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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
CHAPTER 9 CHAPTER 9 Measuring the Long Volatility Strategies of Managed Futures Mark Anson and Ho Ho ertain hedge fund strategies create investment positions that resemble a long put option. Specifically, managed futures or commodity trading advisors have significant exposure to volatility events. This exposure is positively related to volatility much like a long option position. We identify and measure this long volatility exposure, which may not always be transparent from the trading positions of a commodity trading advisor. We also examine ways to apply these long volatility strategies to improve risk management. C INTRODUCTION The managed futures industry has come full circle in its application over the last 15 years. In the early 1990s, global macro funds were the predominant form of the hedge fund industry. These funds were primarily managed futures funds run by commodity trading advisors (CTAs). As the 1990s progressed, other types of hedge fund strategies came to the forefront, such as relative value arbitrage, event driven, merger arbitrage, and equity long/short.
Fung and Hsieh (1997a) found that trend-following styles have a return profile similar to a long option straddle position—a long volatility position. Fung and Hsieh (1997b) documented that commodity trading advisors apply predominantly trend-following strategies. Measuring the Long Volatility Strategies of Managed Futures 185 In our research we use three Barclay Commodity Trading Advisor indices to capture the trading dynamics of the CTA market: Commodity Trading Index, Diversified Commodity Trading Advisor Index, and Systematic Trading Index. These indices are an equally weighted average of a group of CTAs who identify themselves as belonging to one of the three strategies. There are alternative ways to gain exposure to the futures markets without the use of a CTA. One way is a passive managed futures index, such as the Mount Lucas Management Index (MLMI). The MLMI applies a mechanical trading rule for following the price trends in several futures markets.
For general information on our other products and services, or technical support, please contact our Customer Care Department within the United States at 800-762-2974, outside the United States at 317-572-3993 or fax 317-572-4002. Wiley also publishes its books in a variety of electronic formats. Some content that appears in print may not be available in electronic books. For more information about Wiley products, visit our web site at www.wiley.com. Library of Congress Cataloging-in-Publication Data Commodity trading advisors : risk, performance analysis, and selection / [edited by] Greg N. Gregoriou . . . [et al.]. p. cm. ISBN 0-471-68194-6 (cloth) 1. Commodity trading advisors. I. Gregoriou, Greg N., 1956– HG6046.5.C66 2004 332.64'4—dc22 2004007925 Printed in the United States of America 10 9 8 7 6 5 4 3 2 1 Contents Preface ix Acknowledgments xi About the Editors xiii About the Authors xv Introduction xxiii PART ONE Performance 1 CHAPTER 1 Managed Futures and Hedge Funds: A Match Made in Heaven 5 Harry M.
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
Results show that, relative to other hedge fund strategies, hedge fund strategies with significant equity bias (e.g., event driven, equity long short, and emerging markets) had the most negative returns in the worst S&P 500 months as well as the highest positive returns in the months in which the S&P 500 had its best performance. MANAGED FUTURES (COMMODITY TRADING ADVISORS) The term “managed futures” represents an industry composed of professional money managers known as commodity trading advisors (CTAs) or commodity pool operators (CPOs). Commodity trading advisors or commodity pool operators manage client assets on a discretionary basis, using forwards, futures, and options markets as the primary investment area. Managed futures, through their ability to take both long and short investment positions in international financial and non-financial asset sectors, offer risk and return patterns not easily accessible through traditional (such as long-only stock and bond portfolios) or other nontraditional investments (e.g., hedge funds, real estate, private equity, or commodities).
For systematic trend-following global macro managers who trade primarily in futures and option markets, returns are similar to those of commodity trading advisors. CASAM/CISDM Fund of Funds Index (CISDM Fund of Funds): The median performance of all hedge fund of funds managers reporting to the CASAM/ CISDM Hedge Fund Database. Its objective is to provide an estimate of the rate of return to fund of funds which invest in hedge fund managers. Fund of funds managers have discretion in creating funds of funds which reflect either a single strategy or a wide range of underlying hedge fund strategies. CTA INDICES Barclay CTA Index: A benchmark of representative performance of commodity trading advisors reporting to the Barclay Group. The Barclay CTA Index for the year 2009 is unweighted and rebalanced at the beginning of each year.
See CAPM (Capital Asset Pricing Model) Capital International Stock Indices, 168 Capital Market Line (CML), 5–6 CAPM (Capital Asset Pricing Model), 4–6, 18, 62–63 acceptance of, 28 and efficient market hypothesis, 6–10 and market risk, 43 Cash flow, 98 Casualty insurance, 98 CISDM CTA indices, 149, 150, 261, 262 CISDM ELS index, 193 CISDM Fund of Fund indices, 267, 268 CISDM Hedge Fund indices, 55, 131, 142, 144, 145, 185 CISDM indices, 259, 260, 261, 262, 263 Clustering, volatility, 95 Collar strategy, 234 Collateralized debt obligations (CDOs), 228, 229 Commodities, 59, 61, 65, 129, 130, 143–148, 160–165 benchmarks, 179–185, 275 futures, 12 Index return and risk performance, 162–163 volatility, 182, 185 Commodity Futures Trading Commission (CFTC), 11 Commodity pool operators (CPOs), 143 Commodity Research Bureau, 265, 266 Commodity risk, 196 Commodity trading advisors. See CTAs (commodity trading advisors) Conditional model, 41 Conditional performance evaluation, 53–54 Constant proportional portfolio insurance (CPPI), 107 Convexity, 49–50 Core allocation, 110–133 Correlation analysis, 34, 116 Correlations, 24, 68–69, 214 between Barclays Capital U.S. Aggregate and S&P 500, 267 benchmark, 137 betwen cash and futures price changes, 204 CISDM CTA indices, 149 between CISDM Fund of Fund indices and BarCap U.S.
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
So, thanks to Jack for writing this incredibly simple, clear, and commonsense guide to the market. Better late than never. I will recommend it to everyone I know. Market Sense and Nonsense is now required reading for every investor (and the sooner they read it, the better). Joel Greenblatt August 2012 Prologue* Many years ago when I worked as a research director for one of the major Wall Street brokerage firms, one of my job responsibilities included evaluating commodity trading advisors (CTAs).1 One of the statistics that CTAs were required by the regulatory authorities to report was the percentage of client accounts that closed with a profit. I made the striking discovery that the majority of closed accounts showed a net loss for virtually all the CTAs I reviewed—even those who had no losing years! The obvious implication was that investors were so bad in timing their investment entries and exits that most of them lost money—even when they chose a consistently winning CTA!
A comparison between a long-biased equity hedge fund and a fixed income arbitrage fund would be biased in favor of the equity manager if the stock market was in a general uptrend during the comparison period and the reverse if stock prices were predominantly in a declining phase. Markets. Performance may often be more indicative of the market environment than the skill of the manager. Even if two managers fall into the same strategy category, if they trade different markets, then comparisons can be very misleading. For example, if two managers are both trend-following commodity trading advisors (CTAs), but one trades only commodity markets and the other trades foreign exchange (FX), a comparison when one market sector was largely trending and the other experiencing choppy price action (a highly unfavorable environment) would say more about the relative trendiness of the sectors than the skill differences between the managers. Longer Track Records Could Be Less Relevant It is commonly assumed that longer track records for a fund are more meaningful than shorter track records.
The success of a global macro fund is dependent on the manager’s ability to correctly analyze the probable price direction of major global market trends and to successfully time implied trades. Managed futures and FX (CTAs). This group of managers executes all their trades in the futures or FX markets, or both. These types of managers are typically referred to as CTAs, a term that stands for commodity trading advisors, the official designation for managers registered with the Commodity Futures Trading Commission (CFTC) and members of the National Futures Association (NFA). The term is a misnomer on at least two counts. First, a CTA is a fund or account manager with direct investment responsibility and not an advisor as the name appears to suggest. Second, CTAs do not necessarily trade only commodities as the name implies; the vast majority of CTAs also trade futures contracts in one or more financial sectors, including stock indexes, fixed income, and FX.
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
Dunn Capital Management, Inc. 18. Carla Cavaletti, Comeback Kids: Managing Drawdowns According to Commodity Trading Advisors. Futures, Vol. 27, No. 1 (January 1998), 68. 19. Michael Peltz, John W. Henry’s Bid to Manage the Future. Institutional Investor (August 1996). 20. D. Harding, G. Nakou and A. Nejjar, “The Pros and Cons of Drawdown as a Statistical Measure of Risk for Investments.” AIMA Journal, April 2003, 16–17. 21. Carla Cavaletti, Comeback Kids: Managing Drawdowns According to Commodity Trading Advisors. Futures, Vol. 27, No. 1 (January 1998), 68. 22. The Value of a Long-Term Perspective. Marketing Document. John W. Henry and Company, Inc. October 1999. 23. Carla Cavaletti, Comeback Kids: Managing Drawdowns According to Commodity Trading Advisors. Futures, Vol. 27, No. 1 (January 1998), 68. 24. Thomas F. Basso, When to Allocate to a CTA?
When the economic weather changes, we will change our course with it and will not try to forecast the future time or place at which the wind will change. William Dunnigan (1954) In the fall of 2008 I noticed an academic paper right at the time trend followers were having some of their best performance ever. The title of the paper, taking a stab at trend traders (commonly referred to by the regulatory name of Commodity Trading Advisor), was: “Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors” The author Geetesh Bhardwaj wrote that paper while working at AIG Financial Products. So Bhardwaj was part of a firm that took $150 billion of taxpayer dollars to stay afloat, but rips a strategy (trend following) that actually makes money without the government propping it up? The irony to me was inescapable. Shortly after my criticism, Bhardwaj left AIG and joined index mutual fund Vanguard.
Collins, Seeding Tomorrow’s Top Traders; Managed Money; Dunn Capital Management Provides Help to Commodity Trading Advisor Start-ups. Futures, No. 6, Vol. 32, 67, (May 1, 2003) ISSN: 0746-2468. 402 Trend Following (Updated Edition): Learn to Make Millions in Up or Down Markets 8. In J. R. Newman (ed.) The World of Mathematics. New York: Simon and Schuster, 1956. 9. Jim Collins, Good to Great, New York: Harper Business, 2001. 10. Robert Koppel, The Intuitive Trader. Hoboken, NJ: John Wiley & Sons, Inc., 1996, 74. 11. The Reason Foundation. See www.reason.org. 12. Daniel P. Collins, Seeding Tomorrow’s Top Traders; Managed Money; Dunn Capital Management Provides Help to Commodity Trading Advisor Start-ups. Futures, No. 6, Vol. 32, 67, (May 1, 2003) ISSN: 0746-2468. 13. Tricycle Asset Management, part of the Market Wizards Tour.
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
The currency carry trade involves investing in currencies with high interest rates while shorting currencies with low interest rates. This strategy earns an interest rate differential, essentially borrowing one currency at low interest and investing in another currency with a higher interest rate, but it is exposed to the risk that the relative values of the currencies can change. Managed futures investors (also called commodity trading advisors, CTAs) trade many of the same securities as global macro traders: bond futures, equity index futures, currency forwards, and commodity futures. Managed futures investors often focus on finding price trends, buying instruments that are trending up, and shorting instruments that are trending down. For instance, if gold prices have been rising, a managed futures hedge fund may buy gold futures, betting that the price will continue to rise, relying on the maxim that The trend is your friend.
• Enter–exit trading rule. Another approach is to think in terms of discrete trades: ○ For each asset, determine when to enter a new trade and how to size the initial position. ○ Determine how the position is resized over time, depending on the circumstances. ○ Determine when to exit the trade. For instance, if the price of gold goes above its highest value over the past 20 days (what some commodity trading advisors (CTAs) and managed futures traders call a breakout), then buy gold futures. Hold the position until the gold price drops below its 10-day minimum. With this kind of enter–exit rule, you may or may not have a gold position at any time. Furthermore, if you pursue this type of trading rule using many securities, you could have many or few positions on at any time, so your risk varies over time.
—Jesse Livermore David Ricardo’s imperative, which has survived two centuries, suggests an attention to trends.1 Trends are also at the heart of the century-old statement by the legendary trader Jesse Livermore, and trends continue to play an important role for active investors. The traders who are most directly focused on trend-following investing are the managed futures hedge funds and commodity trading advisors (CTAs). Such funds have existed at least since Richard Donchian started his fund in 1949, and they have proliferated since the 1970s when futures exchanges expanded the set of tradable contracts. BarclayHedge estimates that the CTA industry has grown, managing approximately $320 billion as of the end of the first quarter of 2012.2 Managed futures returns can be largely understood by simple, implementable trend-following strategies—specifically time series momentum strategies.
Trend Commandments: Trading for Exceptional Returns by Michael W. Covel
Albert Einstein, Bernie Madoff, Black Swan, business cycle, buy and hold, commodity trading advisor, correlation coefficient, delayed gratification, diversified portfolio, en.wikipedia.org, Eugene Fama: efficient market hypothesis, family office, full employment, Lao Tzu, Long Term Capital Management, market bubble, market microstructure, Mikhail Gorbachev, moral hazard, Myron Scholes, Nick Leeson, oil shock, Ponzi scheme, prediction markets, quantitative trading / quantitative ﬁnance, random walk, Sharpe ratio, systematic trading, the scientific method, transaction costs, tulip mania, upwardly mobile, Y2K, zero-sum game
., 181 268 Index Business Cycles and Forecasting (Bratt), 230 conversion to trend following, 155 buy-and-hold strategy, 151-153 courage, 30 defined, 12 Cowles, Alfred III, 228 Warren Buffett, 157-158 Cox, Christopher, 169 C Cramer, Jim, 162 calculated risk, 56 crashes, trend following during, 97-98 Californication (television program), 211 critical thinking, defined, 217 capitalism, 113-115 critics’ responses to trend following, 193-196 change in markets, 45 crowd mentality, 117-120 Clews, Henry, 224 CTAs (commodity trading advisors), defined, 11 Clinton, Bill, 181-182 CNBC, 161-163, 177, 215-218 Cohn, Gary, 175 commitment, importance of, 127-128 curiosity, importance of, 207-208 current trends, defined, 39 Cutten, Arthur W., 223 Commodities Corporation, 69 D commodity trading advisors (CTAs), defined, 11 decisions, immediacy of, 131-132 The Complete TurtleTrader (Covel), 199 compounding, importance of, 22-23 degrees, worth of, 123 delay in decision-making, avoiding, 131-132 Dennis, Richard, 199 computers, role in trend following, 87-88 “Determining Optimal Risk” (Druz and Seykota), 62 consistency, 139 diversified portfolios, example of, 65-67 Contact (film), 4 contradictions in market predictions, 175-178 Donahue, Phil, 113 Donchian, Richard, 230-231, 239 Index Dow Theory, 225-226 fat tails, 137 Dow, Charles H., 225 Faulkner, Charles, 59 drawdowns, 69-70 Field, Jacob, 226 Druz, David, 18, 62 50 Cent, 166 Duchovny, David, 211 financial bubbles, irrational behavior in, 25-26 Dunn, Bill, 15-16, 195 Dunnigan, William, 229-230, 239 E Economic Bill of Rights, 114 economic philosophy, assumptions in, 25 economy, effect on presidential approval ratings, 181-182 Edwards, Robert D., 226 Efficient-Markets Hypothesis, 101-102 Elizabeth II (queen of England), 47 Elliott, R.
Let men be fools because that is part of their nature.3 Henry Ford: “If I’d asked my customers what they wanted, they would have said a faster horse.”1 Jargon To avoid confusion throughout Trend Commandments, a few of Wall Street’s favorite catch phrases need to be defined. I am defining these because Wall Street suits use them to sell. Do not let the jargon engulf you. CTA: CTA stands for commodity trading advisor. It is a government term used to classify regulated fund managers who primarily trade futures markets. Almost all successful CTAs trade as trend following traders. CTAs are the other quants the media never seems to cover accurately. Managed Futures: This is a term that describes regulated fund managers who use futures to trade for clients. It is an awful term because it fixates on the instrument (futures), not the strategy.
Traders at Work: How the World's Most Successful Traders Make Their Living in the Markets by Tim Bourquin, Nicholas Mango
algorithmic trading, automated trading system, backtesting, buy and hold, commodity trading advisor, Credit Default Swap, Elliott wave, fixed income, Long Term Capital Management, paper trading, pattern recognition, prediction markets, risk tolerance, Small Order Execution System, statistical arbitrage, The Wisdom of Crowds, transaction costs, zero-sum game
The ones that didn’t make it into the report were my worst, probably because I didn’t justify to myself, to my readers, why I was getting into it in the first place. Funny how it worked that way. CHAPTER 2 Linda Raschke Linda Bradford Raschke has been a full-time professional trader since 1981. She began as a market maker in equity options and was a member and floor trader on two exchanges. In the early 1990s, Raschke became a registered Commodity Trading Advisor (CTA) and started LBRGroup, Inc., a professional money management firm. In addition to running successful CTA programs, she has been principal trader for several hedge funds and has run commercial hedging programs. She was recognized in Jack Schwager’s book, The New Market Wizards (John Wiley & Sons, 1992), and in Sue Herera’s book, Women of the Street (John Wiley & Sons, 1997). Raschke has been active with the Market Technicians Association for many years and has lectured in over 30 countries.
I’m mainly trying to align my trading operation in a way that enforces discipline for me and also looks relatively attractive and businesslike from the perspective of someone who may want me to manage their capital for them. Bourquin: And are you managing other people’s money right now as well? Wilson: Yes, I am. I’m registered with the National Futures Association [NFA], and I’m a registered commodity trading advisor [CTA]. Bourquin: Do you take money out of those managed accounts to pay yourself, or, because you have a career, do you reinvest and let your profits run and continually build your account? Wilson: All the management fees come out at the end of every month, and I just let those accumulate outside the account. It feels professional, like I’m paying myself, and I don’t reinvest that money in the trading account.
For the most part, though, I trade my accounts on my own and work on joint ventures with others, where we’ll combine forces to trade an account, and that’s a nice mix. It’s valuable as a trader to not only have the independence of trading your own account, but at the same time be involved and work with other human beings. It can be fun, too. Bourquin: Speaking of the business aspect of trading, have you ever thought about managing money? Carter: It’s funny you asked that, because I actually just started a commodity trading advisor [CTA] fund, called Razor Trading, as well as an options auto-trading service. Personally, I don’t mind taking a lot of risk in my own account, and even though investors will initially always say to go ahead and do the same for them, they’ll start freaking out as soon as they see the first drawdown. So, you really have to expect that and be prepared to deal with all the phone calls and e-mails that will come with it.
Inside the House of Money: Top Hedge Fund Traders on Profiting in a Global Market by Steven Drobny
Albert Einstein, asset allocation, Berlin Wall, Bonfire of the Vanities, Bretton Woods, business cycle, buy and hold, buy low sell high, capital controls, central bank independence, commoditize, commodity trading advisor, corporate governance, correlation coefficient, Credit Default Swap, diversification, diversified portfolio, family office, fixed income, glass ceiling, high batting average, implied volatility, index fund, inflation targeting, interest rate derivative, inventory management, John Meriwether, Long Term Capital Management, margin call, market bubble, Maui Hawaii, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shale / tar sands, oil shock, out of africa, paper trading, Paul Samuelson, Peter Thiel, price anchoring, purchasing power parity, reserve currency, risk tolerance, risk-adjusted returns, risk/return, rolodex, Sharpe ratio, short selling, Silicon Valley, The Wisdom of Crowds, too big to fail, transaction costs, value at risk, yield curve, zero-coupon bond, zero-sum game
If I have a list of things that their computer says to look at, like India bank stocks or swaps in Sweden or the Canadian dollar, then I have no excuse not to look.What we’re trying to do is develop computer-driven systems to identify price changes that appear nonrandom, not for trading so much but as a research tool. Over time, maybe it will develop into a trading system, which would be great because one thing we all know is that, on average, Commodity Trading Advisors’ (CTA) trading models work. What are some trades that you have in your absolute return book right now? Is India in there? I would count India in that category of having outperformance potential but not in terms of having a separate 10 percent allocation. We actually want to get India to a 10 percent allocation such that it becomes 50 percent of our equity allocation.We haven’t thought of India as a 10 percent nonsystematic bet, although maybe it should be.
Now it’s a push of a button and it goes down so smooth. It shows you just how vast the markets are and how many people are playing today. I could walk down onto the floor right now and sell a billion dollars of anything. It will make some noise but it will get done quickly. That’s a phenomenal change from the old psyche. It’s one of those things where you have to be careful what you wish for—it may just come true.The hedge fund/Commodity Trading Advisor (CTA) game is getting to the point where it’s starting to turn in on itself. 204 INSIDE THE HOUSE OF MONEY In the currency markets, at least you have central banks, corporates, banks, and other anonymous, distant entities to trade against. But the other markets are just full of speculators beating each other up. It’s no wonder returns have come down. What do you mean when you say “the old psyche”?
When I say mean reversion, I am saying that margins revert to the mean. People often get it wrong and think it’s price that reverts to the mean, but that is not the case in commodities. It is margins that revert to the mean, so paying up is something you need to do. Adding to a position that is going your way has always been a tough one for fundamental discretionary traders but a hallmark of systematic black box Commodity Trading Advisors (CTAs). Has the huge increase in capital allocated to CTAs (see Figure 12.3) forced you to change your style? It’s changed a little bit in the way we size our positions. Because all these CTAs tend to be shorter duration, their money flows can change the short-term volatility.They can cause dislocations and inef25,000 140 120 Assets Barclay CTA Index 100 15,000 80 60 10,000 40 5,000 20 0 19 79 19 80 19 81 19 82 19 83 19 84 19 85 19 86 19 87 19 88 19 89 19 90 19 91 19 92 19 93 19 94 19 95 19 96 19 97 19 98 19 99 20 00 20 01 20 02 20 03 20 04 0 FIGURE 12.3 1979–2004 Barclay Group CTA Index and CTA Assets under Management, Reprinted courtesy of Sol Waksman and The Barclay Group.
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
Antti Ilmanen Bad Homburg, November 2010 Abbreviations and acronyms AM Arithmetic Mean ATM At The Money (option) AUM Assets Under Management BEI Break-Even Inflation BF Behavioral Finance B/P Book/Price, book-to-market ratio BRP Bond Risk Premium, term premium B-S Black–Scholes C-P BRP Cochrane–Piazzesi Bond Risk Premium CAPM Capital Asset Pricing Model CAY Consumption wealth ratio CB Central Bank CCW Covered Call Writing CDO Collateralized Debt Obligation CDS Credit Default Swap CF Cash Flow CFNAI Chicago Fed National Activity Index CFO Chief Financial Officer CMD Commodity (futures) CPIyoy Consumer Price Inflation year on year CRB Commodity Research Bureau CRP Credit Risk Premium (over Treasury bond) CRRA Constant Relative Risk Aversion CTA Commodity Trading Advisor DDM Dividend Discount Model DJ CS Dow Jones Credit Suisse DMS Dimson–Marsh–Staunton D/P Dividend/Price (ratio), dividend yield DR Diversification Return E( ) Expected (conditional expectation) EMH Efficient Markets Hypothesis E/P Earnings/Price ratio, earnings yield EPS Earnings Per Share ERP Equity Risk Premium ERPB Equity Risk Premium over Bond (Treasury) ERPC Equity Risk Premium over Cash (Treasury bill) F Forward price or futures price FF Fama–French FI Fixed Income FoF Fund of Funds FX Foreign eXchange G Growth rate GARCH Generalized AutoRegressive Conditional Heteroskedasticity GC General Collateral repo rate (money market interest rate) GDP Gross Domestic Product GM Geometric Mean, also compound annual return GP General Partner GSCI Goldman Sachs Commodity Index H Holding-period return HF Hedge Fund HFR Hedge Fund Research HML High Minus Low, a value measure, also VMG HNWI High Net Worth Individual HPA House Price Appreciation (rate) HY High Yield, speculative-rated debt IG Investment Grade (rated debt) ILLIQ Measure of a stock’s illiquidity: average absolute daily return over a month divided by dollar volume IPO Initial Public Offering IR Information Ratio IRP Inflation Risk Premium ISM Business confidence index ITM In The Money (option) JGB Japanese Government Bond K-W BRP Kim–Wright Bond Risk Premium LIBOR London InterBank Offered Rate, a popular bank deposit rate LP Limited Partner LSV Lakonishok–Shleifer–Vishny LtA Limits to Arbitrage LTCM Long-Term Capital Management MA Moving Average MBS (fixed rate, residential) Mortgage-Backed Securities MIT-CRE MIT Center for Real Estate MOM Equity MOMentum proxy MSCI Morgan Stanley Capital International MU Marginal Utility NBER National Bureau of Economic Research NCREIF National Council of Real Estate Investment Fiduciaries OAS Option-Adjusted (credit) Spread OTM Out of The Money (option) P Price P/B Price/Book (valuation ratio) P/E Price/Earnings (valuation ratio) PE Private Equity PEH Pure Expectations Hypothesis PT Prospect Theory r Excess return R Real (rate) RE Real Estate REITs Real Estate Investment Trusts RWH Random Walk Hypothesis S Spot price, spot rate SBRP Survey-based Bond Risk Premium SDF Stochastic Discount Factor SMB Small Minus Big, size premium proxy SR Sharpe Ratio SWF Sovereign Wealth Fund TED Treasury–Eurodollar (deposit) rate spread in money markets TIPS Treasury Inflation-Protected Securities, real bonds UIP Uncovered Interest Parity (hypothesis) VaR Value at Risk VC Venture Capital VIX A popular measure of the implied volatility of S&P 500 index options VMG Value Minus Growth, equity value premium proxy WDRA Wealth-Dependent Risk Aversion X Cash flow Y Yield YC Yield Curve (steepness), term spread YTM Yield To Maturity YTW Yield To Worst Disclaimer Antti Ilmanen is a Senior Portfolio Manager at Brevan Howard, one of Europe’s largest hedge fund managers.
It includes infrastructure, timber, and farmland; art and other collectibles (fine wines, rare coins, stamps); and more novel securities such as catastrophe bonds, carbon credits, intellectual property rights, viatical or life insurance settlements, longevity swaps, and others. If hedge funds can be characterized as an asset class, then the list of alternatives may be extended to include managed futures (typically momentum-oriented commodity-trading advisors or CTAs), global tactical asset allocation managers (typically value-oriented investors), active FX, volatility trading, alternative betas and hedge fund replication, as well as investments focused on corporate governance, sustainable development, and shareholder activism. Figure 11.1 shows the cumulative returns of the big-four alternatives since 1984. Note that historical returns on actively managed asset classes (HF, PE) may be misleading because of survivorship and other reporting biases that can overstate actual returns.
Overall, adjusting for backfill bias appears to reduce (equally weighted) average HF returns by 2% to 4%. The bias is milder for value-weighted averages because backfilling is more often done by small young funds. While many studies make adjustments for survivorship and backfill biases, few studies are able to quantify selection, liquidation, lookback, and lookahead biases. Box 11.4. CTA performance Commodity trading advisors (CTAs) or managed futures may be viewed either as a subset of HFs or as a distinct but close cousin. Unlike most HFs, CTAs tend to be systematic and focus on trend following (diversifying across a large number of liquid assets and both fast and slow models). Chapter 14 discusses simulated trend-following strategies. Bhardwaj–Gorton–Rouwenhorst (2008) present a scathing analysis of CTA performance, titled “Fooling some of the people all the time”.
Hedge Fund Market Wizards by Jack D. Schwager
asset-backed security, backtesting, banking crisis, barriers to entry, beat the dealer, Bernie Madoff, Black-Scholes formula, British Empire, business cycle, buy and hold, Claude Shannon: information theory, cloud computing, collateralized debt obligation, commodity trading advisor, computerized trading, credit crunch, Credit Default Swap, credit default swaps / collateralized debt obligations, delta neutral, diversification, diversified portfolio, Edward Thorp, family office, financial independence, fixed income, Flash crash, hindsight bias, implied volatility, index fund, intangible asset, James Dyson, Jones Act, Long Term Capital Management, margin call, market bubble, market fundamentalism, merger arbitrage, money market fund, oil shock, pattern recognition, pets.com, Ponzi scheme, private sector deleveraging, quantitative easing, quantitative trading / quantitative ﬁnance, Right to Buy, risk tolerance, risk-adjusted returns, risk/return, riskless arbitrage, Rubik’s Cube, Sharpe ratio, short selling, statistical arbitrage, Steve Jobs, systematic trading, technology bubble, transaction costs, value at risk, yield curve
He dropped out of college with a near-perfect GPA and only nine credits short of a degree to pursue a career in futures trading. Although it is a decision that seems bafflingly irrational to me, give credit to Ramsey for knowing exactly what he wanted to do, and as he says, “It turned out okay.” Ramsey trades the highly liquid futures and foreign exchange (FX) markets. Although the majority of Commodity Trading Advisors (CTAs) 1 use a systematic approach, Ramsey is strictly a discretionary trader. He also differs from most CTAs by incorporating fundamentals into his decisionmaking process. Ramsey begins by establishing a broad fundamental macro view that determines his directional bias in each market. Once this bias is established, he will seek to go short the weakest market in a sector if he is bearish or long the strongest market if he is bullish, using technical analysis to time trade entry and position adjustments.
Ramsey continues to trade and monitor his positions even when he is on vacation. He also awakens himself multiple times each night to check on his positions. This type of all-encompassing commitment to trading it is not necessarily recommended as a lifestyle, but rather is offered as an observation of one of the characteristics of trading success. For Ramsey, however, I suspect such commitment is not a burden, as trading is a passion, not a chore. 1The term Commodity Trading Advisor is the official designation for managers registered with the Commodity Futures Trading Commission (CFTC) and members of the National Futures Association (NFA), and is a misnomer on at least two counts: (1) A CTA is a fund or account manager with direct investment responsibility and not an advisor as the name appears to suggest. (2) CTAs do not necessarily trade only commodities as the name implies.
After setting up a real-time quote system to manually day-trade the markets, he abandoned the process after only three days, as he quickly realized, This isn’t me. This just doesn’t work for me. 1Commodity Trading Advisors (CTAs) are managers who trade futures and are registered with the Commodity Futures Trading Commission (CFTC). This official nomenclature is poorly chosen on at least two grounds: Commodity Trading Advisors are managers, not advisors, and a large majority of the trading they engage in is in financial markets (e.g., stock indexes, interest rates, and currencies) rather than commodities. 2Management fees are collected on assets under management. Incentive fees are collected as a percent of profits earned above a high-water mark—the highest NAV level at which incentive fees were previously collected.
New Market Wizards: Conversations With America's Top Traders by Jack D. Schwager
backtesting, beat the dealer, Benoit Mandelbrot, Berlin Wall, Black-Scholes formula, butterfly effect, buy and hold, commodity trading advisor, computerized trading, Edward Thorp, Elliott wave, fixed income, full employment, implied volatility, interest rate swap, Louis Bachelier, margin call, market clearing, market fundamentalism, money market fund, paper trading, pattern recognition, placebo effect, prediction markets, Ralph Nelson Elliott, random walk, risk tolerance, risk/return, Saturday Night Live, Sharpe ratio, the map is not the territory, transaction costs, War on Poverty
To answer this question, let’s pick up the scene six years later, when I am preparing to do this book. My first job is to research possible candidates to be interviewed. In the area of The Silence of the Turtles / 139 futures traders, one reference source I used was the quarterly summary provided by Managed Accounts Reports. This report summarizes the performance of a large number of commodity trading advisors (CTAs), providing a single synopsis sheet for each advisor. At the bottom of each sheet is a summary table with key statistics, such as average annual percentage return, largest drawdown, Sharpe ratio (a return/risk measure), percentage of winning months, and the probabilities of witnessing a 50 percent, 30 percent, and 20 percent loss with the given CTA. To be objective, I flipped through the pages, glancing only at the tables (not the names at the top of the sheets) and checking off the names of those advisors whose exceptional performance seemed to jump off the page.
Nevertheless, it does offer an incredibly important message to those interested in trading: It is possible to develop a system that can significantly beat the market. Moreover, if you can discover such a system and exercise the discipline to follow it, you can succeed in the markets without being a born trader. Monroe Trout THE BEST RETURN THAT LOW RISK CAN BUY I first met Monroe Trout several years ago, when a broker in my firm, who was trying to land Trout’s account, brought him by as part of the company tour. I knew that Trout was a commodity trading advisor (CTA) new to the business, but I didn’t know much else. Subsequently, I often heard Trout’s name mentioned as one of the younger CTAs who was doing very well. I didn’t realize how well until I started to work on this book. In consulting the quarterly issue of Managed Accounts Reports while doing research for this book, I found that in terms of return/risk measurements, Trout’s performance was the best of the more than one hundred managers covered.
Although their trading methods are completely different—Trout is a directional trader, whereas Hull is an arbitrageur—their assessments of the key to successful trading are virtually identical: a combination of having an edge and using rigid money management controls. Al Weiss THE HUMAN CHART ENCYCLOPEDIA I n terms of return/risk ratio, Al Weiss may well have the single best long-term track record for a commodity trading advisor. Since he began trading in 1982 as AZF Commodity Management, Weiss has averaged 52 percent annually. One thousand dollars invested with Weiss in 1982 would have been worth almost $53,000 at the end of 1991. However, returns are only half the story. The truly impressive element in Weiss’s track record is that these high gains were achieved with extremely small equity drawdowns. During this entire period, the largest single equity drawdown witnessed by Weiss was 17 percent in 1986.
Trade Your Way to Financial Freedom by van K. Tharp
asset allocation, backtesting, Bretton Woods, buy and hold, capital asset pricing model, commodity trading advisor, compound rate of return, computer age, distributed generation, diversification, dogs of the Dow, Elliott wave, high net worth, index fund, locking in a profit, margin call, market fundamentalism, passive income, prediction markets, price stability, random walk, reserve currency, risk tolerance, Ronald Reagan, Sharpe ratio, short selling, transaction costs
Although there are many trading and investing methods that make good money, I don’t know of any that meet those requirements. However, most beginning traders and investors with small amounts of money are constantly giving themselves similar expectations—expectations that they are unlikely to meet. DESIGNING OBJECTIVES: A MAJOR PART OF YOUR SYSTEM WORK I once worked with a man whose job was to give money to budding commodity trading advisors (CTAs). Part of his job was to assess the various systems that these CTAs had developed, and many people considered him to be one of the world’s experts in system development. One day I said to him, “If you could give any particular suggestion to traders who are trying to come up with a new system, what would it be?” His response was, “To spend at least 50 percent of the system development time working out objectives.”
LeBeau is well known as a former editor of a great newsletter entitled the Technical Traders Bulletin. He is also a coauthor of an excellent book, Computer Analysis of the Futures Market. Chuck is a talented speaker, and he frequently gives talks at investment conferences. And he has been a guest speaker at many of our How to Develop a Winning Trading System That Fits You workshops. Chuck is now retired and lives near Sedona, Arizona. When he was an active trader, he was a commodity trading advisor (CTA), and later on he had his own hedge fund.4 You might wonder why I asked Chuck, who has such an extensive technical background, to write about fundamental analysis. Chuck used to lecture about fundamental analysis for a major university, and he also ran a discretionary fundamentally based trading system for Island View Financial Group. In Chuck LeBeau’s words, “I prefer to think of myself as a trader who is willing to use the best tools available to get the job done.”
In this last discussion, you learned how to take one of the order concepts (if one of them appeals to you) and use it to your advantage. Such concepts are probably excellent for people who feel that they must know how markets work before they can commit themselves to trade. NOTES 1. Expectancy will be discussed extensively in Chapter 7. It is one of the most important topics that you need to understand as a trader or investor. 2. The CFTC requires that commodity trading advisors include a statement in their advertisements and disclosure documents that says that past results do not reflect upon future results. 3. Tom Basso is now retired from trading and spends his time having fun. However, when he wrote this section, in 1996, he was an active money manager. He still can be reached by e-mail at firstname.lastname@example.org. 4. Chuck LeBeau is also retired. You can reach Chuck LeBeau at email@example.com. 5.
Economists and the Powerful by Norbert Haring, Norbert H. Ring, Niall Douglas
"Robert Solow", accounting loophole / creative accounting, Affordable Care Act / Obamacare, Albert Einstein, asset allocation, bank run, barriers to entry, Basel III, Bernie Madoff, British Empire, buy and hold, central bank independence, collective bargaining, commodity trading advisor, corporate governance, creative destruction, credit crunch, Credit Default Swap, David Ricardo: comparative advantage, diversified portfolio, financial deregulation, George Akerlof, illegal immigration, income inequality, inflation targeting, information asymmetry, Jean Tirole, job satisfaction, Joseph Schumpeter, Kenneth Arrow, knowledge worker, law of one price, light touch regulation, Long Term Capital Management, low skilled workers, mandatory minimum, market bubble, market clearing, market fundamentalism, means of production, minimum wage unemployment, moral hazard, new economy, obamacare, old-boy network, open economy, Pareto efficiency, Paul Samuelson, pension reform, Ponzi scheme, price stability, principal–agent problem, profit maximization, purchasing power parity, Renaissance Technologies, rolodex, Sergey Aleynikov, shareholder value, short selling, Steve Jobs, The Chicago School, the payments system, The Wealth of Nations by Adam Smith, too big to fail, transaction costs, ultimatum game, union organizing, Vilfredo Pareto, working-age population, World Values Survey
Phaliappou and Gottschalg (2009) and Phalippou (2009) have found that investors in private equity buyout on average receive a yield lower than that of the Standard & Poor’s 500, the most important stock market index for the US, after the high fees for these funds are subtracted. However, most investors are not aware of this. The fee structure is so opaque and contracts vary in so many crucial details that it is exceedingly hard to get a good idea of what investors are actually paying (Phalippou 2009). For another important subset of hedge funds, commodity trading advisors (CTAs), Bhardwaj, Gorton and Rouwenhorst (2008) have also examined the industry’s performance claims. They found them just as wanting as the claims of the private equity industry. According to their calculations, returns are ﬁve percentage points above those of Treasury bonds before fees, but barely higher after fees, which average 4 percent per year. This means that fund management claims almost all the proﬁts that exceed those of standard safe investments.
“Why Not Cut Pay?” European Economic Review 42: 459–90. . 2004. “Fairness, Reciprocity and Wage Rigidity.” IZA Discussion Paper 1137. Bhaktavatsalam, Sree Vidya. 2008. Greenspan Helped Pimco Save Billions, Gross Says. Bloomberg, April 21. Bhardwaj, Geetesh, Gary B. Gorton and K. Geert Rouwenhorst. 2008. “Fooling Some of the People All of the Time: The Inefficient Performance and Persistence of Commodity Trading Advisors.” NBER Working Paper 14424. Bhaskar, V., Alan Manning and Ted To. 2002. “Oligopsony and Monopsonistic Competition in the Labor Markets.” Journal of Economic Perspectives 16: 155–74. Black, William. 2010. “Epidemics of ‘Control Fraud’ Lead to Recurrent, Intensifying Bubbles and Crises.” Paper presented at the Murphy Conference on Corporate Law, Fordham Law School, March 12. Blanchard, Olivier. 2006.
Living in a Material World: The Commodity Connection by Kevin Morrison
addicted to oil, barriers to entry, Berlin Wall, carbon footprint, clean water, commoditize, commodity trading advisor, computerized trading, diversified portfolio, Doha Development Round, Elon Musk, energy security, European colonialism, flex fuel, food miles, Hernando de Soto, Hugh Fearnley-Whittingstall, hydrogen economy, Intergovernmental Panel on Climate Change (IPCC), Kickstarter, Long Term Capital Management, new economy, North Sea oil, oil rush, oil shale / tar sands, oil shock, out of africa, Paul Samuelson, peak oil, price mechanism, Ronald Coase, Ronald Reagan, Silicon Valley, sovereign wealth fund, the payments system, The Wealth of Nations by Adam Smith, trade liberalization, transaction costs, uranium enrichment, young professional
Managed commodity funds can be traced back to 1948 when Futures Inc. was set up by Richard Donchian. Donchian is credited 238 | LIVING IN A MATERIAL WORLD with creating a marketing trading tactic known as ‘trend following,’ which is essentially based on the assumption that commodity prices move in long, sustained patterns. It’s a strategy adopted by programmed managed futures funds, otherwise known as Commodity Trading Advisors (CTAs), of which there are now thousands. The CTA is a misnomer; they do not now necessarily invest in commodities but in all classes of futures, including currencies, fixed interest and stock indices. The term harks back to the era prior to the launch of financial futures in the 1970s, and to the period when the only futures were commodities. CTAs also do little advising; they are mainly funds based on programmed trading designed by mathematical boffins.
., J. 254 arsenic 205 Artists’ Project Earth173 n. 28 Asian currency crisis 7 Asian Development Bank 9 World Outlook 2007 9 Associated British Foods 147 Atlantic Richfield 226 n. 43 Aykroyd, Dan 256 Azuriz 164, 165 Babcock, Bruce 56, 57 Bacon, Louis 237, 238 Ballard 193 Baltic Exchange 212, 213 Bath, Henry 226 n. 41 Battery Ventures 266, 267 Bell, Alexander Graham 73 Belmans, Professor Ronald 194 Benton, Oren 42 Berzelius, Jons Jakob 43 Bettelheim, Eric 146, 148 BHP Billiton 13, 200, 205, 211 biodiversity 155–7 BioEnergy 81 292 | INDEX biofuels 11, 38, 44, 55–60, 69, 71–3, 95, 116–18 see also corn Biofuels Security Act (US) 80 biomass 35–6 Birol, Faith 26 Blackstone 266, 267 Blair, Tony 39, 142 Blenheim Capital Management 237 Bolger, Jim 59 Boulle, Jean-Raymond 199 BP 57, 79, 226 n. 43, 261 Brazil sugarcane production 90–4 Brent 138 Brin, Sergey 38 British Transport Police (BTP) 182 Bronks, Richard (Dick) 259, 260 bronze 180–1 Brown, Robert 55, 56 Browne, Sir John 261 Bryce, Colin 259, 260 Bubeck, David 107 Bükk National Park 145 Bunge 89, 234 Bush, George H. 27 Bush, George W. 28, 29, 30, 31, 55, 71, 75, 79, 116, 129, 142, 143, 192 Butter and Cheese Exchange of New York 251 Butter, Cheese, and Egg Exchange of New York 251 Butz, Earl 115 California Public Employees’ Retirement System (Calipers) 244 California Zero Emissions Vehicle (ZEV) mandate 54 Canadian oil sands 49–50 cap-and-trade systems 138–40, 143, 172 n. 23 carbon capture and storage (CCS) 152–3 Carbon Capture and Storage Association, The 176 n. 50 carbon credits 154 carbon dioxide 25–6, 27, 129, 131–2, 170 n. 8, 170 n. 9, 170 n. 10 trading 137, 138–9, 140, 144 carbon economy 145–7 carbon emissions 128–9 carbon footprint 126, 128 carbon market 137–8 carbon pricing 3, 11, 29 carbon sequestration 31, 135, 150 carbon sinks 152–3 CarbonNeutral Company, The 144 Cargill 89, 234 Carnegie, Andrew 215 Carter, President Jimmy 30, 73, 116 Caruso, Guy 38, 53 Carver, George Washington 102, 120 n. 14 Case of Deferred Mitigation (CDM) 151–2, 175 n. 46 Cashman, Eugene 231, 233, 234, 270 Castro, Fidel 92, 219 cattle 87–8 Caxton Associates 237 cellulosic ethanol 56–7 Certified Emissions Reductions (CERs) 151, 152 Charles, Prince of Wales 147 Chavez, Hugo 45 Chemurgic Council 95 Chernobyl disaster 40 Chevron 261 ChevronTexaco 79 Chicago Board of Trade (CBOT) 99–100, 139, 146, 232–3, 234, 236, 246, 247, 248, 249, 253, 255, 257, 266, 269, 270 Chicago Climate Exchange 143, 146, 154, 155 Chicago Mercantile Exchange 245, 246, 248, 249, 256, 264, 267, 269 chicken 84–7 Chile copper production 195–8, 199, 203–10 nitrates 198–9 Chile Copper Company 197 China car market 18–19, 61 n. 2 coal 20–1, 24 demand for metals 12 electricity 19–20, 187–8 energy consumption 25, 61 n. 5 Forbidden City 17–18 lifestyle 17–18 meat consumption 83–4 migration 6–7, 19 nickel 218 pollution 25 population 6–7, 19 standard of living 6 supermarkets in 89 China Institute of Financial Derivatives 249 INDEX Chinatown 162 Chiquita Brands International 239 chlorodifluoromethane (HCFC-22) 152 Cisco Systems 263 Citigroup 242 Clean Air Act (US) 1990 74 Amendments 139 Clean Air Interstate Rule (CAIR) 172 n. 23 clean coal technology 31–3 Clean Water Act 1970 (US) 156 Climate Care 144 Climate Change Registrar 174 n. 35 climate change cost of 134–5 history 127–8 Climate Exchange Plc 146 Clinton, President Bill 80 Clinton, Hillary 80, 116, 143 CME Group 165, 232 co2balance.com 144 Coady Diemar Partners 141 Coady, Pat 141 coal 10–11, 20–3, 24, 25–6, 26–9, 131 China 20–1, 24 clean technology 31–3 liquid 33–4 pollution controls 27–9 UK 22–3 US 20–1 Coalition of Rainforest Nations 151 Coase, Ronald 138 Codelco 10, 203, 207, 213 Cohen, gary 259, 262 Coldplay 145 Collins, Keith 117 Coltrane, John 183 Columbus, Christopher 97, 225 n. 40 Comex 213, 253, 256, 268 Commodities Corp 247 Commodities Corporation 238, 239 Commodities Futures Trading Commission 245 Commodity Exchange Act 1936 (US) 245 Commodity Exchange Inc. (Comex) 255 Commodity Future Trading Commission (CFTC) (US) 74, 253, 263 commodity indices 240–2 commodity market manipulation 245–7 Commodity Trading Advisors (CTAs) 238 Congo, copper in 201, 202, 210–11 | 293 Connaughton, James 29, 31 ConocoPhilips 57, 79 Conservation International 93 Continental Power Exchange (CPE) 257, 258 Cooke, Jay 198, 222 n. 18 copper 4, 9, 11 applications 187–90 Congo 201, 202, 210–11 cost 211–13, 214–17 demand 182–5 electrical applications 186–7 in electricity generation 194–5 history 185–6 prices 199–201, 222 n. 17 production 195–8, 199 recycling 183, 184–5 theft 179–82 trade 211–13 under-sea extraction 217 in vehicles 190–4 Copper Export Association 201 Copper Exporters Incorporated 201 Copper Producers’ Association 200 coralconnect.com 260 corn 68–84, 96–8, 98–9, 99–101 hybrids 101–3 GM 104–6 diversity 106–10 Corn Products 233 cotton 166 Countryside Alliance 146 credit crisis (2007) 7 Crocker, Thomas 138 Cruse, Richard 111 D1 Oils 57, 58, 59 Dabhol gas-fired power plant 35 Dales, John 138 Daly, Herman 136 Darwin, Charles 67 Davis, Adam 157 Davis, Miles 183 Day after Tomorrow, The 15 n. 1 De Angelis, Anthony ‘Tino’ 245 De Beers 200, 210 De Soto, Hernando 136 Deere, John 100 deforestation 87, 147 Dennis, Richard 237 Deripaska, Oleg 199 Deutsche Bank 246, 261 294 | INDEX Diamond, Jared 97 DiCaprio, Leonardo 130 Dimas, Stavros 160 distillers’ dried grains with solubles (DDGS) 81 Dittar, Thomas 231 Donchian, Richard 237–8 Donson, Harry 199 dot.com bubble 7, 14, 241, 243 Doud, Gregg 82, 83 Dow Jones-AIG Commodity Index 240 Dresdner Kleinwort 47 Drexel Burnham Lambert 254 Dreyfus, Louis 89 Duke Energy 258 Dunavant, Billy 237 DuPont 102 E85 79 Ealet, Isabelle 259, 260 Earth Sanctuatires 157 Earth Summit Bali 142 Rio 1992 141 Ebay 38 Ecosystem Marketplace 157 Edison, Thomas 17, 95, 186 Ehrlich, Paul 13, 14, 16 n. 9 Population Bomb, The 14 Eisenhower, President 40 El Paso 258 Electric Power Research Institute (EPRI) 153 electric vehicles 54, 191–3 11th Hour, The 130 Elf 261 Elton, Ben 135 Emissions Trading Program 139 Energy Information Administration (EIA) 38 Energy Policy Act 2005 (US) 28 energy security 28–9 Energy Security Act 1980 (US) 74 Enron 35, 164, 165, 213, 246, 257–64 Enron Online 213, 225 n. 40, 257, 258, 259 Environmental Protection Agency (US) 27, 62 n. 17, 75, 139 ethanol 69–70, 73–81, 92, 119 n. 6 see also biofuels Eurex 262 European Climate Exchange 146 European Union 142, 158, 160 Waste Electrical and Electronic Equipment (WEEE) 185 Evelyn, John 127 exchange-traded funds (ETFs) 13, 270 Exxon 32, 50, 52, 261 ExxonMobil 13, 79, 242, 254 Faraday, Michael 186 Farm Credit Administration 76 farm debt crisis 114–15 farm payments 115–16 farm sinks 154–5 Fearnley-Whittingstall 86 Federal Bureau of Investigation 246 Federal Clean Water Act (US) 156 F-gases 131 Firewire 260 Fisher, Mark 266, 269 Fleming, Roddy 219 flex-fuel cars 92–3 Fonda, Jane 114 Food and Agricultural Organization 148, 159 Ford, Bill 267 Ford, President Gerald 30, 115 Ford, Henry 73, 95, 195 Fordlandia 195 forest economics 149–50 forestry carbon credits 147 forests 147–51 Forrest, Andrew 199 Forward Contracts (Regulations) Act 1952 (US) 249 Forward Markets Commission (FMC) 249 Four Winds Capital Management 149, 159, 184 Franklin, Benjamin 157 Freese, Barbara 27 Friedland, Robert 199 Frost Fairs 127 fuel cell vehicles 53, 192–3 Futures Inc. 237 futures trading 235–6, 245, 247–50 gas 21–2 Gas Exporting Countries Forum (GECF) 61 n. 8 gasohol 73 gene shuffling 105 General Atlantic 267, 269 General Motors 53, 54, 191, 193 INDEX genetically modified organism (GMO) seeds 105–6 Glencore 199, 211 Global Forest Resources Assessment 2005 148 Global Initiatives173 n. 28 Global Positioning Systems (GPS) 191 global warming 24–6, 75 Globex 267 glycerin 82 Golder and Associates 206 Goldman 255, 260, 261 Goldman Sachs 57, 146, 254, 259 Goldman Sachs Commodity Index (GSCI) 240, 241 Goldstein Samuelson 245 Google 37, 38 Gore, Al 16 n. 5, 28, 38, 126, 129, 143 Government National Mortgage Association (Ginnie Mae) certificates 146 Grant, President Ulysses S. 214 Greenburg, Marty 269 greenhouse effect 131 greenhouse gas emissions 25, 131 see also carbon dioxide; nitrous oxide Greenspan, Alan 244 Gresham Investment Management 242, 243 Guggenheim brothers 197 Guttman, Lou 251, 255, 259 Hamanaka, Yasuo 246 Hanbury-Tension, Robin 146 Harding, President Warren 103 hedge funds 23640 Henry Moore Foundation 180, 181 Herfindahl, Orris 215, 226 n. 46 Heston, Charlton 15, n. 4 Hezbollah 46 Hi-Bred Corn Company 102 high fructose corn syrup (HFCS) 89–90 Highland Star 219 Hill, James Jerome 215 Homestead Act 1862 (US) 100 Honda 53 Howard, John 133, 171 n. 16 Hu Jintao, President 219 Hub, Henry 257 Humphries, Jon 181 Hunt Brothers 245 Hunter, Brian 246, 247 | 295 Hurricane Katrina 135 Hurricane Rita 134 Hussein, Saddam 48 hybrid cars 53 hydroelectric power 34 hydrogen cars 54 hypoxia zone 111 iAqua 165 IEA 32 IMF 16 n. 6 Inconvenient Truth, An 16 n. 5, 129 Indonesia palm oil 93–4 Integrated Gasification Combined Cycle (IGCC) 31–2 intelligent lighting 38 IntercontinentalExchange (ICE) 246, 261, 262, 265, 266, 267 Intergovernmental Council of Copper Exporting Countries (CIPEC) 203, 204 International Bauxite Association (IBA) 203 International Carbon Action Partnership (ICAP) 144 International Commercial Exchange (ICE) 273 n. 15 International Copper Cartel 201 International Energy Agency (IEA) 19, 25, 26, 40, 140–1, 153, 194 International Monetary Fund 57 International Panel on Climate Change (IPCC) (UN) 24, 132, 134, 147, 149, 170 n. 3 International Petroleum Exchange (IPE) 250, 256, 257, 262, 263, 264, 265 International Thermonuclear Experimental Reactor (ITER) 41 International Tin Council 203 International Treaty on Plant Genetic Resources for Food and Agriculture 107 Iowa Farm Bureau 36, 76, 155 Iowa Stored Energy Park 36 Japanese car market 18 Jardine Matheson 225 n. 40 Jarecki, Dr Henry 242, 243, 244 jatropha 57–9 Jefferson, Thomas 109 Jevons, William Stanley 20 Joint, Charles 181 296 | INDEX Joint Implementation (JI) 151 Jones, Paul Tudor 237, 238 Kabila, Joseph 210 Kanza, T.R. 211 Katanga of Congo 201, 225 n. 37 Kennecott Copper 199 Kennedy, Joseph (Joe) 264 Khosla Partners 38 Khosla, Vinod 37 Kitchen, Louise 258 Kleiner Perkins, Caufield & Byers 37 Kooyker, Willem 237, 238 Kovner, Bruce 237, 238 Krull, Pete 81 Kyoto Protocol 24, 27, 50, 140, 141, 142, 143, 147, 151, 169 n. 2, 194 clean development mechanism (CDM) 151 Lamkey, Kendall 112 Land and Water Resources, Inc. 155 Land Grant College Act (US) 101 Lange, Jessica 114 lead credits 172 n. 20 LED (light-emitting diodes) 38 Lehman brothers 241 Leiter, Joseph 245 Leopold II, King 210 Liebreich, Michael 39 Liffe 267 Lincoln, President Abraham 69, 100, 101, 119 n. 1 Lintner, Dr John 243 liquid coal 33–4 London Clearing House 263 London International Financial Futures and Options Exchange (Liffe) 262 London Metal Exchange (LME) 16 n. 10, 43, 196, 204, 212, 213, 246 Long Term Capital Management (LTCM) 247 Louisiana Light Sweet 253 Lourey, Richard 166, 168 Lovelock, James 131 Lyme Timber Company, The 149 Mackintosh, John 232, 234, 270 Madonna 6 malaria 156 Malthus, Thomas 130 manure lagoons 154–5 Mao, Chairman 210 Markowitz, Harry 243 Marks, Jan 268 Marks, Michel 252, 253, 254, 268 Marks, Rebecca 164, 165 Mars, Forrest E., Jr 60 Matheson, Hugh 225 n. 40 Matif 262 McCain, John 80 McDonalds 89 Megatons to Megawatts programme 42 Melamed, Leo 249–50, 264 Mendel, Gregor (Johann) 102, 122 n. 30 Merrill Lynch 241, 246 Mesa Water 163–4 methane 128, 131, 152, 154 methyl tertiary butyl ether (MTBE) 74 Microsoft 13 Midwestern Regional Greenhouse Gas Reduction Accord 143 milk 88–9 Milken, Michael 254 Millennium Ecosystem Assessment Board 156 Mittal, Laskma 212 Mobile 261 Mocatta Metals 242 Monsanto 106, 108 Montéon, Michael 199 Montgomery, David 138 Moor Capital 237 Moore, Henry 179, 182 Morgan, J.P. 246 Morgan Stanley 254, 255, 259, 260, 261 Muir, John 125 Mulholland, William 162 Murphy, Eddie 256 Murray Darling Basin 165–6 Musk, Elon 38 Nabisco 238–9 Nanosolar 38 Nasdaq 262 Nassar, President 210 National Aeronautics and Space Administration (NASA) 192 National Alcohol Programme (Brazil) 92 National Cattlemen’s Beef Association 82 National Commission on Supplies and Shortages (US) 7, 16 n. 5 National Corn Growers’ Association 80 National Energy Policy (US) 28 INDEX National Petroleum Council (NPC) 30, 50 National Security Space Office (NSSO) (US) 39 NCDEX 248, 249 Nelson, Willie 115 New Deal Farm Laws 103 New Deal for Agriculture 76, 89 New Energy Finance 39 New Farm and Forest Products Task Force 95 New York Board of Trade 240, 255 New York Cocoa Exchange 255 New York Coffee and Sugar Exchange 255 New York Cotton Exchange 237, 252, 255 New York Mercantile Exchange (Nymex) 43, 156, 246, 248, 251, 252, 253, 254, 255, 256, 257, 261, 262, 263, 264, 265, 266, 267, 268, 269 New York Metal Exchange 226 n. 42 New York Stock Exchange (NYSE) 198, 253, 255, 256, 264, 268, 270 Newman, Paul 265 Nicholson, Jack 162 nickel 217–18, 227 n. 50, 227 n. 52 nitrates 110–11 nitrogen oxide emissions 139 nitrous oxide 131, 139, 140, 152 Nixon, President 27, 30, 115, 231, 252 Noble Group 199 North, John Thomas 198, 199, 209 Norton, Gale 163 nuclear energy 34 nuclear power 21, 39–44 Nuexco Trading Corporation 42 Nybot 255, 256, 265 Obama, Barack 79, 80, 143 obesity 121 n. 19 O’Connor, Edmund 231 O’Connor, William 231 OECD 158, 159 oil 44–53 energy content 51–2 palm 93 prices 8–9, 10, 52–3 sands 49–50 shale 50–1, 64 n. 33 shocks 5, 7 soya 82 trading 250–5, 266 Oliver, Jamie 86 onion futures trading 245 | 297 Ontario Teachers’ Fund 244, 272 n. 8 Organization of the Petroleum Exporting Countries (Opec) 4, 9, 10, 22, 44–7, 203, 204, 251, 254 over-the-counter (OTC) trading 16 n. 8, 254–5 Owens Valley rape (1908) 162 Pachauri, Dr Rajendra K. 59 Page, Larry 38 Paley Commission 8 palm oil 93 Palmer, Fred 62 n. 14 Parthenon Capital 156, 267 PayPal 38 Peadon, Brian 165 perfluorocarbon 131 PGGM 244 Phaunos Timber Fund 149 Phelps Dodge 199 Phibro 254 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
In part, we try to rely on negative feedback systems—we seek to buy when relative prices are too low and to sell when relative prices are too high—on the assumption that prices out of line will return to equilibrium levels. As you know from my papers, substitution is a powerful force in markets; that is, assets with similar risk characteristics will be priced to provide similar expected returns.” To illustrate this point, Scholes gave an actual example of a Platinum Grove transaction involving CTAs—Commodity Trading Advisors who trade in the futures markets and are registered with the Commodity Futures Trading Commission. In 2006, the Bank of Japan appeared close to ending its long-held practice of a continuously easing monetary policy and maintaining interest rates close to zero. Many CTAs saw this development as an opportunity to sell—to go short—futures contracts on Japanese bonds maturing ten years out.
See U.S. bonds Bounded rationality, 15–16 BP-Amoco, 185–186, 195 Brainard, William, 150, 151 Bronx School of Science, 58, 59 Brown, Keith, 22 Brunnermeier, Markus, 29 Bubbles high-tech (1990s), 145–147, 245 as macro-inefficiency of markets, 41 NASDAQ bubble (1998–2000), 29–30 See also Stocks Buffett, Warren, 170 Buyers f inancial market transactions by, 113–114 rational-agent model on, 4 –5, 6, 10, 15–16, 30, 71 See also Behavioral Finance; Capital Ideas; Investor behavior/choices CalPERS, 176 Campbell, John, 6, 70, 172 “Capital Asset Prices: A Theory of Market Equilibrium Under Conditions of Risk” (Sharpe), 91 Capital Asset Pricing Model (CAPM) alpha representation of predictions of, 38 current investor opinion about, 172–173, 177 difference between reality and idealized, 10 estimating, 92 on expected returns, 92–93 inf luence on BGI strategies by, 143–144 bern_z04bindex.qxd 4/3/07 8:20 AM Page 267 Index informed manner of investment in, 19 Leibowitz’s work on, 196–197, 206–207 market ineff iciency explanation of discrepancy of, 132 Markowitz’s examination of assumptions of, 104 –108 origins and development of, 100, 171–172 portable alpha development of, 176, 182–195 rational expectations similarities to, 67 Sharpe’s algebraic specif ication of, 167–168 Sharpe’s views on, 91–96 Sharpe-Treynor-Lintner-Mossin original, 165 Shiller on market portfolio mandated by, 84, 87 Treynor’s pioneering work on, 24 unworldly nature of, 48 See also Finance theories Capital Ideas arbitrage issue of, 25–28 Behavioral Finance impact on, 30 –31, 32 continuing evolution and impact of, 238–239, 246 described, 3–4 liquid markets focus of, 158 Lo’s work on, 240 –241 LTCM’s revelations about, 78–79 Merton’s work/comments on, 43, 47–48, 56, 240 Notes on Usage, xxii overriding assumption of, 4 rational model supporting, 10 267 Sharpe’s characterization of, 93–94 Shiller’s analytical framework using, 43, 65– 66, 240 –241 Swensen’s application of, 154 –164 See also Buyers; Finance theories; Neoclassical theory; Sellers Capital Ideas ( Bernstein) on America’s financial markets, 241 CAPM described in, 165 on eff icient markets, 154 f inancial market transformation since, 48–49, 238–239 on Leibowitz’s celebration of Sharpe, 212–213 on market eff iciency, 220 Merton interviewed for, 47 on noise traders, 19 rational-agent model failures impact on, 6, 30 risk management applications discussed in, 237 Samuelson on lack of easy pickings in, 38 Capital market. See Market Carnegie Foundation, 201 Carter, Andrew, 200 Case, Karl, 82 CFA Institute, 213 Chicago Mercantile Exchange, 85 Clarke, Roger, 106 Cohn, Richard, 29 Commodity Futures Trading Commission, 119 Corzine, Jon, 216 Country A and B industry swap (Merton), 55–56, 84 bern_z04bindex.qxd 4/3/07 8:20 AM 268 Page 268 INDEX CREF portfolio, 200 –202 CTAs (Commodity Trading Advisors), 119–120 Damsma, Marvin alpha-beta separation strategies of, 184 –192, 193–194, 238 Amoco pension fund developed by, 182–183 Amoco portfolio strategies used by, 178 BP-Amoco f inancing strategies used by, 195 on new strategic partnership structure, 186 on value of separating beta and alpha returns, 175–176 Darwin, Charles, 62 “Dayenu” (Passover prayer), 212–213 Day trading, 56 Debreu, Gérard, 94 De Finetti, Bruno, 108–109 “De Finetti Scoops Markowitz” (Markowitz), 108 Dimson, Elroy, 188 Diversif ication CAPM on, 92–93 as control over volatility, 221 Markowitz’s key notion of, 92, 154, 155, 159, 210 risk management using, 154, 155, 159, 173 Samuelson’s support of, 42 Dividends Shiller and Jung’s study on ratio of price and, 74 –76 Shiller’s excess volatility applied to, 79–80 Dodd, David, 147 Dragon risks, 210, 211, 225 Dunn, Patricia, 137, 141–142 Earnings per share, investor fetish about, 41–42 Economics described, 3 Marshall’s inf luence on study of, 3–4 See also Finance theories Eff icient Market Hypothesis arbitrage assumption in, 25–28 implications of Behavioral Finance reality for, 13–14 informed manner of investment in, 19 Lo’s study of, 60 – 62 rational expectations similarities to, 67 Samuelson on alpha as vindication of, 40 Shiller’s micro-eff iciency studies and, 72–76 unworldly nature of, 48 Endogenous Economic Fluctuations: Studies in the Theory of Rational Beliefs ( Kurz), 71 Endogenous volatility, 72 Equilibrium Black-Litterman model on, 215, 216, 220, 224 –228, 244 Litterman’s description of, 215, 218 market eff iciency and role of, 220, 234 Equilibrium models, 100 Equity trading, change in patterns of, 63 ETFs (exchange-traded funds), 142 bern_z04bindex.qxd 4/3/07 8:20 AM Page 269 Index Excess volatility phenomenon, 68–71, 79–80 Exogenous volatility, 72 Failure of invariance Kahneman’s def inition of, 6–8 Thaler’s house money effect example of, 8, 14 Fama, Eugene on Behavioral Finance, 30 CAPM work by, 165–166, 171 eff icient market as def ined by, 79 eff icient market theory by, 69 on premium for holding stocks, 94 on relevant information, 80 Family balance sheets, 82–84 Federal Reserve Bank of New York, 76 Federal Reserve Regulation T, 105 Fidelity Research Institute, 22 Fidelity’s Magellan Fund (1977–1990), 20 50-50 choice experiment on decisions for, 9 TIAA-CREF use of, 9–10 Finance theories BGI strategies and role of, 143–145 Black on putting trust in, 94 Eff icient Market Hypothesis, 13–14, 25–28, 40, 48, 60 – 62, 67, 72–76 Heisenberg Uncertainty Principle, 244 latest developments in, 237–238 Modern Portfolio Theory, 30, 154 neoclassical, 26, 56, 57, 100 options pricing theory, 238 Portfolio Selection theory, 100, 103, 105–108 risk management as heart of, 237 269 Sharpe on, 144 state-preference, 94 –96 theories of choice, 4 –8 See also Behavioral Finance; Capital Asset Pricing Model (CAPM); Capital Ideas; Economics Financial Analysts Journal, 24, 104, 196, 207 Financial Engines model, 97–98, 162 Financial market eff iciency Black-Litterman model based on, 220 Capital Ideas on, 220 drive toward ever-greater, 243–244 equilibrium role in, 220, 234 Fama’s def inition of, 79 Litterman’s position on, 219–223 Swensen on dilemmas of, 164 Financial markets Adaptive Markets Hypothesis notion of, 62– 64 as “dazzling creations,” xvi economic role played by, 113 Fama’s def inition of eff icient, 79 globalization of, 241–243 ineff iciency of, 28–30, 41 Shiller on creating macro, 86–87 Shiller on excess volatility phenomenon of, 68–71, 79–80 Shiller on micro-eff iciency of, 72–76 Shiller on REITs (real estate investment trusts), 84 –86 Swensen on dilemmas of eff icient, 164 transactions in the, 113–114 using simulations of, 101–104 See also Pricing bern_z04bindex.qxd 270 4/3/07 8:20 AM Page 270 INDEX Financial markets ineff iciency irrational pricing and, 28–30 macro, 41 memorable demonstrations of, 28–30 Fixed-income bond management, 200 Forecasting versus risk transfer, 123–124 Fortune’s ten most admired companies, 172 Foundation Trilogy, The (Asimov), 59 Fouse, Bill, 128, 129, 130, 131, 133, 146 Framing house money effect, 8, 14 retirement funds experiment on, 8–10 French, Kenneth, 165–166, 171 Fuller & Thaler investment strategies used at, 16–18 sector of choice by, 19 smaller-capitalizations taken by, 25 success of, 20 See also Thaler, Richard Fuller, Russell, 16 Functional and Structural Finance (Merton and Bodie), 50 Fundamental Law of Active Management, 138, 139, 140 Futures market, 119–120 The General Theory of Employment, Interest, and Money ( Keynes), 3 Global f inancial markets, 241–243 Global Investment Strategies, 216 Goldman Sachs active management approach by, 224 –228, 229, 233 alpha focus of, 229–232 Black’s move to, 214 –215 Litterman’s career at, 214 –215, 216–218 portfolio performance of, 216–217, 221–223, 230, 233–234 transaction cost management by, 223–224 volatility focus by, 218, 223 See also Black-Litterman model Gottlieb, Jason, 218–219 Gould, Jeffrey, 31 Goyal, Amit, 13 Graham & Dodd awards, 196 Graham, Benjamin, 147 Grantham, Jeremy, 221 Grantham, Mayo, Van Otterloo, 221 Grauer, Fred on BGI’s financial strategies, 140–141 on BGI’s personnel policies, 141–143 made WFIA CEO, 130 –131 Merrill Lynch experience by, 131 on Stagecoach Fund, 132–133 on WFIA low-cost index funds strategies, 134 –136 See also Wells Fargo Investment Advisors ( WFIA) “A Great Company Can Be a Great Investment” (Anderson and Smith), 172 Grinold, Richard, 137, 138, 139, 140 Gross, Bill alpha engine strategy used by, 185–186 BondsPLUS product developed by, 180 –182 separation of alpha-beta management by, 178, 238 See also StocksPLUS Grossman, Blake, 130, 131, 139, 144 –145, 147, 244 bern_z04bindex.qxd 4/3/07 8:20 AM Page 271 Index Grossman, Sanford, 32–33, 114 –115 GTAA (global tactical asset allocation), 216 Hamilton, Booz Allen, 171 Harlow-Brown paradigm, 22–23 Harlow, W.
Derivatives Markets by David Goldenberg
Black-Scholes formula, Brownian motion, capital asset pricing model, commodity trading advisor, compound rate of return, conceptual framework, correlation coefficient, Credit Default Swap, discounted cash flows, discrete time, diversification, diversified portfolio, en.wikipedia.org, financial innovation, fudge factor, implied volatility, incomplete markets, interest rate derivative, interest rate swap, law of one price, locking in a profit, London Interbank Offered Rate, Louis Bachelier, margin call, market microstructure, martingale, Myron Scholes, Norbert Wiener, Paul Samuelson, price mechanism, random walk, reserve currency, risk/return, riskless arbitrage, Sharpe ratio, short selling, stochastic process, stochastic volatility, time value of money, transaction costs, volatility smile, Wiener process, yield curve, zero-coupon bond, zero-sum game
A commodity pool does just that; it pools the funds of a number of persons for trading futures contracts, or possibly to invest in another commodity pool. A commodity pool is like a mutual fund in its pooling function. By pooling funds, a commodity pool or mutual fund provides access to investors that they would otherwise not have on their own. For those who want advice, there are also Commodity Trading Advisors (CTAs) who are individuals or organizations which, for compensation or profit, advise others, directly or indirectly, as to the value of or the advisability of buying or selling futures contracts. Providing advice indirectly includes exercising trading authority over a customer’s account as well as giving advice through written publications or other media. The analogy here is to a managed investment account.
INDEX adjusted intrinsic value (AIV) 381, 396–7, 406; for European call, definition of 375–6 adjusted time premium (ATP) 396, 397 All-or-None (AON) orders 214, 215 American options 328 annualized dividend yields 88 anticipatory selling 339 anticipatory buying 339–40 arbitrage: arbitrage definitions 100–2; arbitrage opportunities 75, 77–8, 100–1, 103, 167, 172, 188, 192, 208, 260, 292, 450, 474; pricing by 464; risk-free arbitrage 100, 373; risky arbitrage 100–1 arithmetic Brownian motion (ABM) model of prices: equivalent martingale measures (EMMs) 530–1; option pricing in continuous time 540–1 back stub period 294 backwardation, contango and 198–9 Bank of International Settlements (BIS) 246 basic American call (put) option pricing model 332–4 basic European option pricing model, interpretation of 397–8 basic (naked) strategies 347–63 basis risk 223, 237, 238; cross hedging and 244; spot price risk and 178–82 bid-asked spread, trading within 133–4 bid prices 127, 134, 136, 161, 336 binomial option pricing model (BOPM) 436–48, 467–8, 475–6, 485–506; arbitrage, pricing by 464; binomial process (any N) 439; binomial process (N=1) 448; combination function C(N,j) 443–4; concept checks: algorithm for determination of B, verification of 485; binomial completeness, rule of thumb on 449; binomial model, time modeling in 438; calculation of combination function C(N,j) 444; hedge ratio interpretation 482; hedging a European call option in BOPM (N=2) 477; solution to 505; option price behavior (N=2) 477; solution to 505–6; path 2 contribution analysis 496; path 3 contribution analysis solution to 506; path structures of binomial process, working with 442; solution to 472; price paths for N-period binomial model 442; solution to 471–2; pricing terminal options 446; underlying stock price uncertainty modeling 438–40; valuation of option (time=0) using RNVR 490; verification of numerical example (N=2) numbers 489; verification of option values (N=2) in comparison with replicating portfolio method 493; European call option valuation at expiration 446; exercises for learning development of 501–5; fundamental theorem of asset pricing (FTAP2) 490; hedging a European call option (N=2) 477–85; implementation (N=2) 485–90; joint probability of given path 444; key concepts 501; logic of BOPM (N=1) and its drivers 463; multi-period model (N >1), path integral approach 493–500; numerical example (N=2) 487–90; option price behavior (N=2) 476; option valuation for 445–8; path structure of binomial process 440–2, 442–4; paths, thinking of BOPM in terms of 493–9; price paths, total number of 440–2; price paths ending at specific terminal price, total number of 442–4; pricing option at expiration 445–6; pricing option currently (time t=0) 446–8; proof that the BOPM (N=1) is complete, three parts of 517; proof of model for general N 499–500; replication, no-arbitrage and 464; risk-neutral valuation, exogenous variables and 615; risk-neutral valuation relationship: derivation as 467–8; interpretation of 468; as risk-neutral valuation relationship (RNVR) formula (N > 1) 490–3; as set of BOPMs (N=1) 491; stock price behavior (N=2) 475–6; stock price evolution: for binomial process (N=2) 440; for N-period binomial process, summary of 444–5; stock price tree (N=2) 488; stock price uncertainty 439; time in discrete time framework, modeling of 437–8; trinomial model (three stock outcomes) 464 Black-Scholes option pricing model: option pricing in continuous time 566–85, 588–9; from Bachelier 571–83; historical volatility estimator method 583–4; implied volatility estimator method 585; importance of 588–9; parsimony of 588; potential for 589; reduction of GBM to ABM with drift 567–70; risk-neutral transition density functions, generation of unknown from knowns 570–1; volatility estimation in Black-Scholes model 583–5; risk-neutral valuation, contribution to 598, 601; valuation of forward contracts in continuous time (assets with a dividend yield) 88 block trade eligibility 214, 228 block trade minimum 214, 228 Bond Equation 552, 554–5 boundaries, absorption of 541 Brownian motion paths, non-smoothness of 560; see also arithmetic Brownian motion (ABM); geometric Brownian motion (GBM) Buffett, Warren 252 buyers and sellers, matching of 125, 126–7 buying back stock 339 buying forward 7–8 calendar spreads 199 ‘calling away’ of stock 422 capital asset pricing model (CAPM) 447, 605 capital gains: effect on stock prices 98–9; capital gains process 98–9, 111 carrying charge hedging 188–93; convergence, implications for 189; equilibrium (no-arbitrage) in full carrying charge market 190–3; overall profits on 189 cash and carry transactions 5 cash commodity prices 35 cash flows: for annual rate swap 302; in non-intermediated swaps 282–4 cash settlement vs. commodity settlement 157; implications from problem of 157 CBOE (Chicago Board Options Exchange) 324–5, 334; asked price entries 335, 336; bid entries 335, 336; equity option specifications 343; exchange-traded option contracts 325; last sale entries 335, 336; Merck call options and price quotes 334–7; mini equity option specifications 344; net entries 335, 336; open interest entries 335, 336; volume entries 335, 336 certainty equivalent (CE): certainty equivalent cash flow 397; risk-neutral valuation and 603, 604 Clearing Houses: counterparty risk 140; futures exchange and 140; guarantor of trades 140; intermediation by 14–15; membership 140; operations and functions 139–53 clearing of trades 126; process of, offsetting futures trades and 141–4 close of market 145 clustering (persistence), volatility and 585 combination function C(N,j) 443–4 combinations of positions 50 combining charts to see profits from hedged positions 54–5 commentary on financial futures contracts price quotes 216–17 commitment prices 41 commitment to buy 67 commodities, ways to buy and sell 5 commodity forward contracts: paying fixed and receiving floating in 276; as single period swaps 275–6 Commodity Futures Trading Commission (CFTC) 123, 124, 125, 140, 215, 229 Commodity Pool Operators (CPOs) 123 Commodity Trading Advisors (CTAs) 123 complete markets 449 complete risk-expected return analysis of riskless hedge (BOPM, N=1) 607–18; direct calculation of ?c 612–15; direct calculation of ?s 611–12; expected return of hedge portfolio 616–18; hedge portfolio, percentage returns for 616–18; perfect positive correlation, statistics result for 609–11; volatility of hedge portfolio 608–11 concept checks: binomial option pricing model (BOPM): algorithm for determination of B, verification of 485; binomial completeness, rule of thumb on 449; binomial model, time modeling in 438; calculation of combination function C(N,j) 444; hedge ratio interpretation 482; hedging a European call option in BOPM (N=2) 477; option price behavior (N=2) 477; solution to 505–6; path 2 contribution analysis 496; path 3 contribution analysis: solution to 506; path structure of binomial process, working with 442; solution to 472; price paths for N-period binomial model 442; solution to 471–2; pricing terminal options 446; underlying stock price uncertainty modeling 438; valuation of option (time=0) using RNVR 490; verification of numerical example (N=2) numbers 489; verification of option values (N=2) in comparison with replicating portfolio method 493; equivalent martingale measures (EMMs): contingent claim pricing, working with 514; martingale condition, calculation of 525; option pricing, working with 514; two period investment strategy under EMM, proof for (t=0) 521; solution to 538; financial futures contracts: backwardation and contango, markets in 224; bank borrowing in spot Eurodollar (ED) market 250; ‘buying’ and ‘selling’ Eurodollar (ED) futures 256; calculation of adjusted hedge ratios 245; solution to 269; calculation of optimal (risk-minimizing) hedge ratio 240; cash settlement and effective price on S&P 500 spot index units 234; solution to 269; exchange rate risk, currency positions and 218; solution to 268; foreign exchange (FX) risk and jet fuel market 219; solution to 268–9; underlying spot 3-month Eurodollar (ED) time deposit 261; solution to 270; forward market contracting: controlling for counterparty risk 12–13; exploration of forward rates in long-term mortgage market 9–10; exploration of spot rates in long-term mortgage market 11; solution to 29; intermediation by Clearing House 15–16; solution to 29–30; spot markets, dealing with price quotes in 6–7; futures market contracting: price quotes in futures markets 19; hedging a European call option in BOPM (N=2): value confirmation 485; hedging with forward contracts: charting payoff to long forward position 39; solution to 62; charting payoff to short forward position 42; solution to 62; charting profits to fully unhedged position 45; solution to 63; charting profits to long spot position sold forward 49; payoff per share to long forward position 39; solution to 61; payoff per share to short forward position 42; profits to fully naked (unhedged) short forward position 50; solution to 64; profits to long spot position sold forward 48–9; profits to naked long spot position 45; wheat price volatility, dealing with 36; hedging with futures contracts: bond equivalent yield (BEY) of actual T-bill 167; solution to 207–8; construction of risk-free arb if r > 0 with no dividends 173; solution to 208; effect of narrowing basis in traditional short hedge 178; solution to 208–9; effect of widening basis in traditional short hedge 176; failure of traditional hedging 184; solution to 209; profits in traditional short hedge and the basis 172; verification that arb is arb without non-interest carrying charges and is riskless 192–3; solution to 209; verification of no current cost in arb 190; verification of riskless arb 191; interest-rate swaps: calculation of implied forward rates (IFRs) 310; solution to 319; graphical representation of swap’s cash flows 283; solution to 318; paying fixed in an interest rate derivative (IRD) 279; solution to 317–18; receiving variable in an interest rate derivative (IRD) 280; strip of forward contracts, short’s position in 278; solution to 317; swapping fixed for floating payments 276; solution to 317; market organization for futures contracts: Globex LOB trading, practicalities in 135–6; solution to 160–1; limit order execution 132; market order with protection, processing with CME Globex 128–9; solution to 160; market price best bids below sell market orders with and without protection, results?
298–9 intermediate settlement prices 154 intermediation 13–14, 14–15 International Monetary Fund (IMF) 246 intrinsic value 326, 330, 333, 337 Introducing Broker (IB) 123 investor’s accounts, tracking equity in 151–3 invoice price on delivery 153–6 Itô’s Lemma 562–6 joint probability of given path 444 JPY/USD futures 213–15 key concepts: binomial option pricing model (BOPM) 501; equivalent martingale measures (EMMs) 537; financial futures contracts 265–6; hedging with forward contracts 56; hedging with futures contracts 204; interest-rate swaps 315; market organization for futures contracts 158; model-based option pricing (MBOP) 466; option pricing in continuous time 590; option trading strategies 364, 431; options markets 341; rational option pricing (ROP) 408–9; risk-neutral valuation 634; spot, forward, and futures contracting 27; valuation of forward contracts (assets with dividend yield) 116; valuation of forward contracts (assets without dividend yield) 83 last trade date/time view calendar 214, 228 law of one price (LOP): model-based option pricing (MBOP) 452; risk-neutral valuation 597; valuation of forward contracts (assets without dividend yield) 77 LBAC (lower bound for American call option on underlying, no dividends) 374–5 LBACD (lower bound for American call option on underlying, continuous dividends) 383–5; call on underlier with continuous, proportional dividends over life of option 384–5; call on underlier with no dividends over life of option 384 LBAP (lower bound for American put option on underlying, no dividends) 378–80; intrinsic value lower bound for American put, example of 379–80 LBAPD (lower bound for American put option on underlying, continuous dividends) 387–8 LBEC (lower bound for European call option on underlying, no dividends) 375–8; implications of 377–8 LBECD (lower bound for European call option on underlying, continuous dividends) 382–3 LBEP (lower bound for European put option on underlying, no dividends) 380–1; adjusted intrinsic value (AIV) for European put, definition of 380–1 LBEPD (lower bound for European put option on underlying, continuous dividends) 386–7 learning about options, framework for 326–7 leverage, options and 327 LIBID (London Interbank Bid Rate) 249–50, 252 LIBOR (London Interbank Offered Rate) 249, 250–4, 262, 263–4; Federal Funds (FF) vs. 251–2; interest-rate swaps 274–5, 278, 282, 293, 297, 303, 304, 306, 307, 309–10, 311–13; yield curve (spot rates) 304 Limit Bid (LBid) 129 Limit Offer (LOff) 129 limit order book (LOB) 130–1; depth in 131–4; Globex LOB trading, practicalities in 135–6 limit orders 129–30 liquidity: enablement of 16; financial futures contracts and 220, 222, 231, 237, 252, 258; liquidity options 333 lock-in characteristics 220, 233 locked-in prices 12 Log Bond equation: option pricing in continuous time 552–3; valuation of forward contracts (assets with dividend yield) 96 long a European call option on the underlying 351–5; economic characteristics 353 long a European put option on the underlying 348, 357–9; economic characteristics 358 long a zero-coupon riskless bond and hold to maturity 348, 360–2; economic characteristic 361 long and short positions, identification of 339–40 long call positions, difference between long underlying positions and 354 long forward positions, payoff to 37–9 long positions in options markets 339–40 long spot and long forward positions, difference between payoffs to 76–7 long the underlying 347–9; economic characteristics 349 long vs. short positions: hedging with futures contracts 164; options markets 339–40 Lufthansa 217–20 mapping out prices, spot, forward, and futures contracting 20–6 margin calls 145, 147–8, 152 marginal carrying charges 188 marginal rate of substitution (MRS) 604, 605 margins (performance bonds) 144–5, 148; initial margin 145; maintenance margin 145 market completeness 598 market levels 11 market orders 127–9 market organization for futures contracts 121–61; bid-asked spread, trading within 133–4; bid prices 127; buyers and sellers, matching of 125, 126–7; cash settlement vs. commodity settlement 157; implications from problem of 157; Clearing Houses: counterparty risk 140; futures exchange and 140; guarantor of trades 140; membership 140; operations and functions 139–53; clearing of trades 126; clearing process, offsetting futures trades and 141–4; close of market 145; Commodity Futures Trading Commission (CFTC) 123, 124, 125, 140, 215, 229; Commodity Pool Operators (CPOs) 123; Commodity Trading Advisors (CTAs) 123; concept checks: Globex LOB trading, practicalities in 135–6; solution to 160–1; limit order execution 132; market order with protection, processing with CME Globex 128–9; solution to 160; market price best bids below sell market orders with and without protection, results? 130; solution to 160; trading crude oil futures 147; solution to 161; convergence, forcing of 157; daily settlement process 144–51, 153; demutualization 139–40; depth in limit order book (LOB) 131–4; effective price and invoice price on delivery 153–6; equity in customer’s account 145, 148; exchange membership 139–40; exercises for learning development of 158–9; floor-brokers 140; floor-traders 140; Futures Commission Merchant (FCM) 122, 123, 124, 125, 137, 140; futures contracts: ‘buying’ and ‘selling’ of 126–7; daily value of 146; differences between forward contracts and 122; futures price and 127; market participants 122–5; futures trading: cash flow implications of 144; daily settlement, perspectives on 144; delivery obligations 142; offsetting trades 142–4; phases of 125–6; Globex and Globex LOB 134–6; Globex trades, rule for recording of 135; guaranteeing futures obligations 139–41; intermediate settlement prices 154; Introducing Broker (IB) 123; investor’s accounts, tracking equity in 151–3; invoice price on delivery 153–6; key concepts 158; Limit Bid (LBid) 129; Limit Offer (LOff) 129; limit order book (LOB) 130–1; depth in 131–4; limit orders 129–30; margin calls 145, 147–8, 152; margins (performance bonds) 144–5, 148; initial margin 145; maintenance margin 145; market orders 127–9; market with protection market orders (CME Group) 128; marking to market 144–51; daily unrealized gains and losses, adjustments for 146; matching trades 139–41; National Futures Association (NFA) 123; offset vs. delivery 155–6; long offsets futures position just prior to expiration 156; long trader takes delivery of underlying commodity 155–6; offsetting futures trades 141–4; open access trading 140; open contract 145; open interest 145; open outcry pit trades: CME Clearing House requirements for 137–9; trades entry into clearing system 138–9; trading cards, submission of 139; order execution 125–6; futures contract definition and 126; order submission 125–6; orders, types of 127–34; overall profits (and losses) 144, 150, 151, 153, 156, 157; participants in futures market 122–5; performance bonds (margins) 144–5, 148; pit trading, order flow process and 136–9; protection, market orders with 127–9; realization of daily value 149; recontracting futures positions 149, 151; Registered Commodity Representatives (RCRs) 122–3; segregated consumer funds 123–5; settlement prices 145–6, 151; settlement variation 146; short positions, assumption of 147–8; tracking equity in investor’s account 151–3; trading futures contracts, questions on organizational structures for 141 market price of risk (MPR): equivalent martingale measures (EMMs) and 605–6; risk-neutral valuation and 624 market risk 225–6 market with protection market orders (CME Group) 128 marking to market 144–51; daily unrealized gains and losses, adjustments for 146 martingale properties 533–6 matching principle 300 matching trades 139–41 mathematical modeling 596–7 maturity dates 328 Merck stock price fluctuations 346–7 minimum price increment 214, 215 minimum variance hedging 185–8; estimation of risk minimization hedge ratio 187–8; OLS regression 187–8; risk minimization hedge ratio, derivation of 186–7 model-based option pricing (MBOP) 398, 406, 455; alternative option pricing techniques 464–5; complete markets 449; European call option, synthesis of 453–64; hedge ratio and dollar bond position, definition of (step 2) 455; implications of replication (step 4) 462–4; parameterization (step 1) 454; replicating portfolio, construction of (step 3) 456–62; down-state, replication in 457; hedge ratio, magnitude of 461–2; sign of B 459–60; solving equations for ?
Stock Market Wizards: Interviews With America's Top Stock Traders by Jack D. Schwager
Asian financial crisis, banking crisis, barriers to entry, beat the dealer, Black-Scholes formula, commodity trading advisor, computer vision, East Village, Edward Thorp, financial independence, fixed income, implied volatility, index fund, Jeff Bezos, John Meriwether, John von Neumann, locking in a profit, Long Term Capital Management, margin call, money market fund, Myron Scholes, paper trading, passive investing, pattern recognition, random walk, risk tolerance, risk-adjusted returns, short selling, Silicon Valley, statistical arbitrage, the scientific method, transaction costs, Y2K
I get that information off the floors in the case of stocks and stock options and from the banks in the case of currencies. How do you turn information like who is doing what into an alternative option pricing model? The best example I can think of involves the gold market rather than stocks. Back in 1993, after a thirteen-year slide, gold rebounded above the psychologically critical $400 level. A lot of the commodity trading advisors [money managers in the futures markets, called CTAs for short], who are mostly trend followers, jumped in on long side of gold, assuming that the long-term downtrend had been reversed. Most of these people use models that will stop out or reverse their long positions if prices go down by a certain amount. Because of the large num• her of CTAs in this trade and their stop-loss style of trading, I felt that a price decline could trigger a domino-effect selling wave.
Shaw Financial Products, 259 DESoFT, 259 diagnostic capability, 29 discounts, 140-41, 157,213,250-52,253,315 Columbia Pictures, 100 commissions, 48, 134, 148, 175, 203, 213, 214, Disney, 93-94 diversification, 33, 34, 51, 86-87, 93, 306, 319, 321 dividend capture strategy, 140-41 328 Commodities Corporation, 191 dividends, 13, 133, 140-41 dog races, 129-30 commodity markets, 199,231 commodity trading advisors (CTAs), 231 Commodore Computer, 13-14 Dow Jones Industrial Average, 47, 109 Compaq, 37 competition, 73, 145, 229 earnings: compilers, 258 Complete Guide to the Futures Market, A (Schwager), 327n compliance departments, 11 1—13 Comp USA, 90 computers: market for, 37-38, 39, 43-44, 86-88, 90, 139, 244, 269,279-80 parallel-processor, 258-63 research based on, 77, 81, 129-30, 157-58, 181, 194-95, 197, 202-3, 204, 208, 215, 255, 274, 319 software for, 26, 86-88, 90, 139, 215 see also Internet confirmation statements, 16 conjunction trades, 109 consolidates, 24M4 contingency plans, 184, 186, 187 Cook, Mark D., 95-126 background of, 95-97 fund managed by, 97 losses of, 101-7, 108, 122-24, 126, 314 as novice trader, 97-107 profits of, 96, 97-98, 101, 112-19, 125 strategy of, 97, 107-26, 299, 301, 303, 304, 305, 308,312,314,315,316,319 Cook, Martha, 104-5, 123-24 Dutch tulip craze, 64 fraud, 91-92 Friess Associates, 58-59, 67, 73 front-running, 79-80 futures, 144-45, 191-92, 199, 202-3 see also options Galante, Dana, 75-94 fund managed by, 76, 85-89, 93 losses of, 86-88 as novice trader, 76, 77-85 profits of, 75-76, 93-94 strategy of, 75-77, 81, 85-94, 311, 324 as woman, 77, 88-89 gambling, 90, 225-26, 255-56, 266-67 Gap, 12,67-68 Gatev, Evan G., 256»i expectations of, 15, 16, 40-41, 49, 61, 63, 86-88, 138-39, 141,215-17,279,286-87 loss of, 81,89-90, 92,94, 138-39, 215-17, 279 General Electric, 136, 137 General Motors, 145 Goetzmann, William N., 256n price vs., 21, 22, 43-44, 52, 58, 59, 60, 62, 65, 66, gold prices, 102, 231-32, 310 72-73, 79, 81, 89, 92, 94, 149, 152, 154-55, 158, 164, 165, 166-67, 172,216,306,320,321, 325 quarterly reports of, 40, 60, 62-63, 84, 138-39, 141,215-17,286-87 "earnings surprise," 216 Goldwyn, Samuel, 118 "greater fool" premise, 34, 36 Greenberg, Ace, 136, 138 163, 165,318 Institutional Investor All-Star analyst team, 32 150, 151, 162, 185, 186, 218, 229-30, 240, 249, 259, 262, 279-81, 310-11 inventory, 90, 92 Investing Championship, U.S., I l l , 118, 170 investment: diversification of, 33, 34, 51, 86-87, 93, 306, 319, 321 institutional, 192,318 long-term, 40, 42, 157, 164, 234-35 return on, 46, 55, 59, 75-76, 97, 170, 243 size of, 32-33, 52 speculation vs., 20, 84-85, 177, 225-26 sea also portfolios Investor's Business Daily, 152 Istanbul Stock Exchange, 148, 1 53-54, 156 January effect, 198-99 Janus 20, 39-40 Japan, 7-8,64,223,303,310 J, Crew, 10 J.
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
Hence, the sieve we use focuses on disequilibrium conditions in equities, currencies, interest rates, and derivatives thereof, as well as policy-generated situations. We look for situations where asset prices do not correctly reflect macro fundamentals or do not identify and anticipate structural shifts in the economy. This approach is to be distinguished from more systematic approaches, which rely heavily on quantitative models based on historical data to take diversified positions in a large number of markets (commodity trading advisors, for example). Although these quantitative strategies may trade the same markets (currencies, fixed income, equity indices, etc.), the emphasis is on extracting technical signals rather than developing and expressing fundamental macro views. Our process essentially commences with a detailed and thorough analysis of the data, an analysis that can be either quantitative or qualitative.
However, given how much the commodity markets have evolved in the last five years in terms of product breadth, volume, and other factors, it is difficult to give a precise answer because these distributions are changing along with changes in the types of players in the market. The increased speculative flows in recent years have definitely changed the level of volatility, both from long-only players entering the market through indices and other means, and from the growing size and influence of CTA’s (commodity trading advisors). While some question whether speculative flows are fundamental to the functioning of the market, for me, it is clear that such flows have had a major impact. Speculative flows change the term structure of a market, which, in turn, changes the reaction function of a producer or storage operator. Commodity markets now tend to gap more quickly, showing evidence of what I call “single point volatility.”
Getting a Job in Hedge Funds: An Inside Look at How Funds Hire by Adam Zoia, Aaron Finkel
backtesting, barriers to entry, collateralized debt obligation, commodity trading advisor, Credit Default Swap, credit default swaps / collateralized debt obligations, discounted cash flows, family office, fixed income, high net worth, interest rate derivative, interest rate swap, Long Term Capital Management, merger arbitrage, offshore financial centre, random walk, Renaissance Technologies, risk-adjusted returns, rolodex, short selling, side project, statistical arbitrage, stocks for the long run, systematic trading, unpaid internship, value at risk, yield curve, yield management
Note: These funds seek to profit from changes in global economies, which are typically triggered by changes in government policy. These changes can affect interest rates and in turn may impact currency, stock, and bond markets. Global macro funds depend on their own fundamental macroeconomic research and often employ a top-down global approach. Managed Futures This strategy invests in listed financial and commodity futures markets and currency markets around the world. The managers are usually referred to as Commodity Trading Advisors (CTAs). Trading disciplines are generally systematic or discretionary. Systematic traders tend to use price- and market-specific information (often technical) to make trading decisions, while discretionary managers use a more judgmental or fundamental approach. c01.indd 10 1/10/08 11:00:57 AM Getting Started 11 Multi-strategy Multi-strategy investing uses various strategies simultaneously to realize short- and long-term gains.
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
In the long run, the only way hedge-fund establishments are going to survive while charging higher fees is if they deliver higher net-of-fee returns and/or much lower volatility. This is an empirical issue. 226 Table 12.1 Hedge-fund strategy definition. Convertible Arbitrage strategies consist of convertible bond investments. The idea is to buy a company’s convertible bond and sell the same company short the common stock. CTA Global or Commodity Trading Advisor funds invest in listed financial and commodity markets as well as currency markets all over the world. They can follow systematic or discretionary strategies. Distressed Securities involve buying back, at a low price, the securities of companies experiencing financial difficulties. Securities range from the lowest to highest risk (that is, senior secured debt to common stock). Emerging Market Strategies, as the name implies, invest in the emerging markets’ bonds and equities.
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
The law of active management implies that if you can realise a Sharpe ratio of 0.40 on a single instrument when trading with a holding period of a month, then betting once a day could boost that to an SR of 1.8. If you held your positions for just an hour and bet eight times a day you’d get to an SR of 5.2! As I said earlier, this assumes that profitable opportunities can be found at such timescales, and also ignores trading costs. 38. The analysis was done on a large set of Commodity Trading Advisors, a type of fund that is dominated by systematic trend followers. These returns are post fees (so the gross returns would be higher), but also include interest received on margin funds. These two effects roughly balance out. A small number of other types of systematic hedge fund are able to consistently return Sharpe ratios above 1.0. However, as I’ll discuss below, this is often due to negative skew. 47 Systematic Trading Table 2 assumes that you can achieve these theoretical pre-cost SR, but then takes costs into account.39 It looks like trading every day, or every couple of days, could theoretically give you an SR above 1.0 on a single asset.
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
The managers of an activist fund take sizable positions in small, flawed companies and then use their influence to force management changes and restructuring. A third type of event-driven fund is venture capital. Venture funds invest in start-up companies. Directional or tactical strategies. The largest group of hedge funds uses directional or tactical strategies. An example of a directional fund is a commodities trading advisor (CTA). CTAs use charts and mathematical models to identify trends in global futures markets. CTAs may go long or short a market to profit from both rising and falling futures prices. A second example of a tactical fund is a macro fund. These are fundamentally driven “top-down,” big-picture bets on currencies, interest rates, commodities, and global stock markets. Table 10-3 lists the three major categories of hedge funds and the major strategies within each category.
A Demon of Our Own Design: Markets, Hedge Funds, and the Perils of Financial Innovation by Richard Bookstaber
"Robert Solow", affirmative action, Albert Einstein, asset allocation, backtesting, beat the dealer, Black Swan, Black-Scholes formula, Bonfire of the Vanities, butterfly effect, commoditize, commodity trading advisor, computer age, computerized trading, disintermediation, diversification, double entry bookkeeping, Edward Lorenz: Chaos theory, Edward Thorp, family office, financial innovation, fixed income, frictionless, frictionless market, George Akerlof, implied volatility, index arbitrage, intangible asset, Jeff Bezos, John Meriwether, London Interbank Offered Rate, Long Term Capital Management, loose coupling, margin call, market bubble, market design, merger arbitrage, Mexican peso crisis / tequila crisis, moral hazard, Myron Scholes, new economy, Nick Leeson, oil shock, Paul Samuelson, Pierre-Simon Laplace, quantitative trading / quantitative ﬁnance, random walk, Renaissance Technologies, risk tolerance, risk/return, Robert Shiller, Robert Shiller, rolodex, Saturday Night Live, selection bias, shareholder value, short selling, Silicon Valley, statistical arbitrage, The Market for Lemons, time value of money, too big to fail, transaction costs, tulip mania, uranium enrichment, William Langewiesche, yield curve, zero-coupon bond, zero-sum game
Issues of risk, return, and liquidity apply to all hedge fund strategies, and indeed to the whole range of possible investments. Consider the following scan of articles from various issues of the Journal of Alternative Investments, just one of a number of journals on hedge funds: “Currency Market Trading Performance”; “Timber Investment”; “Current Attitudes to Private Equity”; “Convertible Arbitrage: A Manager’s Perspective”; “Macro Trading and Investment Strategies”; “Commodity Trading Advisor Survey”; “Stock Selection in Eastern European Markets”; “Market Neutral versus Long/Short Equity”; “Merger Arbitrage: Evidence of Profitability”; “Analysis of Real Estate Investments in the U.S.”; “Benefits of International Small Cap Stocks.” What is the common ground, other than being related to investments? If this is a sample of articles specific to hedge funds, what would articles on the broader world of investments outside of hedge funds look like?
Trading Risk: Enhanced Profitability Through Risk Control by Kenneth L. Grant
backtesting, business cycle, buy and hold, commodity trading advisor, correlation coefficient, correlation does not imply causation, delta neutral, diversification, diversified portfolio, fixed income, frictionless, frictionless market, George Santayana, implied volatility, interest rate swap, invisible hand, Isaac Newton, John Meriwether, Long Term Capital Management, market design, Myron Scholes, performance metric, price mechanism, price stability, risk tolerance, risk-adjusted returns, Sharpe ratio, short selling, South Sea Bubble, Stephen Hawking, the scientific method, The Wealth of Nations by Adam Smith, transaction costs, two-sided market, value at risk, volatility arbitrage, yield curve, zero-coupon bond
Monthly data have drawbacks in terms of both granularity and statistical significance, but they can be useful nevertheless from a number of perspectives. Over extended periods of time, they will provide insights into longer-term performance patterns that are difficult to discern with daily or weekly information. This is particularly true for complex portfolios, where it may not be practical to synchronize performance data on a more frequent basis. Moreover, because many investment portfolios (particularly hedge funds and Commodity Trading Advisors) report results on a monthly basis, monthly data can be a useful means of benchmarking your performance against other professional traders. I would suggest that even if you are able to put together daily or weekly performance results, you should create and analyze your monthly time series as well. In doing so, you are likely to find different and interesting patterns in this “higher aggregation level data” that are potentially important to the full understanding of your portfolio 42 TRADING RISK management dynamics.
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
Investment in managed futures could have been analyzed in the chapter on hedge funds. But it’s useful to compare the performance of these investments with their passive counterparts. In Chapter 9, returns on the Credit Suisse/Tremont hedge fund index were analyzed over the period 1994 to 2009. A subset of this overall index is the Credit Suisse/Tremont Managed Futures index tracking the returns of Commodity Trading Advisors (CTA) who take positions in bond, currency, and equity as well as commodity futures markets.12 Since 1980, the Barclay CTA index has provided another source of managed futures returns. The index was begun in 1985 with backdating of returns to January 1980.13 Table 12.7 reports the returns on these two active indexes and compares them with returns on the two passive indexes described earlier.