Wyplosz. “Two Cases for Sand in the Wheels of International Finance.” Economic Journal, 105 (1995): 162–72. Elias, David. Dow 40,000: Strategies for Profiting from the Greatest Bull Market in History. New York: McGraw-Hill, 1999. Elton, Edwin J., Martin Gruber, and Christopher R. Blake. “Survivorship Bias and Mutual Fund Performance.” Review of Financial Studies, 9(4) (1996): 1097–1120. ———. “The Persistence of Risk-Adjusted Mutual Fund Performance,” Journal of Business, 69 (1996): 133–37. Fair, Ray C. “How Much Is the Stock Market Overvalued?” Unpublished paper, Cowles Foundation, Yale University, 1999. Fama, Eugene. “Efficient Capital Markets: A Review of Theory and Empirical Work.” Journal of Finance, 25 (1970): 383–417.


pages: 339 words: 109,331

The Clash of the Cultures by John C. Bogle

asset allocation, buy and hold, collateralized debt obligation, commoditize, corporate governance, corporate social responsibility, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, diversified portfolio, estate planning, Eugene Fama: efficient market hypothesis, financial innovation, financial intermediation, fixed income, Flash crash, Hyman Minsky, income inequality, index fund, interest rate swap, invention of the wheel, market bubble, market clearing, money market fund, mortgage debt, new economy, Occupy movement, passive investing, Paul Samuelson, Ponzi scheme, post-work, principal–agent problem, profit motive, random walk, rent-seeking, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, shareholder value, short selling, South Sea Bubble, statistical arbitrage, survivorship bias, The Wealth of Nations by Adam Smith, transaction costs, Vanguard fund, William of Occam, zero-sum game

During that long period, as shown in Exhibit 4.2, the cumulative wealth gains created on an initial investment of $10,000 in the unmanaged 500 Index exceeded the average comparable actively managed equity fund—an enhancement in wealth of more than 50 percent. Exhibit 4.2 Returns of Large-Cap Equity Mutual Funds: $10,000 Invested over 15 Years (1997–2011) Source: Morningstar, adjusted for survivorship bias. As such, these returns are substantially lower than those displayed in Exhibits 4.3 and 4.4, which include survivorship bias. Large Cap Category Annual Return Investment Gain From Original $10,000 Investment Core Funds 3.9% $7,750 Growth Funds 3.7 7,250 Value Funds 4.6 9,630 Average 4.1% $8,270 S&P 500 Index 5.4% $12,010 Exhibit 4.3 Fund Returns versus Investor Returns Over 15 Years (1997–2011) Source: Morningstar. *These returns have not been adjusted for survivor bias.


pages: 537 words: 144,318

The Invisible Hands: Top Hedge Fund Traders on Bubbles, Crashes, and Real Money by Steven Drobny

Albert Einstein, Asian financial crisis, asset allocation, asset-backed security, backtesting, banking crisis, Bernie Madoff, Black Swan, Bretton Woods, BRICs, British Empire, business cycle, business process, buy and hold, capital asset pricing model, capital controls, central bank independence, collateralized debt obligation, commoditize, Commodity Super-Cycle, commodity trading advisor, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, currency peg, debt deflation, diversification, diversified portfolio, equity premium, family office, fiat currency, fixed income, follow your passion, full employment, George Santayana, Hyman Minsky, implied volatility, index fund, inflation targeting, interest rate swap, inventory management, invisible hand, Kickstarter, London Interbank Offered Rate, Long Term Capital Management, market bubble, market fundamentalism, market microstructure, moral hazard, Myron Scholes, North Sea oil, open economy, peak oil, pension reform, Ponzi scheme, prediction markets, price discovery process, price stability, private sector deleveraging, profit motive, purchasing power parity, quantitative easing, random walk, reserve currency, risk tolerance, risk-adjusted returns, risk/return, savings glut, selection bias, Sharpe ratio, short selling, sovereign wealth fund, special drawing rights, statistical arbitrage, stochastic volatility, stocks for the long run, stocks for the long term, survivorship bias, The Great Moderation, Thomas Bayes, time value of money, too big to fail, transaction costs, unbiased observer, value at risk, Vanguard fund, yield curve, zero-sum game

If I am a pension fund manager, I am going to be worried about interest rates going up on a forward basis and inflation in a low-growth world, which does not necessarily lead me to equities. The challenge with investment committees or boards is not that they only meet quarterly but that they tend to drive portfolios to the things that have performed well recently—the things that are in vogue. The focus is too oriented on “survivorship bias.” At these extremes, on the upside or the downside, people do not interpolate based on the current data set but rather extrapolate based on recent past experience. Said another way, they chase returns, and that is a big problem. Good macro investors try to do the inverse: at extremes they interpolate based on small data points and do not necessarily try to extrapolate Armageddon or euphoria.

See Risk premia payment Price/earnings (P/E) multiples, exchange rate valuation (relationship) Primary Dealer Credit Facility, placement Prime broker risk Princeton University (endowment) Private equity cash flow production tax shield/operational efficiency arguments Private sector debt, presence Private-to-public sector risk Probability, Bayesian interpretation Professor, The bubble predication capital loss, avoidance capital management cataclysms, analysis crowding factor process diversification efficient markets, disbelief fiat money, cessation global macro fund manager hedge fund space historical events, examination idea generation inflation/deflation debate interview investment process lessons LIBOR futures ownership liquidity conditions, change importance market entry money management, quality opportunities personal background, importance portfolio construction management positioning process real macro success, personality traits/characteristics (usage) returns, generation risk aversion rules risk management process setback stocks, purchase stop losses time horizon Titanic scenario threshold trades attractiveness, measurement process expression, options (usage) personal capital, usage quality unlevered portfolio Property/asset boom Prop shop trading, preference Prop trader, hedge fund manager (contrast) Protectionism danger hedge process Public college football coach salary, public pension manager salary (contrast) Public debt, problems Public pensions average wages to returns endowments impact Q ratio (Tobin) Qualitative screening, importance Quantitative easing (QE) impact usage Quantitative filtering Random walk, investment Real annual return Real assets Commodity Hedger perspective equity-like exposure Real estate, spread trade Real interest rates, increase (1931) Real macro involvement success, personality traits/characteristics (usage) Real money beta-plus domination denotation evolution flaws hedge funds, differentiation impacts, protection importance investors commodity exposure diversification, impact macro principles management, change weaknesses Real money accounts importance long-only investment focus losses (2008) Real money funds Commodity Hedger operation Equity Trader management flexibility frontier, efficiency illiquid asset avoidance importance leverage example usage management managerial reserve optimal portfolio construction failure portfolio management problems size Real money managers Commodity Investor scenario liquidity, importance long-term investor misguidance poor performance, usage (excuse) portfolio construction valuation approach, usage Real money portfolios downside volatility, mitigation leverage, amount management flaws Rear view mirror investment process Redemptions absence problems Reflexivity Rehypothecation Reichsmarks, foreign holders (1922-1923) Relative performance, inadequacy Reminiscences of a Stock Operator (Lefèvre) Renminbi (2005-2009) Repossession property levels Republic of Turkey examination investment rates+equities (1999-2000) Reserve currency, question Resource nationalism Returns forecast generation maximization momentum models targets, replacement Return-to-worst-drawdown, ratios (improvement) Reward-to-variability ratio Riksbank (Sweden) Risk amount, decision aversion rules capital, reduction collars function positive convexity framework, transition function global macro manager approach increase, leverage (usage) measurement techniques, importance parameters Pensioner management pricing reduction system, necessity Risk-adjusted return targets, usage Risk assets, decrease Risk-free arbitrage opportunities Risk management Commodity Hedger process example game importance learning lessons portfolio level process P&L, impact tactic techniques, importance Risk premia annualization earning level, decrease specification Risk/reward trades Risk-versus-return, Pensioner approach Risk-versus-reward characteristics opportunities Roll yield R-squared (correlation) Russia crisis Russia Index (RTSI$) (1995-2002) Russia problems Savings ratio, increase Scholes, Myron Sector risk, limits Securities, legal lists Self-reinforcing cycles (Soros) Sentiment prediction swings Seven Sisters Sharpe ratio increase return/risk Short-dated assets Short selling, ban Siegel’s Paradox example Single point volatility 60-40 equity-bond policy portfolio 60-40 model 60-40 portfolio standardization Smither, Andrew Socialism, Equity Trader concern Society, functioning public funds, impact real money funds, impact Softbank (2006) Soros, George self-reinforcing cycles success Sovereign wealth fund Equity Trader operation operation Soybeans (1970-2009) Special drawing rights (SDR) Spot price, forward price (contrast) Spot shortages/outages, impact Standard deviation (volatility) Standard & Poor’s 500 (S&P500) (2009) decrease Index (1986-1995) Index (2000-2009) Index (2008) shorting U.S. government bonds, performance (contrast) Standard & Poor’s (S&P) shorts, coverage Stanford University (endowment) State pension fund Equity Trader operation operation Stochastic volatility Stock index total returns (1974-2009) Stock market increase, Predator nervousness Stocks hedge funds, contrast holders, understanding pickers, equity index futures usage shorting/ownership, contrast Stops, setting Stress tests, conducting Subprime Index (2007-2009) Sunnies, bidding Super Major Survivorship bias Sweden AP pension funds government bond market Swensen, David equity-centric portfolio Swiss National Bank (SNB) independence Systemic banking crisis Tactical asset allocation function models, usage Tactical expertise Tail hedging, impact Tail risk Take-private LBO Taleb, Nassim Tax cut sunset provisions Taxes, hedge Ten-year U.S. government bonds (2008-2009) Theta, limits Thundering Herd (Merrill Lynch) Time horizons decrease defining determination shortening Titanic funnel, usage Titanic loss number Titanic scenario threshold Topix Index (1969-2000) Top-line inflation Total credit market, GDP percentage Total dependency ratio Trade ideas experience/awareness, impact generation process importance origination Traders ability Bond Trader hiring characteristics success, personality characteristics Trades attractiveness, measurement process hurdle money makers, percentage one-year time horizon selection, Commodity Super Cycle (impact) time horizon, defining Trading decisions, policy makers (impact) floor knowledge noise level ideas, origination Tragedy of the commons Transparency International, Corruption Perceptions Index Treasury Inflation-Protected Securities (TIPS) trade Triangulated conviction Troubled Asset Relief Program (TARP) Turkey economy inflation/equities (1990-2009) investment rates+equities (1999-2000) stock market index (ISE 100) Unconventional Success (Swensen) Underperformance, impact Undervaluation zones, examination United Kingdom (UK), two-year UK swap rates (2008) United States bonds pricing debt (1991-2008) debt (2000-2008) home prices (2000-2009) hyperinflation listed equities, asset investment long bonds, market pricing savings, increase stocks tax policy (1922-1936) trade deficit, narrowing yield curves (2004-2006) University endowments losses impact unlevered portfolio U.S.


pages: 504 words: 139,137

Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined by Lasse Heje Pedersen

activist fund / activist shareholder / activist investor, algorithmic trading, Andrei Shleifer, asset allocation, backtesting, bank run, banking crisis, barriers to entry, Black-Scholes formula, Brownian motion, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, conceptual framework, corporate governance, credit crunch, Credit Default Swap, currency peg, David Ricardo: comparative advantage, declining real wages, discounted cash flows, diversification, diversified portfolio, Emanuel Derman, equity premium, Eugene Fama: efficient market hypothesis, fixed income, Flash crash, floating exchange rates, frictionless, frictionless market, Gordon Gekko, implied volatility, index arbitrage, index fund, interest rate swap, late capitalism, law of one price, Long Term Capital Management, margin call, market clearing, market design, market friction, merger arbitrage, money market fund, mortgage debt, Myron Scholes, New Journalism, paper trading, passive investing, price discovery process, price stability, purchasing power parity, quantitative easing, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, selection bias, shareholder value, Sharpe ratio, short selling, sovereign wealth fund, statistical arbitrage, statistical model, stocks for the long run, stocks for the long term, survivorship bias, systematic trading, technology bubble, time value of money, total factor productivity, transaction costs, value at risk, Vanguard fund, yield curve, zero-coupon bond

Since funds are more likely to start reporting after having experienced good performance, this leads to a “backfill bias”: Funds that have poor performance from the beginning never make it into the database, while better performing funds are more likely to start reporting. Some databases and researchers account for this by only including returns from a certain time period after the hedge funds started reporting, disregarding the biased backfilled data. Another effect is that some hedge funds stop reporting when they experience poor performance, leading to a “survivorship bias.” A bias pulling in the opposite direction arises from the fact that the most successful hedge funds often do not report to the databases. These funds value their privacy and do not need any additional exposure to clients; they may in fact be closed to new investments due to limited capacity. Hence, the databases exclude some of the most impressive track records, such as that of Renaissance Technologies.

See hedge fund strategies stress loss, 59 stress tests, 32, 59; margin requirements and, 77 strike price, 235–36 structured credit, 262 stub, 309–11, 310t, 310f style drift, 72, 73f styles of investment, ix, 2, 14–16. See also specific styles subprime credit crisis, xii; “greatest trade ever” in, 2, 292, 313, 320–22; ripple effects on banks and hedge funds, 145; spreading to other markets, 83, 84f. See also global financial crisis of 2007–2009 subsidiaries. See carve-outs; spin-offs; split-offs supply shocks, 5, 194–96, 195t; as catalyst of trend, 210 survivorship bias, 23 suspending redemptions, 75 swap contracts, margin requirements for, 80 swap rate, 259 swaps. See credit default swaps (CDSs); interest-rate swaps swap spreads, 13, 241, 259–60 swap spread tightener, 259–60 swaptions, 241, 262 systematic global tactical asset allocation funds, 185 systematic macro hedge funds, 185 systematic risk. See beta tactical asset allocation, 167, 175–76; global macro funds using, 176, 185.


pages: 467 words: 154,960

Trend Following: How Great Traders Make Millions in Up or Down Markets by Michael W. Covel

Albert Einstein, Atul Gawande, backtesting, beat the dealer, Bernie Madoff, Black Swan, buy and hold, buy low sell high, capital asset pricing model, Clayton Christensen, commodity trading advisor, computerized trading, correlation coefficient, Daniel Kahneman / Amos Tversky, delayed gratification, deliberate practice, diversification, diversified portfolio, Edward Thorp, Elliott wave, Emanuel Derman, Eugene Fama: efficient market hypothesis, Everything should be made as simple as possible, fiat currency, fixed income, game design, hindsight bias, housing crisis, index fund, Isaac Newton, John Meriwether, John Nash: game theory, linear programming, Long Term Capital Management, mandelbrot fractal, margin call, market bubble, market fundamentalism, market microstructure, mental accounting, money market fund, Myron Scholes, Nash equilibrium, new economy, Nick Leeson, Ponzi scheme, prediction markets, random walk, Renaissance Technologies, Richard Feynman, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, survivorship bias, systematic trading, the scientific method, Thomas L Friedman, too big to fail, transaction costs, upwardly mobile, value at risk, Vanguard fund, William of Occam, zero-sum game

To evaluate the effectiveness of trend following on stocks we must first determine: • What stocks will be considered? • When and how will a stock be purchased? • When and how will a stock be sold? 307 A 308 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets Data Integrity Data Coverage The database used included 24,000+ individual securities from the NYSE, AMEX, and NASDAQ exchanges. Coverage spanned from January 1983 to December 2004. Survivorship Bias The database used for this project included historical data for all stocks that were delisted at some point between 1983 and 2004. Slightly more than half of the database is composed of delisted stocks. Corporate Actions All stock prices were proportionately back adjusted for corporate actions, including cash dividends, splits, mergers, spinoffs, stock dividends, reverse splits, and so on.

Delisted Stocks, Symbol Overlap, and Unique Identifiers In our experience, a very common mistake made in testing stock trading strategies is the failure to understand and deal with the reality that actively traded securities existed for companies that have since gone out of business or have been acquired by other companies. These securities will not show up in most databases. Only the securities of “surviving” companies will show up in the typical database or charting service. To account for this survivorship bias, delisted companies were included in our universe. Because current companies sometimes use ticker symbols that were previously used by former (since delisted) companies, a unique serial number was necessary to identify each stock. At the time of this writing, the entire database showed 24,057 individual securities. However, only 11,384 securities were active on U.S. exchanges. This left 12,673 securities that did exist historically but do not exist now.


100 Baggers: Stocks That Return 100-To-1 and How to Find Them by Christopher W Mayer

bank run, Bernie Madoff, business cycle, buy and hold, cloud computing, disintermediation, Dissolution of the Soviet Union, dumpster diving, Edward Thorp, hindsight bias, housing crisis, index fund, Jeff Bezos, market bubble, Network effects, new economy, oil shock, passive investing, peak oil, shareholder value, Silicon Valley, Stanford marshmallow experiment, Steve Jobs, survivorship bias, The Great Moderation, The Wisdom of Crowds

This would be the main population of stocks I poked and prodded in the six months after we created the database. I want to say a few words about what I set out to do—and what I don’t want to do. There are severe limitations or problems with a study like this. For one thing, I’m only looking at these extreme successes. There is hindsight bias, in that things can look obvious now. And there is survivorship bias, in that other companies may have looked similar at one point but failed to deliver a hundredfold gain. I am aware of these issues and others. They are hard to correct. I had a statistician, a newsletter reader, kindly offer to help. I shared the 100-bagger data with him. He was aghast. He related his concerns using a little story. As he wrote to me, Let’s say I am curious to find out what it is that makes basketball players so tall.


pages: 741 words: 179,454

Extreme Money: Masters of the Universe and the Cult of Risk by Satyajit Das

affirmative action, Albert Einstein, algorithmic trading, Andy Kessler, Asian financial crisis, asset allocation, asset-backed security, bank run, banking crisis, banks create money, Basel III, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Black Swan, Bonfire of the Vanities, bonus culture, Bretton Woods, BRICs, British Empire, business cycle, capital asset pricing model, Carmen Reinhart, carried interest, Celtic Tiger, clean water, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate governance, corporate raider, creative destruction, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, Daniel Kahneman / Amos Tversky, debt deflation, Deng Xiaoping, deskilling, discrete time, diversification, diversified portfolio, Doomsday Clock, Edward Thorp, Emanuel Derman, en.wikipedia.org, Eugene Fama: efficient market hypothesis, eurozone crisis, Everybody Ought to Be Rich, Fall of the Berlin Wall, financial independence, financial innovation, financial thriller, fixed income, full employment, global reserve currency, Goldman Sachs: Vampire Squid, Gordon Gekko, greed is good, happiness index / gross national happiness, haute cuisine, high net worth, Hyman Minsky, index fund, information asymmetry, interest rate swap, invention of the wheel, invisible hand, Isaac Newton, job automation, Johann Wolfgang von Goethe, John Meriwether, joint-stock company, Jones Act, Joseph Schumpeter, Kenneth Arrow, Kenneth Rogoff, Kevin Kelly, laissez-faire capitalism, load shedding, locking in a profit, Long Term Capital Management, Louis Bachelier, margin call, market bubble, market fundamentalism, Marshall McLuhan, Martin Wolf, mega-rich, merger arbitrage, Mikhail Gorbachev, Milgram experiment, money market fund, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, mutually assured destruction, Myron Scholes, Naomi Klein, negative equity, NetJets, Network effects, new economy, Nick Leeson, Nixon shock, Northern Rock, nuclear winter, oil shock, Own Your Own Home, Paul Samuelson, pets.com, Philip Mirowski, plutocrats, Plutocrats, Ponzi scheme, price anchoring, price stability, profit maximization, quantitative easing, quantitative trading / quantitative finance, Ralph Nader, RAND corporation, random walk, Ray Kurzweil, regulatory arbitrage, rent control, rent-seeking, reserve currency, Richard Feynman, Richard Thaler, Right to Buy, risk-adjusted returns, risk/return, road to serfdom, Robert Shiller, Robert Shiller, Rod Stewart played at Stephen Schwarzman birthday party, rolodex, Ronald Reagan, Ronald Reagan: Tear down this wall, Satyajit Das, savings glut, shareholder value, Sharpe ratio, short selling, Silicon Valley, six sigma, Slavoj Žižek, South Sea Bubble, special economic zone, statistical model, Stephen Hawking, Steve Jobs, survivorship bias, The Chicago School, The Great Moderation, the market place, the medium is the message, The Myth of the Rational Market, The Nature of the Firm, the new new thing, The Predators' Ball, The Wealth of Nations by Adam Smith, Thorstein Veblen, too big to fail, trickle-down economics, Turing test, Upton Sinclair, value at risk, Yogi Berra, zero-coupon bond, zero-sum game

Taking the losses into account, Tiger returned 25 percent per annum over its life. Starting life in 1980 with $10 million, it had $22 billion under management by 1998. The fund’s highest percentage returns were on a small dollar base. The losses came from a larger base (a 50 percent loss on $22 billion is a loss of $11 billion). Tiger may have lost more dollars than it made over its life.13 Historical returns exclude funds that fail or no longer accept new investment—survivorship bias. Only funds with a successful track record report performance—backfill bias. The difference between the best and worst performing funds is large. For investors seeking alpha, high average returns are meaningless, like a comfortable average ambient temperature where your feet are in the oven and your head is in the refrigerator. A confluence of events boosted hedge fund returns. Macro funds benefited from the growth of emerging economies, the end of communism in Eastern Europe, world trade and deregulation of financial markets.

See also Erin Burnett stress cardiomyopathy, 177 strike prices, 120, 209 strip mining, 156 structures bids, 192 credit, 188 finance, 188 style drift, 242 subjective truths, 130 subordinated (“sub” or “junior”) debt, 148 subordination levels, 172 subprime mortgages, 70. See also mortgages shorting (2005/2006), 256 subsidies, 334, 348 Suma Oriental, 82 Sumitomo, 227 Summers, Lawrence, 116, 129, 214, 300, 304, 315 Sunday Times, 364 super jumbo loans, 182 Super Return annual industry conference, 162 super senior tranches, 175 supply of assets, 267 survivorship bias, 243 suspension of deep-water drilling, 362 Suze Orman Show, The, 93 Suze Orman’s Financial Freedom, 93 swaps correlation, 255 credit default swaps (CDS), 232, 237 dispersion, 255 Fiat, 222-223 first-to-default (FtD), 220-221 gamma, 255 total return swap (TRS), 209 Swensen, David, 124 Swift, Jonathon, 130 Sydney Airport, 159 synchronous lateral excitation, 273 synthetic securitization, 173, 176 systematic risk, 118 T TAC (target amortization class) bonds, 178 TAF (term auction facility), 340 tail risk, 246 Tainter, Joseph, 349 takeovers (risk arb), 242 Taleb, Nicholas Nassim, 126, 246 Talking Heads, The, 46 taming risk, 120-122 Tang dynasty, 351 tansu savings, 39 Tao Jones Averages, The, 96 TARDIS (Time And Relative Dimension(s) In Space) trades, 217-218 target redemption forwards, 217 Tavakoli, Janet, 177 taxes avoidance, 48-49 cuts, 348 Dubai International Financial Centre (DIFC), 82-83 favorable regimes, 41 leveraged buyouts (LBOs), 138 VAT (value added tax), 262 tchotchkes, 162 Teenage Cancer Trust, 262 Teledyn, 60 television, financial news, 91-99 Templars, 32 temporary suspension of deep-water drilling, 362 Terra Firma Capital Partners, 154, 157, 162, 165 terrorism, 44 Texas Instruments (TI), 122 Texas International, 146 Texas Pacific Group, 154 Textron, 60 Thain, John, 291, 319, 330 Thaler, Richard, 126 Thatcher, Margaret, 66, 81, 158 the Government National Mortgage Association (GNMA or Ginnie Mae), 179 theoretical profits, 231 theories, bubbles, 277-278 Theory of the Leisure Class, The, 41 This American Life, 185 Thompson, Todd, 93 Thoreau, Henry David, 359 Thornton, John, 76 Thorp, Edward, 121 thought leaders, 90 thundering herd, the, 66 TICKETs (tradable interest bearing convertible to equity trust securities), 160 Tierney, John, 98 Tiger Fund, 243 Time, 45, 129 Time Warner, 58 Tobias, Seth, 322 TOBs (tender option bonds), 222 toggle loans, 154 toilets, Japanese, 38 Tokyo as a financial center, 78 tools, six sigma, 60 Torii, Mayumi, 43 Toscanini, Arturo, 157 total return swap (TRS), 209 Tourre, Fabrice, 199 toxic currency structures, 218-219 toxic waste, 172 Toynbee, Arnold, 354 Toys R Us, 155 TPG, 156 trade protectionism, 334, 349 trading, 23-24 alleys, 92 banks, 73 proprietary, 352 securities, 66 stabilization of global trade, 349 traditional banking models, 68 tranches, 169 AAA, 203 equity, 192 innovation of, 178 super senior, 175 synthetic CDOs, 174 Z, 170, 178 transfers risk, central banks, 281-282 systems, money, 22 Transformers, 278 Travelers, merger of with Citicorp, 75 Treynor, Jack, 117 trickle-down economics, 42-43 Triffin dilemma, 31 Triffin, Robert, 31 Trollope, Anthony, 173 Troubled Asset Relief Program (TARP), 340 troy ounce bars, 25.


pages: 584 words: 187,436

More Money Than God: Hedge Funds and the Making of a New Elite by Sebastian Mallaby

Andrei Shleifer, Asian financial crisis, asset-backed security, automated trading system, bank run, barriers to entry, Benoit Mandelbrot, Berlin Wall, Bernie Madoff, Big bang: deregulation of the City of London, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, capital controls, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, computerized trading, corporate raider, Credit Default Swap, credit default swaps / collateralized debt obligations, crony capitalism, currency manipulation / currency intervention, currency peg, Elliott wave, Eugene Fama: efficient market hypothesis, failed state, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, full employment, German hyperinflation, High speed trading, index fund, John Meriwether, Kenneth Rogoff, Kickstarter, Long Term Capital Management, margin call, market bubble, market clearing, market fundamentalism, merger arbitrage, money market fund, moral hazard, Myron Scholes, natural language processing, Network effects, new economy, Nikolai Kondratiev, pattern recognition, Paul Samuelson, pre–internet, quantitative hedge fund, quantitative trading / quantitative finance, random walk, Renaissance Technologies, Richard Thaler, risk-adjusted returns, risk/return, Robert Mercer, rolodex, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, sovereign wealth fund, statistical arbitrage, statistical model, survivorship bias, technology bubble, The Great Moderation, The Myth of the Rational Market, the new new thing, too big to fail, transaction costs

Nevertheless, the tentative bottom line on hedge-fund performance is surprisingly positive. The best evidence comes in the form of a paper by Roger Ibbotson of the Yale School of Management, Peng Chen of Ibbotson Associates, and Kevin Zhu of the Hong Kong Polytechnic University.8 The authors start with performance statistics for 8,400 hedge funds between January 1995 and December 2009. Then they correct for “survivorship bias”: If you just measure the funds that exist at the end of the period, you exclude ones that blew up in the meantime—and so overestimate average performance. Next, the authors tackle “backfill bias”: Hedge funds tend to begin reporting results after a year of excellent profits, so including those atypical bonanzas makes hedge funds appear unduly brilliant. Having made these adjustments, the authors report that the average hedge fund returned 11.4 percent per year on average, or 7. 7 percent after fees—and, crucially, that the 7. 7 percent net return included 3 percentage points of alpha.

No hedge-fund database is perfect, since all rely on voluntary self-reporting. Hennessee turned out to have monthly results for half of the thirty-six “Tiger cub” funds run by managers who had worked for Robertson at some point before 2000. (Tiger cubs are separate from “Tiger seeds,” which are funds that have received capital from Robertson since 2000.) The Hennessee data included two funds that blew up, so it was not subject to the “survivorship bias” that bedevils hedge-fund performance statistics. And because Tiger cubs tend to invest in equities rather than in less liquid loans or derivatives that are not traded on an exchange, their results are likely to be adjusted to reflect price moves promptly and cleanly. Every up and down wiggle is captured, minimizing the “smoothing bias” that occurs when hedge funds mark their portfolios to market infrequently.


pages: 301 words: 78,638

Atomic Habits: An Easy & Proven Way to Build Good Habits & Break Bad Ones by James Clear

"side hustle", Atul Gawande, Cal Newport, Checklist Manifesto, choice architecture, clean water, cognitive dissonance, delayed gratification, deliberate practice, en.wikipedia.org, financial independence, invisible hand, Lao Tzu, late fees, meta analysis, meta-analysis, Paul Graham, randomized controlled trial, ride hailing / ride sharing, Sam Altman, Saturday Night Live, survivorship bias, Walter Mischel

Focus on your system instead. What do I mean by this? Are goals completely useless? Of course not. Goals are good for setting a direction, but systems are best for making progress. A handful of problems arise when you spend too much time thinking about your goals and not enough time designing your systems. Problem #1: Winners and losers have the same goals. Goal setting suffers from a serious case of survivorship bias. We concentrate on the people who end up winning—the survivors—and mistakenly assume that ambitious goals led to their success while overlooking all of the people who had the same objective but didn’t succeed. Every Olympian wants to win a gold medal. Every candidate wants to get the job. And if successful and unsuccessful people share the same goals, then the goal cannot be what differentiates the winners from the losers.


pages: 232 words: 71,965

Dead Companies Walking by Scott Fearon

bank run, Bernie Madoff, business cycle, corporate raider, creative destruction, crony capitalism, Donald Trump, Eugene Fama: efficient market hypothesis, fear of failure, Golden Gate Park, hiring and firing, housing crisis, index fund, Jeff Bezos, Joseph Schumpeter, late fees, McMansion, moral hazard, new economy, pets.com, Ponzi scheme, Ronald Reagan, short selling, Silicon Valley, Snapchat, South of Market, San Francisco, Steve Jobs, survivorship bias, Upton Sinclair, Vanguard fund, young professional

They then market the hell out of those newer funds by touting their astounding returns until those funds, too, get too big to keep posting great stats. Then, rinse and repeat—they start the whole cycle again. But what about the new funds that don’t do well? There are plenty of those. And companies have a sure-fire strategy for dealing with them: they shut them down and erase them from their books. It’s called survivorship bias, and it happens all the time. A fund goes south and starts to post poor results, so the bosses step in and—bingo, bango—it goes down the Wall Street rabbit hole, never to be heard from again. They either wipe it out entirely or they merge it into other, better-performing funds. Of course, the investors in that fund don’t get their money back or anything. Those losses aren’t imaginary for them.


pages: 290 words: 76,216

What's Wrong with Economics? by Robert Skidelsky

"Robert Solow", additive manufacturing, agricultural Revolution, Black Swan, Bretton Woods, business cycle, Cass Sunstein, central bank independence, cognitive bias, conceptual framework, Corn Laws, corporate social responsibility, correlation does not imply causation, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, disruptive innovation, Donald Trump, full employment, George Akerlof, George Santayana, global supply chain, global village, Gunnar Myrdal, happiness index / gross national happiness, hindsight bias, Hyman Minsky, income inequality, index fund, inflation targeting, information asymmetry, Internet Archive, invisible hand, John Maynard Keynes: Economic Possibilities for our Grandchildren, Joseph Schumpeter, Kenneth Arrow, knowledge economy, labour market flexibility, loss aversion, Mark Zuckerberg, market clearing, market friction, market fundamentalism, Martin Wolf, means of production, moral hazard, paradox of thrift, Pareto efficiency, Paul Samuelson, Philip Mirowski, precariat, price anchoring, principal–agent problem, rent-seeking, Richard Thaler, road to serfdom, Robert Shiller, Robert Shiller, Ronald Coase, shareholder value, Silicon Valley, Simon Kuznets, survivorship bias, technoutopianism, The Chicago School, The Market for Lemons, The Nature of the Firm, the scientific method, The Wealth of Nations by Adam Smith, Thomas Kuhn: the structure of scientific revolutions, Thomas Malthus, Thorstein Veblen, transaction costs, transfer pricing, Vilfredo Pareto, Washington Consensus, Wolfgang Streeck, zero-sum game

Thinking fast and slow Kahneman and Tversky claimed that we make choices according to two mental systems, the first intuitive, the second calculating, which they label fast and slow thinking. Slow thinking is logical; fast thinking is intuitive, and frequently irrational. They have found impressive evidence of ‘irrational’ choices – for example, investors’ preference for high-cost actively managed funds which underperform zero-cost index funds. Behavioural economists identify the following ‘systemic’ errors that people make. 1. Survivorship bias We tend to look only at what was successful. Think of a newspaper article that claims it can help you imitate Mark Zuckerberg’s morning routine. The obvious implication is that you too could become a billionaire if you just wore grey t-shirts and ate the right breakfast, but this ignores the multitudes of non-billionaires doing just that. 2. Loss aversion It is fairly well established that people hate losing something more than they love gaining it.


pages: 670 words: 194,502

The Intelligent Investor (Collins Business Essentials) by Benjamin Graham, Jason Zweig

3Com Palm IPO, accounting loophole / creative accounting, air freight, Andrei Shleifer, asset allocation, business cycle, buy and hold, buy low sell high, capital asset pricing model, corporate governance, corporate raider, Daniel Kahneman / Amos Tversky, diversified portfolio, dogs of the Dow, Eugene Fama: efficient market hypothesis, Everybody Ought to Be Rich, George Santayana, hiring and firing, index fund, intangible asset, Isaac Newton, Long Term Capital Management, market bubble, merger arbitrage, money market fund, new economy, passive investing, price stability, Ralph Waldo Emerson, Richard Thaler, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, shareholder value, sharing economy, short selling, Silicon Valley, South Sea Bubble, Steve Jobs, stocks for the long run, survivorship bias, the market place, the rule of 72, transaction costs, tulip mania, VA Linux, Vanguard fund, Y2K, Yogi Berra

The indexes used to represent the U.S. stock market’s earliest returns contain as few as seven (yes, 7!) stocks.1 By 1800, however, there were some 300 companies in America (many in the Jeffersonian equivalents of the Internet: wooden turnpikes and canals). Most went bankrupt, and their investors lost their knickers. But the stock indexes ignore all the companies that went bust in those early years, a problem technically known as “survivorship bias.” Thus these indexes wildly overstate the results earned by real-life investors—who lacked the 20/20 hindsight necessary to know exactly which seven stocks to buy. A lonely handful of companies, including Bank of New York and J. P. Morgan Chase, have prospered continuously since the 1790s. But for every such miraculous survivor, there were thousands of financial disasters like the Dismal Swamp Canal Co., the Pennsylvania Cultivation of Vines Co., and the Snickers’s Gap Turn-pike Co.

(For more on P/E ratios, see p. 168.) 1 If dividends are not included, stocks fell 47.8% in those two years. 1 By the 1840s, these indexes had widened to include a maximum of seven financial stocks and 27 railroad stocks—still an absurdly unrepresentative sample of the rambunctious young American stock market. 2 See Jason Zweig, “New Cause for Caution on Stocks,” Time, May 6, 2002, p. 71. As Graham hints on p. 65, even the stock indexes between 1871 and the 1920s suffer from survivorship bias, thanks to the hundreds of automobile, aviation, and radio companies that went bust without a trace. These returns, too, are probably overstated by one to two percentage points. 2 Those cheaper stock prices do not mean, of course, that investors’ expectation of a 7% stock return will be realized. 3 See Jeremy Siegel, Stocks for the Long Run (McGraw-Hill, 2002), p. 94, and Robert Arnott and William Bernstein, “The Two Percent Dilution,” working paper, July, 2002


pages: 306 words: 82,765

Skin in the Game: Hidden Asymmetries in Daily Life by Nassim Nicholas Taleb

availability heuristic, Benoit Mandelbrot, Bernie Madoff, Black Swan, Brownian motion, Capital in the Twenty-First Century by Thomas Piketty, Cass Sunstein, cellular automata, Claude Shannon: information theory, cognitive dissonance, complexity theory, David Graeber, disintermediation, Donald Trump, Edward Thorp, equity premium, financial independence, information asymmetry, invisible hand, knowledge economy, loss aversion, mandelbrot fractal, mental accounting, microbiome, moral hazard, Murray Gell-Mann, offshore financial centre, p-value, Paul Samuelson, Ponzi scheme, price mechanism, principal–agent problem, Ralph Nader, random walk, rent-seeking, Richard Feynman, Richard Thaler, Ronald Coase, Ronald Reagan, Rory Sutherland, Silicon Valley, Steven Pinker, stochastic process, survivorship bias, The Nature of the Firm, transaction costs, urban planning, Yogi Berra

Confusion arises because it may seem that the “one-off” risk is reasonable, but that also means that an additional one is reasonable. (See Figure 9). The good news is that some classes of risk can be deemed to be practically of probability zero: the earth survived trillions of natural variations daily over three billion years, otherwise we would not be here. We can use conditional probability arguments (adjusting for the survivorship bias) to back-out the ruin probability in a system. FIGURE 9. Why ruin is not a renewable resource. No matter how small the probability, in time, something bound to hit the ruin barrier is about guaranteed to hit it. No risk should be considered a “one-off” event. Now, we do not have to take nor is permanent sustainability necessary. We can just extend shelf time. The longer the t, the more the expectation operators diverge.


pages: 261 words: 86,905

How to Speak Money: What the Money People Say--And What It Really Means by John Lanchester

asset allocation, Basel III, Bernie Madoff, Big bang: deregulation of the City of London, bitcoin, Black Swan, blood diamonds, Bretton Woods, BRICs, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Celtic Tiger, central bank independence, collapse of Lehman Brothers, collective bargaining, commoditize, creative destruction, credit crunch, Credit Default Swap, crony capitalism, Dava Sobel, David Graeber, disintermediation, double entry bookkeeping, en.wikipedia.org, estate planning, financial innovation, Flash crash, forward guidance, Gini coefficient, global reserve currency, high net worth, High speed trading, hindsight bias, income inequality, inflation targeting, interest rate swap, Isaac Newton, Jaron Lanier, joint-stock company, joint-stock limited liability company, Kodak vs Instagram, liquidity trap, London Interbank Offered Rate, London Whale, loss aversion, margin call, McJob, means of production, microcredit, money: store of value / unit of account / medium of exchange, moral hazard, Myron Scholes, negative equity, neoliberal agenda, New Urbanism, Nick Leeson, Nikolai Kondratiev, Nixon shock, Northern Rock, offshore financial centre, oil shock, open economy, paradox of thrift, plutocrats, Plutocrats, Ponzi scheme, purchasing power parity, pushing on a string, quantitative easing, random walk, rent-seeking, reserve currency, Richard Feynman, Right to Buy, road to serfdom, Ronald Reagan, Satoshi Nakamoto, security theater, shareholder value, Silicon Valley, six sigma, Social Responsibility of Business Is to Increase Its Profits, South Sea Bubble, sovereign wealth fund, Steve Jobs, survivorship bias, The Chicago School, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, trickle-down economics, Washington Consensus, wealth creators, working poor, yield curve

Because these businesses are often well run and managed for the long term, make money for their shareholders, and have the track record to prove it. There’s some evidence that family-controlled businesses do better than purely public companies. The reason for that must surely be that they, if well run, have a longer-term focus and steadier nerve than companies chasing a good set of quarterly figures to keep their shareholders happy. There may also be a strong element of “survivorship bias” in the statistics, in that family firms that are less well run will be forced out of the market and/or be bought out by more efficient competitors; so the ones still in business are by definition the successful survivors. Abenomics The name given to the policies of the Japanese prime minister Shinzo Abe, who started his second term in office in 2012. There were supposed to be three “arrows”—the Japanese are keen on archery—to the policy, involving approach to fiscal policy, reforms to the labor market, and printing money like it’s about to go out of style, in an attempt to end deflation and start a beneficial level of inflation.


pages: 398 words: 86,855

Bad Data Handbook by Q. Ethan McCallum

Amazon Mechanical Turk, asset allocation, barriers to entry, Benoit Mandelbrot, business intelligence, cellular automata, chief data officer, Chuck Templeton: OpenTable:, cloud computing, cognitive dissonance, combinatorial explosion, commoditize, conceptual framework, database schema, DevOps, en.wikipedia.org, Firefox, Flash crash, Gini coefficient, illegal immigration, iterative process, labor-force participation, loose coupling, natural language processing, Netflix Prize, quantitative trading / quantitative finance, recommendation engine, selection bias, sentiment analysis, statistical model, supply-chain management, survivorship bias, text mining, too big to fail, web application

Finance, deal with this is to just provide the most recent symbol as the unique key. This means that whenever you reload fresh data, some of your identifiers will have changed. More importantly, any company that is no longer traded does not have a current symbol. In most current datasets, if you look up “S” you get Sprint; if you look up “Sears” you get SHLD; and if you look up Kmart, you get nothing. This causes a major “survivorship bias” in data: the stock market looks much more profitable if you never look at companies that have gone bankrupt or been bought out. There are universal unique identifiers with acronyms such as CUSIP, ISIN, and SEDOL. But these are proprietary and often not available. They also only solve half the problem: they do not get recycled the way ticker symbols do, but they will change over time as minor changes happen to a stock (e.g., the CUSIP changed when KMRT became SHLD).


pages: 335 words: 94,657

The Bogleheads' Guide to Investing by Taylor Larimore, Michael Leboeuf, Mel Lindauer

asset allocation, buy and hold, buy low sell high, corporate governance, correlation coefficient, Daniel Kahneman / Amos Tversky, diversification, diversified portfolio, Donald Trump, endowment effect, estate planning, financial independence, financial innovation, high net worth, index fund, late fees, Long Term Capital Management, loss aversion, Louis Bachelier, margin call, market bubble, mental accounting, money market fund, passive investing, Paul Samuelson, random walk, risk tolerance, risk/return, Sharpe ratio, statistical model, stocks for the long run, survivorship bias, the rule of 72, transaction costs, Vanguard fund, yield curve, zero-sum game

READ WHAT OTHERS SAY Most of the world's leading investment researchers, scholars, authors, and almost anyone who isn't trying to sell you their investment products, agree that low-cost, passive investing is an excellent strategy for most or all of your portfolio. Following are what many of them have to say on the subject of passive vs. active investing: Frank Armstrong, author of The Informed Investor: "Do the right thing: In every asset class where they are available, index!-Four of five funds will fail to meet or beat an appropriate index." Gregory A. Baer and Gary Gensler, authors of The Great Mutual Fund Trap: "With returns corrected for survivorship bias, the average actively managed funds trail the market by about 3 percentage points a year." William Bernstein, Ph.D., M.D., author of The Four Pillars of Investing, frequent guest columnist for Morningstar and often quoted in The Wall Street Journal: "An index fund dooms you to mediocrity? Absolutely not: It virtually guarantees you superior performance." Due to their simplicity, low cost and ease of manageability, investing in index funds is an excellent choice for nearly every investor.


pages: 346 words: 89,180

Capitalism Without Capital: The Rise of the Intangible Economy by Jonathan Haskel, Stian Westlake

"Robert Solow", 23andMe, activist fund / activist shareholder / activist investor, Airbnb, Albert Einstein, Andrei Shleifer, bank run, banking crisis, Bernie Sanders, business climate, business process, buy and hold, Capital in the Twenty-First Century by Thomas Piketty, cloud computing, cognitive bias, computer age, corporate governance, corporate raider, correlation does not imply causation, creative destruction, dark matter, Diane Coyle, Donald Trump, Douglas Engelbart, Douglas Engelbart, Edward Glaeser, Elon Musk, endogenous growth, Erik Brynjolfsson, everywhere but in the productivity statistics, Fellow of the Royal Society, financial innovation, full employment, fundamental attribution error, future of work, Gini coefficient, Hernando de Soto, hiring and firing, income inequality, index card, indoor plumbing, intangible asset, Internet of things, Jane Jacobs, Jaron Lanier, job automation, Kenneth Arrow, Kickstarter, knowledge economy, knowledge worker, laissez-faire capitalism, liquidity trap, low skilled workers, Marc Andreessen, Mother of all demos, Network effects, new economy, open economy, patent troll, paypal mafia, Peter Thiel, pets.com, place-making, post-industrial society, Productivity paradox, quantitative hedge fund, rent-seeking, revision control, Richard Florida, ride hailing / ride sharing, Robert Gordon, Ronald Coase, Sand Hill Road, Second Machine Age, secular stagnation, self-driving car, shareholder value, sharing economy, Silicon Valley, six sigma, Skype, software patent, sovereign wealth fund, spinning jenny, Steve Jobs, survivorship bias, The Rise and Fall of American Growth, The Wealth of Nations by Adam Smith, Tim Cook: Apple, total factor productivity, Tyler Cowen: Great Stagnation, urban planning, Vanguard fund, walkable city, X Prize, zero-sum game

More worrying is that when value-for-money procurement fails, innovation is often used as an excuse (“we lost money, but we were trying something new!”). This runs the risk that doing too much innovation procurement creates a cover for standard procurement failure. The final question for any government considering using innovation to foster procurement is Clint Eastwood’s: “Do you feel lucky?” It is very hard to know what the real odds of success in innovation procurement are partly because survivorship bias is great (How many failed attempts to use procurement to foster innovation do we simply not know about?), and partly because what made it work is so unclear (To what extent was fostering innovation in semiconductors or data communications good luck? How easy would it be to pick the next winner?). Training and Education. We might also foresee a growing public role in financing particular sorts of training and education.


Concentrated Investing by Allen C. Benello

activist fund / activist shareholder / activist investor, asset allocation, barriers to entry, beat the dealer, Benoit Mandelbrot, Bob Noyce, business cycle, buy and hold, carried interest, Claude Shannon: information theory, corporate governance, corporate raider, delta neutral, discounted cash flows, diversification, diversified portfolio, Edward Thorp, family office, fixed income, high net worth, index fund, John von Neumann, Louis Bachelier, margin call, merger arbitrage, Paul Samuelson, performance metric, random walk, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, shareholder value, Sharpe ratio, short selling, survivorship bias, technology bubble, transaction costs, zero-sum game

That base case index fund allows an investor to obtain a market return very cheaply, so unless an active manager can add value over and above that index, the investor is better off in the index fund. For active managers as a whole, investing is a zero sum game, less fees and transaction costs, so most active managers won’t do as well as the market because they are the market. Academic studies tend to flatter the active managers due to survivorship bias, which means that because the worst drop out, they aren’t counted. How, then, does a manager add value over the market? In Simpson’s opinion, a “closet indexer”—an investor who closely follows index components to achieve returns in line with the index without disclosing that they are doing so—and who varies from the index “a little bit here and there and everywhere” won’t outperform.159 A broadly diversified portfolio will likely underperform the market after taking out fees.


pages: 825 words: 228,141

MONEY Master the Game: 7 Simple Steps to Financial Freedom by Tony Robbins

3D printing, active measures, activist fund / activist shareholder / activist investor, addicted to oil, affirmative action, Affordable Care Act / Obamacare, Albert Einstein, asset allocation, backtesting, bitcoin, buy and hold, clean water, cloud computing, corporate governance, corporate raider, correlation does not imply causation, Credit Default Swap, Dean Kamen, declining real wages, diversification, diversified portfolio, Donald Trump, estate planning, fear of failure, fiat currency, financial independence, fixed income, forensic accounting, high net worth, index fund, Internet of things, invention of the wheel, Jeff Bezos, Kenneth Rogoff, lake wobegon effect, Lao Tzu, London Interbank Offered Rate, market bubble, money market fund, mortgage debt, new economy, obamacare, offshore financial centre, oil shock, optical character recognition, Own Your Own Home, passive investing, profit motive, Ralph Waldo Emerson, random walk, Ray Kurzweil, Richard Thaler, risk tolerance, riskless arbitrage, Robert Shiller, Robert Shiller, self-driving car, shareholder value, Silicon Valley, Skype, Snapchat, sovereign wealth fund, stem cell, Steve Jobs, survivorship bias, telerobotics, the rule of 72, thinkpad, transaction costs, Upton Sinclair, Vanguard fund, World Values Survey, X Prize, Yogi Berra, young professional, zero-sum game

I’ve read that from 1984 to 1998, only about 4% of funds [with over $100 million in assets under management (AUM)] beat the Vanguard 500. And that 4% isn’t the same every year—a more simple way of saying that is that 96% of all mutual funds fail to beat the market. DS: Those statistics are only the tip of the iceberg. The reality is even worse. When you look at past performance, you can only look at the funds in existence today. TR: Survivors. DS: Exactly. Those statistics suffer from survivorship bias. Over the last ten years, hundreds of mutual funds have gone out of business because they performed poorly. Of course, they don’t take the funds with great returns and merge them into funds with lousy returns. They take the funds with lousy returns and merge them into funds with great returns. TR: So the 96% isn’t accurate? DS: It’s worse. TR: Wow. DS: There’s another reason the investor’s reality is worse than the numbers you cite, and that’s because of our own behavioral mistakes we make as individual investors.

Rebates, 256 multitasking, 267–69 municipal bonds, 319–20 Munnell, Alicia, 139–40, 308, 427 Murdoch, Rupert, 83 mutual funds, 92–104 account fee, 115 actively managed, 93, 95, 100, 110, 124, 165, 479–80, 502 and annuities, 168, 424–25 associated costs of, 112 average returns, 116–18 bond, 158 cash drag, 115 cost calculator, 111 deferred sales charge, 115 dollar-weighted return on, 118–19 exchange fee, 115 expense ratio, 108, 113 failure to beat the market, 93–94, 96, 101, 106 fees of, 105–15, 119, 121, 141, 180, 273, 278, 479 and 401(k)s, 93, 110, 114, 139, 141, 144 high-cost, 85, 105, 112 index funds, 94 money market funds, 303 no load, 108 offer, 84 options, 163 pay to play, 144, 157 promise of protection in, 98 purchase fee, 115 ratings of, 92, 102–3 redemption fee, 115 retirement accounts in, 93, 110, 114, 141 returns on investment, 116–19, 400 sales charge (load), 115 soft-dollar costs, 114–15 as stacked deck, 88 stock-picking, 119, 180 and survivorship bias, 470 tax costs, 111, 114, 119, 279, 472 as $13 trillion lie, 93, 97 time-weighted returns, 118–19 transaction costs, 114 turnover in, 279–80 Namale Resort and Spa, 207, 341 nanotechnology, 562, 567 Napoléon III, 555 NASA, 557, 561 Nash, Ogden, 65 National Association of Personal Financial Advisors (NAPFA), 132 national debt, 149 natural resources, 556 Necker Island, 208 Nelson, Willie, 52, 61, 341 nest egg, 58, 257 Netflix, 465–66 Nixon, Richard M., 370–71 Nixon rally, 371–72 Norton, Michael, 589, 601 Notes from a Friend (Robbins), 597–98 numbers: off base, 240–41 “real,” 238, 364 Obama, Barack, 208, 560 Oduyoye, Darin, 499–500 O’Higgins, Michael, 398 oil, 506, 509, 510–11, 556–57 online rewards programs, 255–56 OPEC, 506, 511 opportunity, 269 optical illusions, 38–39 organ donation, 39–40 O’Rielly, William, 115 Orman, Suze, 254 Page, Larry, 377 Palm Beach, Florida, 289–90 passion, 573–87 passive investing (indexing), 97 paycheck, automatic deductions from, 64 pay to play, 144, 157 penny wisdom, 63 pensions, 34–35, 409 cash-balance plans, 155, 156 defined benefit plans, 155 do-it-yourself, 86 hidden fees in, 86 in Security/Peace of Mind Bucket, 308 Personal Fund, 111 Personal Power (Robbins), xxvi Peter, Irene, 297 philanthropy, 392, 457, 466, 486, 489, 494, 538, 595–96, 601; see also giving back photography, 269–70 physical mastery, 42 physiology, changing, 196–99 Pickens, T.


Capital Ideas Evolving by Peter L. Bernstein

Albert Einstein, algorithmic trading, Andrei Shleifer, asset allocation, business cycle, buy and hold, buy low sell high, capital asset pricing model, commodity trading advisor, computerized trading, creative destruction, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, diversification, diversified portfolio, endowment effect, equity premium, Eugene Fama: efficient market hypothesis, financial innovation, fixed income, high net worth, hiring and firing, index fund, invisible hand, Isaac Newton, John Meriwether, John von Neumann, Joseph Schumpeter, Kenneth Arrow, London Interbank Offered Rate, Long Term Capital Management, loss aversion, Louis Bachelier, market bubble, mental accounting, money market fund, Myron Scholes, paper trading, passive investing, Paul Samuelson, price anchoring, price stability, random walk, Richard Thaler, risk tolerance, risk-adjusted returns, risk/return, Robert Shiller, Robert Shiller, Sharpe ratio, short selling, Silicon Valley, South Sea Bubble, statistical model, survivorship bias, systematic trading, technology bubble, The Wealth of Nations by Adam Smith, transaction costs, yield curve, Yogi Berra, zero-sum game

First, calculating market-beating performance for hedge funds is not as simple as calculating it for a mutual fund or long-only active portfolio manager. There is no “market” in hedge funds as there is in stocks or bonds. What is it then that these funds are outperforming? They may outperform some arbitrary benchmark such as the Treasury bill return plus percentage points, but the result could be more the outcome of messy data and survivorship bias. bern_c02.qxd 24 3/23/07 8:53 AM THE Page 24 B E H AV I O R A L AT TAC K Second, calculating hedge fund risk is a controversial procedure. Volatility measures employed as risk measurements in conventional investing are not appropriate in a long/short environment. Among other things, hedge fund returns are subject to fat tails or tail risk—higherthan-normal probabilities of extreme negative returns.


pages: 398 words: 31,161

Gnuplot in Action: Understanding Data With Graphs by Philipp Janert

bioinformatics, business intelligence, Debian, general-purpose programming language, iterative process, mandelbrot fractal, pattern recognition, random walk, Richard Stallman, six sigma, survivorship bias

The inspiration to this story stems from the book Graphic Discovery by Howard Wainer, Princeton University Press (2005). 301 302 CHAPTER 15 Coda: Understanding data with graphs The not-so-obvious obvious answer is to add the armor in those areas where no bullet holes were found. Why? Because airplanes are subject to hits everywhere, but if the hits strike in the white areas in figure 15.1, the airplane doesn’t come back from its mission. (Statisticians speak of survivorship bias.) Therefore, those are the most vital areas of the machine and should receive the best possible protection. So, let this be our final lesson. Evidence, be it graphical or otherwise, is just that: mere data. But actual insight arises only through the correct interpretation of those facts. appendix A: Obtaining, building, and installing gnuplot The easiest way to install gnuplot on your local computer is to download and install a precompiled package.


pages: 505 words: 142,118

A Man for All Markets by Edward O. Thorp

3Com Palm IPO, Albert Einstein, asset allocation, beat the dealer, Bernie Madoff, Black Swan, Black-Scholes formula, Brownian motion, buy and hold, buy low sell high, carried interest, Chuck Templeton: OpenTable:, Claude Shannon: information theory, cognitive dissonance, collateralized debt obligation, Credit Default Swap, credit default swaps / collateralized debt obligations, diversification, Edward Thorp, Erdős number, Eugene Fama: efficient market hypothesis, financial innovation, George Santayana, German hyperinflation, Henri Poincaré, high net worth, High speed trading, index arbitrage, index fund, interest rate swap, invisible hand, Jarndyce and Jarndyce, Jeff Bezos, John Meriwether, John Nash: game theory, Kenneth Arrow, Livingstone, I presume, Long Term Capital Management, Louis Bachelier, margin call, Mason jar, merger arbitrage, Murray Gell-Mann, Myron Scholes, NetJets, Norbert Wiener, passive investing, Paul Erdős, Paul Samuelson, Pluto: dwarf planet, Ponzi scheme, price anchoring, publish or perish, quantitative trading / quantitative finance, race to the bottom, random walk, Renaissance Technologies, RFID, Richard Feynman, risk-adjusted returns, Robert Shiller, Robert Shiller, rolodex, Sharpe ratio, short selling, Silicon Valley, Stanford marshmallow experiment, statistical arbitrage, stem cell, stocks for the long run, survivorship bias, The Myth of the Rational Market, The Predators' Ball, the rule of 72, The Wisdom of Crowds, too big to fail, Upton Sinclair, value at risk, Vanguard fund, Vilfredo Pareto, Works Progress Administration

billion in 2007 New York Times, March 25, 2009, page B1. Management fees Incentive fees in 2015 averaged 17.7 percent of any profit, compared to 19.3 percent in 2008, according to The Wall Street Journal, September 10, 2015. Management fees had declined to an average of 1.54 percent. hedge fund returns The studies encountered difficulties obtaining clean long-term data and in correcting for survivorship bias: funds that died early and may not be in the database are expected to have performed more poorly. Omitting them and studying only the survivors overstates the results. Later analyses Dichev, Ilia D. and Yu, Gwen, “Higher risk, lower returns: What hedge fund investors really earn,” Journal of Financial Economics, 100 (2011) 248–63; Lack, Simon, The Hedge Fund Mirage, Wiley, New York, 2012.


pages: 892 words: 91,000

Valuation: Measuring and Managing the Value of Companies by Tim Koller, McKinsey, Company Inc., Marc Goedhart, David Wessels, Barbara Schwimmer, Franziska Manoury

activist fund / activist shareholder / activist investor, air freight, barriers to entry, Basel III, BRICs, business climate, business cycle, business process, capital asset pricing model, capital controls, Chuck Templeton: OpenTable:, cloud computing, commoditize, compound rate of return, conceptual framework, corporate governance, corporate social responsibility, creative destruction, credit crunch, Credit Default Swap, discounted cash flows, distributed generation, diversified portfolio, energy security, equity premium, fixed income, index fund, intangible asset, iterative process, Long Term Capital Management, market bubble, market friction, Myron Scholes, negative equity, new economy, p-value, performance metric, Ponzi scheme, price anchoring, purchasing power parity, quantitative easing, risk/return, Robert Shiller, Robert Shiller, shareholder value, six sigma, sovereign wealth fund, speech recognition, stocks for the long run, survivorship bias, technology bubble, time value of money, too big to fail, transaction costs, transfer pricing, value at risk, yield curve, zero-coupon bond

Even with the best statistical techniques, however, this number is probably too high, because the observable sample includes only countries with strong historical returns.5 Statisticians refer to this phenomenon as survivorship bias. Zvi Bodie writes, “There were 36 active stock markets in 1900, so why do we only look at two, [the UK and 3 E. Dimson, P. Marsh, and M. Staunton, “The Worldwide Equity Premium: A Smaller Puzzle,” in Hand- book of Investments: Equity Risk Premium, ed. R. Mehra (Amsterdam: Elsevier Science, 2007). 4 D. C. Indro and W. Y. Lee, “Biases in Arithmetic and Geometric Averages as Estimates of Long-Run Expected Returns and Risk Premia,” Financial Management 26, no. 4 (Winter 1997): 81–90; and M. E. Blume, “Unbiased Estimators of Long-Run Expected Rates of Return,” Journal of the American Statistical Association 69, no. 347 (September 1974): 634–638. 5 S. Brown, W. Goetzmann, and S. Ross, “Survivorship Bias,” Journal of Finance (July 1995): 853–873. 288 ESTIMATING THE COST OF CAPITAL U.S. markets]?


pages: 526 words: 160,601

A Generation of Sociopaths: How the Baby Boomers Betrayed America by Bruce Cannon Gibney

1960s counterculture, 2013 Report for America's Infrastructure - American Society of Civil Engineers - 19 March 2013, affirmative action, Affordable Care Act / Obamacare, American Society of Civil Engineers: Report Card, Bernie Madoff, Bernie Sanders, Bretton Woods, business cycle, buy and hold, carbon footprint, Charles Lindbergh, cognitive dissonance, collapse of Lehman Brothers, collateralized debt obligation, corporate personhood, Corrections Corporation of America, currency manipulation / currency intervention, Daniel Kahneman / Amos Tversky, dark matter, Deng Xiaoping, Donald Trump, Downton Abbey, Edward Snowden, Elon Musk, ending welfare as we know it, equal pay for equal work, failed state, financial deregulation, Francis Fukuyama: the end of history, future of work, gender pay gap, gig economy, Haight Ashbury, Home mortgage interest deduction, Hyperloop, illegal immigration, impulse control, income inequality, Intergovernmental Panel on Climate Change (IPCC), invisible hand, Jane Jacobs, Kitchen Debate, labor-force participation, Long Term Capital Management, Lyft, Mark Zuckerberg, market bubble, mass immigration, mass incarceration, McMansion, medical bankruptcy, Menlo Park, Mont Pelerin Society, moral hazard, mortgage debt, mortgage tax deduction, neoliberal agenda, Network effects, obamacare, offshore financial centre, oil shock, operation paperclip, plutocrats, Plutocrats, Ponzi scheme, price stability, quantitative easing, Ralph Waldo Emerson, RAND corporation, rent control, ride hailing / ride sharing, risk tolerance, Robert Shiller, Robert Shiller, Ronald Reagan, Rubik’s Cube, school choice, secular stagnation, self-driving car, shareholder value, short selling, side project, Silicon Valley, smart grid, Snapchat, source of truth, stem cell, Steve Jobs, Stewart Brand, survivorship bias, TaskRabbit, The Wealth of Nations by Adam Smith, Tim Cook: Apple, too big to fail, War on Poverty, white picket fence, Whole Earth Catalog, women in the workforce, Y2K, Yom Kippur War, zero-sum game

This cyclically adjusted ratio is not without its controversies, but is the best data set offering perspective over the long, long term. The raw S&P 500 P/E ratio was 11.7 in Q4 1988, 18.1 in Q4 1995, 30.5 in Q4 1999, and was around 23–25 in the first half of 2016. The story, in other words, is the same. The case could be made that things are somewhat better or somewhat worse—somewhat better because models digest interest rates, and interest rates are low, “justifying” higher valuations; somewhat worse, because of survivorship bias, the rise of ultra-high P/E ratios in private equity (if there’s any “E” at all), etc. My view is that things are probably somewhat worse. 33. Holland, A. Steven. “Real Interest Rates: What Accounts for Their Rise?” Federal Reserve Bank of St. Louis, Dec. 1984, pp. 1–3 (online pagination), research.stlouisfed.org/publications/review/84/12/Rates_Dec1984.pdf. Note that Holland was measuring Treasury rates, not consumer rates, but the one drove the other; e.g., FRED MORTG vs.


pages: 733 words: 179,391

Adaptive Markets: Financial Evolution at the Speed of Thought by Andrew W. Lo

"Robert Solow", Albert Einstein, Alfred Russel Wallace, algorithmic trading, Andrei Shleifer, Arthur Eddington, Asian financial crisis, asset allocation, asset-backed security, backtesting, bank run, barriers to entry, Berlin Wall, Bernie Madoff, bitcoin, Bonfire of the Vanities, bonus culture, break the buck, Brownian motion, business cycle, business process, butterfly effect, buy and hold, capital asset pricing model, Captain Sullenberger Hudson, Carmen Reinhart, collapse of Lehman Brothers, collateralized debt obligation, commoditize, computerized trading, corporate governance, creative destruction, Credit Default Swap, credit default swaps / collateralized debt obligations, cryptocurrency, Daniel Kahneman / Amos Tversky, delayed gratification, Diane Coyle, diversification, diversified portfolio, double helix, easy for humans, difficult for computers, Ernest Rutherford, Eugene Fama: efficient market hypothesis, experimental economics, experimental subject, Fall of the Berlin Wall, financial deregulation, financial innovation, financial intermediation, fixed income, Flash crash, Fractional reserve banking, framing effect, Gordon Gekko, greed is good, Hans Rosling, Henri Poincaré, high net worth, housing crisis, incomplete markets, index fund, interest rate derivative, invention of the telegraph, Isaac Newton, James Watt: steam engine, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, John Meriwether, Joseph Schumpeter, Kenneth Rogoff, London Interbank Offered Rate, Long Term Capital Management, longitudinal study, loss aversion, Louis Pasteur, mandelbrot fractal, margin call, Mark Zuckerberg, market fundamentalism, martingale, merger arbitrage, meta analysis, meta-analysis, Milgram experiment, money market fund, moral hazard, Myron Scholes, Nick Leeson, old-boy network, out of africa, p-value, paper trading, passive investing, Paul Lévy, Paul Samuelson, Ponzi scheme, predatory finance, prediction markets, price discovery process, profit maximization, profit motive, quantitative hedge fund, quantitative trading / quantitative finance, RAND corporation, random walk, randomized controlled trial, Renaissance Technologies, Richard Feynman, Richard Feynman: Challenger O-ring, risk tolerance, Robert Shiller, Robert Shiller, Sam Peltzman, Shai Danziger, short selling, sovereign wealth fund, Stanford marshmallow experiment, Stanford prison experiment, statistical arbitrage, Steven Pinker, stochastic process, stocks for the long run, survivorship bias, Thales and the olive presses, The Great Moderation, the scientific method, The Wealth of Nations by Adam Smith, The Wisdom of Crowds, theory of mind, Thomas Malthus, Thorstein Veblen, Tobin tax, too big to fail, transaction costs, Triangle Shirtwaist Factory, ultimatum game, Upton Sinclair, US Airways Flight 1549, Walter Mischel, Watson beat the top human players on Jeopardy!, WikiLeaks, Yogi Berra, zero-sum game

And we would reply, ‘We don’t want to know what your system is, but if you’d like some feedback, show us what the simulated returns are by month.’ After looking at them, we would sometimes be able to say something like, ‘Your strategy was probably some variant of the following, and this is the financial database you probably used; your simulated profits in this month, this month, and this month are attributable to errors in that database, and your overall returns are artificially high because of the following type of survivorship bias,’ and so on.” You might be skeptical that a single private firm, no matter how talented its research staff, could advance so far ahead of the rest of the world. However, there’s a compelling analogy—not from the world of biology, but from the world of cryptography. In the early 1970s, a team at IBM created the Data Encryption Standard (DES) algorithm to protect sensitive government data. The DES algorithm included a mysterious component called the “S-box,” which many people suspected was a backdoor for government cryptographers to read encrypted data more easily.


pages: 701 words: 199,010

The Crisis of Crowding: Quant Copycats, Ugly Models, and the New Crash Normal by Ludwig B. Chincarini

affirmative action, asset-backed security, automated trading system, bank run, banking crisis, Basel III, Bernie Madoff, Black-Scholes formula, business cycle, buttonwood tree, Carmen Reinhart, central bank independence, collapse of Lehman Brothers, collateralized debt obligation, collective bargaining, corporate governance, correlation coefficient, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, discounted cash flows, diversification, diversified portfolio, family office, financial innovation, financial intermediation, fixed income, Flash crash, full employment, Gini coefficient, high net worth, hindsight bias, housing crisis, implied volatility, income inequality, interest rate derivative, interest rate swap, John Meriwether, Kickstarter, liquidity trap, London Interbank Offered Rate, Long Term Capital Management, low skilled workers, margin call, market design, market fundamentalism, merger arbitrage, Mexican peso crisis / tequila crisis, Mitch Kapor, money market fund, moral hazard, mortgage debt, Myron Scholes, negative equity, Northern Rock, Occupy movement, oil shock, price stability, quantitative easing, quantitative hedge fund, quantitative trading / quantitative finance, Ralph Waldo Emerson, regulatory arbitrage, Renaissance Technologies, risk tolerance, risk-adjusted returns, Robert Shiller, Robert Shiller, Ronald Reagan, Sam Peltzman, Sharpe ratio, short selling, sovereign wealth fund, speech recognition, statistical arbitrage, statistical model, survivorship bias, systematic trading, The Great Moderation, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond

Copycat funds are discussed in more detail later in this chapter. 7. The Sortino is a risk-adjusted performance measure that adjusts the standard deviation for nonnormal returns. 8. The HFR Relative Value hedge fund index is equal weighted, not asset weighted, and may not be entirely investable. It has the benefit of diversification across many hedge funds. Some academics think this database has more survivorship bias than do other databases. 9. Statement made on April 30, 2008, while giving a guest lecture in Claremont, California. 10. Five is nearly the square root of 24, multiplied by the biweekly standard deviation to obtain the annualized standard deviation. A few years after PGAM was launched, it changed this to the expected 0.5% tail gain or loss using a bootstrap. To calculate the bootstrap, take perhaps 10 years of data and write a program that draws random returns from that historical sample.


pages: 827 words: 239,762

The Golden Passport: Harvard Business School, the Limits of Capitalism, and the Moral Failure of the MBA Elite by Duff McDonald

activist fund / activist shareholder / activist investor, Affordable Care Act / Obamacare, Albert Einstein, barriers to entry, Bayesian statistics, Bernie Madoff, Bob Noyce, Bonfire of the Vanities, business cycle, business process, butterfly effect, capital asset pricing model, Capital in the Twenty-First Century by Thomas Piketty, Clayton Christensen, cloud computing, collateralized debt obligation, collective bargaining, commoditize, corporate governance, corporate raider, corporate social responsibility, creative destruction, deskilling, discounted cash flows, disintermediation, disruptive innovation, Donald Trump, family office, financial innovation, Frederick Winslow Taylor, full employment, George Gilder, glass ceiling, global pandemic, Gordon Gekko, hiring and firing, income inequality, invisible hand, Jeff Bezos, job-hopping, John von Neumann, Joseph Schumpeter, Kenneth Arrow, Kickstarter, London Whale, Long Term Capital Management, market fundamentalism, Menlo Park, new economy, obamacare, oil shock, pattern recognition, performance metric, Peter Thiel, plutocrats, Plutocrats, profit maximization, profit motive, pushing on a string, Ralph Nader, Ralph Waldo Emerson, RAND corporation, random walk, rent-seeking, Ronald Coase, Ronald Reagan, Sam Altman, Sand Hill Road, Saturday Night Live, shareholder value, Silicon Valley, Skype, Social Responsibility of Business Is to Increase Its Profits, Steve Jobs, survivorship bias, The Nature of the Firm, the scientific method, Thorstein Veblen, union organizing, urban renewal, Vilfredo Pareto, War on Poverty, William Shockley: the traitorous eight, women in the workforce, Y Combinator

At the same time, a list of the unsuccessful ones would probably be as long or longer. That’s the thing about HBS—tens of thousands of people have graduated from the School over the years, and while they naturally choose to emphasize the success stories while downplaying the less than successful ones, the image of the School as a preeminent training ground for the supersuccessful is unquestionably the result of a kind of survivorship bias. If your company—or your career—has stalled out, they simply stop talking about it. Consider the winner of the Student Business portion of the School’s 2014 New Venture Competition. The victor, an online butler called Alfred, could serve as a Saturday Night Live parody of a startup. Unlike the engineering-driven ideas you’re likely to see coming out of the likes of Stanford or MIT, Alfred is simply high-concept marketing, a virtual butler for people too busy to make their own bed.


pages: 321

Finding Alphas: A Quantitative Approach to Building Trading Strategies by Igor Tulchinsky

algorithmic trading, asset allocation, automated trading system, backtesting, barriers to entry, business cycle, buy and hold, capital asset pricing model, constrained optimization, corporate governance, correlation coefficient, credit crunch, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, Eugene Fama: efficient market hypothesis, financial intermediation, Flash crash, implied volatility, index arbitrage, index fund, intangible asset, iterative process, Long Term Capital Management, loss aversion, market design, market microstructure, merger arbitrage, natural language processing, passive investing, pattern recognition, performance metric, popular capitalism, prediction markets, price discovery process, profit motive, quantitative trading / quantitative finance, random walk, Renaissance Technologies, risk tolerance, risk-adjusted returns, risk/return, selection bias, sentiment analysis, shareholder value, Sharpe ratio, short selling, Silicon Valley, speech recognition, statistical arbitrage, statistical model, stochastic process, survivorship bias, systematic trading, text mining, transaction costs, Vanguard fund, yield curve

We also need to ensure that the data can support alpha production – that the data will be generated in the future following a reliable schedule. Sometimes we learn that the data producer will cease to generate the data. In this case, the data is not usable for an alpha because there is no way to get more data in real time. Another possible problem is survival bias. Even if a data vendor provides an alpha model that performs well when it is tested, this does not mean the model will perform well in the future. That is because we do not know how many models the vendor developed before this single model was selected. If the vendor tried 1,000 models and only one survived, we may face survival bias. The bias is introduced by the vendor and out of our control. In this case, some out-of-sample testing period for the dataset might be useful. Out-of-sample testing is helpful because it is not conducted in a controlled universe and strong performance is a good indicator of an alpha’s robustness.

The large number of combinations also means that it is impossible to inspect each of the resulting formulas by hand. Even if one wants to investigate a sample manually, the alpha expression can be very complicated and without obvious financial significance. Moreover, the sheer number of trials means that it is common for combinations that make no mathematical and economic sense to be erroneously recognized as alphas through survival bias. A good search process should reject such noise from the output or – better – should prevent it from happening in the first place. Last, the impossibility of inspecting every single alpha reduces the researcher’s confidence in each alpha compared with his confidence in alphas made by hand. Therefore, new kinds of testing are required for an automated search to maintain the quality of the alphas.


pages: 436 words: 98,538

The Upside of Inequality by Edward Conard

affirmative action, Affordable Care Act / Obamacare, agricultural Revolution, Albert Einstein, assortative mating, bank run, Berlin Wall, business cycle, Capital in the Twenty-First Century by Thomas Piketty, Carmen Reinhart, Climatic Research Unit, cloud computing, corporate governance, creative destruction, Credit Default Swap, crony capitalism, disruptive innovation, diversified portfolio, Donald Trump, en.wikipedia.org, Erik Brynjolfsson, Fall of the Berlin Wall, full employment, future of work, Gini coefficient, illegal immigration, immigration reform, income inequality, informal economy, information asymmetry, intangible asset, Intergovernmental Panel on Climate Change (IPCC), invention of the telephone, invisible hand, Isaac Newton, Jeff Bezos, Joseph Schumpeter, Kenneth Rogoff, Kodak vs Instagram, labor-force participation, liquidity trap, longitudinal study, low skilled workers, manufacturing employment, Mark Zuckerberg, Martin Wolf, mass immigration, means of production, meta analysis, meta-analysis, new economy, offshore financial centre, paradox of thrift, Paul Samuelson, pushing on a string, quantitative easing, randomized controlled trial, risk-adjusted returns, Robert Gordon, Ronald Reagan, Second Machine Age, secular stagnation, selection bias, Silicon Valley, Simon Kuznets, Snapchat, Steve Jobs, survivorship bias, The Rise and Fall of American Growth, total factor productivity, twin studies, Tyler Cowen: Great Stagnation, University of East Anglia, upwardly mobile, War on Poverty, winner-take-all economy, women in the workforce, working poor, working-age population, zero-sum game

Meanwhile, fifteen companies with combined market capitalization less than $10 billion in 2000 are now worth over $2 trillion today.37 If the past is any guide to the future, the sustainability of competitive advantages derived from patents and other means appear to be risky and short lived indeed. Even if the winners’ advantages prove to be sustainable, it is hardly obvious that the return on investment before the fact—the relevant measure of profitability—is truly high. Numerous Internet investments are hit-driven businesses where only a handful of start-ups succeed from an enormous sea of failures. Survival bias washes away the true cost of investment that creates success—the cost of the many failures needed to produce a handful of fortunate successes. A study by the Kauffman Foundation, for example, found, “[Venture capital] returns haven’t significantly outperformed the public market since the late 1990s . . . despite occasional high-profile successes.”38 And those were the start-ups funded by large high-profile venture funds—the funds with access to the most promising investments.

For a variety of reasons, the apparent increase in corporate profits seems unlikely to stem from a permanent decline in competitiveness due to a rise in oligopolistic pricing power, monopsony, or asymmetrical information. In fact, there is little reason to believe competitiveness has permanently declined but for rising profitability. The long-term rise in profitability seems confined to the IT-related sector, where survival bias hides the true return on investment and disruptive turmoil, near-zero incremental costs, and free products indicate fierce competition and anything but business as usual. Moreover, the long-term rise in profitability is likely overstated by a temporary rise in profitability in the aftermath of the financial crisis. Misdiagnosis of these trends can lead to solutions that do more harm than good.

See status socioeconomic segregation, 157, 167–68 Solon, Gary, 178 solutions, 243–66 balanced trade and strengthening bank guarantees, 254–59 lowering marginal corporate tax rate, 249–54 middle-class tax cut slowing growth, 259–64 ultra-high-skilled immigration, 244–49 South Korea incentives, 67 test scores, 219 Spain government investment, 147 productivity growth, 23 Sparber, Chad, 237 status, 69–70, 71, 80–81, 82 keep up with the Joneses, 168–69 loss driving irrational risk-taking, 32–34 STEM majors, 245–46 Stiglitz, Joseph, 31, 83n, 92–93, 95–96, 186 stock buy backs, 53, 251–52 student loan debt, 173–74 subprime-mortgage lending, 1, 49, 53–54, 132–34, 136, 168–69, 256, 258 success-is-unearned myth, 87–113 business profitability and competition, 95–106 CEO pay as motivation for risk-taking, 92–95 efforts by investors to influence economic policy accelerate growth, 106–8 income redistribution and, 87–88, 96–98, 103, 113 raising wages by fiat, 108–13 top 0.1 percent earning of pay, 88–92 Sullivan, James, 46–47, 165 Summers, Larry, 49, 95–98, 106, 115–16, 118–19, 142, 153, 156, 172, 186, 233, 250n Sumner, Scott, 200 supervisory capacity, 210–13, 244 supply and demand, 49, 59, 117–20 survival bias, 101, 106 Svejnar, Jan, 83 Swift, Taylor, 16 talent, 14–15, 91, 195–96, 199–200, 215 properly trained, 13, 18, 38, 39, 43, 50–51, 59, 81, 91–92, 110, 140–41, 186, 215, 236, 240, 254 shortage of, 13, 51–52, 87, 91–92 tax credits, 107, 107 tax inversions, 251, 253 tax policy, 2, 3, 72–73, 77, 198 lowering marginal corporate tax rate, 249–54 middle-class tax cut slowing growth, 259–64 tax rates, 3, 14, 72–73, 106–7 tax repatriation, 251–52 teacher quality, 218, 226–28, 240 teachers’ unions, 217, 227–28 teacher tenure, 227–28 technology, 29–30, 100 benefiting most productive workers, 17–18 reducing need for capital, 12, 13, 18–19 value of manufacturing companies vs., 128, 128–29 technology-hollows-out-the-middle-class myth, 155–75 accessibility of college credentials, 169–74 income distribution, 157–66 marriage value and growing success of women, 166–69 test scores, 11, 217–18, 239–41 charter schools, 223–24 international vs.


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The Telomere Effect: A Revolutionary Approach to Living Younger, Healthier, Longer by Dr. Elizabeth Blackburn, Dr. Elissa Epel

Albert Einstein, epigenetics, impulse control, income inequality, longitudinal study, Mark Zuckerberg, megacity, meta analysis, meta-analysis, mouse model, phenotype, Ralph Waldo Emerson, randomized controlled trial, selective serotonin reuptake inhibitor (SSRI), stem cell, survivorship bias, The Spirit Level, twin studies

This trend is probably not true lengthening happening; it just looks that way because the folks with shorter telomeres have passed away by this age (which is called survival bias—in any aging study, the oldest people are the healthy survivors). It’s the people with longer telomeres who are living into their eighties and nineties. Figure 9: Telomeres Shorten with Age. Telomere length declines with age, on average. It declines fastest during early childhood and then has a slower average rate of decline with age. Interestingly, many studies find telomere length is not shorter in those who live to be a lot older than seventy years. This is thought to be due to “survival bias,” meaning that those still alive at this age tended to have been those people with longer telomeres. Their telomeres probably had been longer all along, starting from birth.


pages: 202 words: 58,823

Willful: How We Choose What We Do by Richard Robb

activist fund / activist shareholder / activist investor, Alvin Roth, Asian financial crisis, asset-backed security, Bernie Madoff, capital asset pricing model, cognitive bias, collapse of Lehman Brothers, Daniel Kahneman / Amos Tversky, David Ricardo: comparative advantage, delayed gratification, diversification, diversified portfolio, effective altruism, endowment effect, Eratosthenes, experimental subject, family office, George Akerlof, index fund, information asymmetry, job satisfaction, John Maynard Keynes: Economic Possibilities for our Grandchildren, lake wobegon effect, loss aversion, market bubble, market clearing, money market fund, Pareto efficiency, Paul Samuelson, Peter Singer: altruism, principal–agent problem, profit maximization, profit motive, Richard Thaler, Silicon Valley, sovereign wealth fund, survivorship bias, the scientific method, The Wealth of Nations by Adam Smith, Thomas Malthus, Thorstein Veblen, transaction costs, ultimatum game

If their winnings dropped below a threshold, they became bankrupt, and their payoff would be zero. The subjects ended up selecting strategies that kept them alive in the game but lowered their expected payout. The author interprets this as evidence that a “deeply ingrained (and usually reliable) heuristic towards survival leads subjects to associate survival with optimality.” On this basis, he speculates that real-world managers conduct business too conservatively if they suffer from “survival bias.”15 Maybe. But I can imagine myself behaving like the experimental subjects, particularly since they couldn’t leave the lab early and the amount of money at stake was only a few dollars. Watching after I’d been eliminated would be boring, so I’d forgo a payout to stay in the game. In Life of Alexander, Plutarch describes Alexander the Great grappling with this conundrum: Whenever he heard Philip [II of Macedon] had taken any town of importance, or won any signal victory, instead of rejoicing at it altogether, he would tell his companions that his father would anticipate everything, and leave him and them no opportunities of performing great and illustrious actions.


pages: 247 words: 81,135

The Great Fragmentation: And Why the Future of All Business Is Small by Steve Sammartino

3D printing, additive manufacturing, Airbnb, augmented reality, barriers to entry, Bill Gates: Altair 8800, bitcoin, BRICs, Buckminster Fuller, citizen journalism, collaborative consumption, cryptocurrency, David Heinemeier Hansson, disruptive innovation, Elon Musk, fiat currency, Frederick Winslow Taylor, game design, Google X / Alphabet X, haute couture, helicopter parent, illegal immigration, index fund, Jeff Bezos, jimmy wales, Kickstarter, knowledge economy, Law of Accelerating Returns, lifelogging, market design, Metcalfe's law, Minecraft, minimum viable product, Network effects, new economy, peer-to-peer, post scarcity, prediction markets, pre–internet, profit motive, race to the bottom, random walk, Ray Kurzweil, recommendation engine, remote working, RFID, Rubik’s Cube, self-driving car, sharing economy, side project, Silicon Valley, Silicon Valley startup, skunkworks, Skype, social graph, social web, software is eating the world, Steve Jobs, survivorship bias, too big to fail, US Airways Flight 1549, web application, zero-sum game

With each iteration of human communication tools there’s resistance, just as there’s resistance to any emerging and scary technology. But when the usefulness is greater than the fear, its eventual uptake is inevitable. Regardless of what caused the evolution of language (there are currently a number of competing theories of how language arrived), it’s clear that those who mastered its use built themselves an evolutionary survival bias. The ability to master language has been the ‘killer app’ when it comes to hunting, farming, defending and all forms of civilisation development. Even today the mastery of a language — whether it’s one of a populous, the language of an industry or a particular computer code — usually comes with social and economic benefits. There’s a reason why autocratic nation states have historically restricted education, discussion and free speech: they stifle human activity and restrict change.


pages: 687 words: 189,243

A Culture of Growth: The Origins of the Modern Economy by Joel Mokyr

"Robert Solow", Andrei Shleifer, barriers to entry, Berlin Wall, business cycle, clockwork universe, cognitive dissonance, Copley Medal, creative destruction, David Ricardo: comparative advantage, delayed gratification, deliberate practice, Deng Xiaoping, Edmond Halley, epigenetics, Fellow of the Royal Society, financial independence, framing effect, germ theory of disease, Haber-Bosch Process, hindsight bias, income inequality, information asymmetry, invention of movable type, invention of the printing press, invisible hand, Isaac Newton, Jacquard loom, Jacques de Vaucanson, James Watt: steam engine, Johannes Kepler, John Harrison: Longitude, Joseph Schumpeter, knowledge economy, labor-force participation, land tenure, law of one price, Menlo Park, moveable type in China, new economy, phenotype, price stability, principal–agent problem, rent-seeking, Republic of Letters, Ronald Reagan, South Sea Bubble, statistical model, survivorship bias, the market place, The Structural Transformation of the Public Sphere, The Wealth of Nations by Adam Smith, transaction costs, ultimatum game, World Values Survey, Wunderkammern

In other words, it stands to reason that potential cultural entrepreneurs emerged in other societies as well, but for one reason or another did not succeed in bringing about a radical shift in the prevailing culture of the groups that mattered for sustained technological change. In fact, as we shall see in chapters 16 and 17, this was indeed the case in China. To understand why they were more successful in Europe, we need to identify those elements in the Occidental environment that facilitated this success. Cultural entrepreneurs, no less than business entrepreneurs, fail more often than they succeed, but survival bias tends to focus attention on the successful ones. For every Luther and Calvin there were many failed religious innovators, about whom we rarely know much. The most famous, to be sure, was the Bohemian reformer Jan Hus, who was executed in 1415 and his movement suppressed. Other failed cultural innovators included Miguel Servetus (executed in Geneva in 1553) and Jan of Leyden (executed in Munster in 1536).23 To see what set Europe apart, it is useful to ask about the circumstances under which the cultural entrepreneurs of the era operated.