Why Risk Takers Stopped Taking Risk by Stephen Duneier
In the NFL, after a touchdown is scored the coach has the option to either kick for an extra point or attempt a 2-point conversion. Historically, extra points have a 97% success rate, while two-point conversions are successful 55% of the time. Since the expected return for the 2-pointer is greater (.55 x 2 = 1.1) than that of the extra point attempt (.97 x 1 = .97), you’d think most coaches would opt for the conversion. Better yet, when you consider that 2-point conversions executed with a run play have a 75% success rate and therefore an expected utility of 1.5, you’d think any coach that went for an extra point would be ridiculed for making such an irrational decision. Alas, you’d be wrong. In reality, coaches opt to kick the extra point in all but the most dire circumstances, and I can’t recall a single time any of them have been criticized for making such a suboptimal selection.
Actually, it’s a bit more complicated than I’ve suggested. Although the two-point conversion is the optimal choice if your goal is to maximize the number of points scored, that isn’t necessarily what coaches desire.
You see, in football, the coach’s objective is not to score the most points over time, but to score the most points within a segregated time period. In fact, it is easily possible for a team to score the most points and even the most net points over the course of a season, and yet not make it into the playoffs. (See 2014 NFL Season chart comparing points scored to games won.) So while opting to attempt the 2-point conversion is clearly the more rational option for someone looking to maximize total point accumulation, it’s not necessarily the right decision given the parameters as defined by the rules of the game.
Think about that for just a moment. For those of us in the business of making rational decisions in the face of uncertainty, one of the fundamental tenets is that we seek to maximize expected returns. In other words, we play the odds. As an investment manager, if I were facing a decision with the historical probabilities presented above, the only logical decision would be to attempt a 2-point conversion. After all, as investors, we don’t have segregated time buckets for which we need be concerned. I mean, a 2% return on January 31 means the same to me as a 2% return on February 1st. Therefore, I don’t have to make suboptimal decisions. Instead, I can make choices with the sole goal of maximizing my returns over time. Right?
Wrong. Well, it used to be that way, but the rules of the game have clearly changed to more closely match that of the NFL. We’ll get into one of the reasons why it has happened in a moment, but for now let’s explore the ramifications.
Bracketing occurs in our industry in a number of ways, all of which create segregated buckets of time, causing a shift in objectives away from maximizing expected returns, just as it does in the NFL. As a result, it’s understandable that the thought process of investment managers today shares more in common with NFL coaches than it does with those who sat in these seats just ten years ago.
It’s self-reinforcing too. Just as NFL coaches are applauded for choosing the suboptimal route of kicking the extra point, investment managers are rewarded for shortening their time horizons, window dressing their portfolios around month-end, quarter-end and year-end, and for focusing more on p&l volatility than returns.
Herein Lies the Problem If you think about it, the reason time horizons are truncated in sports is to make every moment more exciting, more uncertain. The shorter the season, the fewer the games, the fewer the opportunities to make up for a bad run, the more exciting the spectacle. What the promoters of sports seek is a fan sitting on the edge of their seat exhilarated by the fact that even the worst team can beat the best team on any given Sunday. What they count on is the fact that the shorter the time horizon, the greater the likelihood for an outlier result, essentially that noise will overwhelm signal. Granted, over the course of a season or tournament, the best teams often rise to the top, but in sports like NCAA football, a single loss can take a team out of contention for the entire season. It’s what makes college football so much more exciting than the pros, and it’s also why, even though the rules of the game are nearly identical, the way the game is played is so radically different. This difference in approach also explains why the greatest college quarterback of all time, Tim Tebow, garnered so little interest from NFL scouts and why so few fantastic college coaches have been able to successfully make the transition to the pros. It becomes a fundamentally different game, with different play calling, demanding different skill sets and generating different results, and yet the only significant difference between college and pro football is the frequency with which conclusions regarding success/failure are drawn.
There is a fundamental difference between sports and investing though, and this difference is the source of numerous problems which have developed over time, including lower returns for the industry and a rise in the impact of noise relative to signal on price action across all markets. You see, the end investor does not seek excitement for their money. They do not want noise to have a greater influence than signal on their returns. They do, however, seek to maximize expected returns, even if their behavior often appears at odds with that desire.
What’s Not So New About the New Normal This isn't a phenomenon unique to football or the current state of markets. Decades of research regarding human behavior has attempted to differentiate the phenomenon I've been discussing to this point, known as "narrow framing" to cognitive psychologists and "myopic loss aversion" to behavioral economists, from the more recognizable "risk aversion." One notable study, done by Mehra and Prescott in 1985, attempted to understand the equity premium puzzle, with premium referring to the outperformance of US equities over a safe investment like US treasuries and puzzle referring to how high it had been for so long (roughly 6% per year for 70 years). The real question they were asking is, if stocks have so outperformed treasuries over such a long period of time, why are they perceived as more risky? Ultimately, the conclusion they reached is that the "risk attitude of loss-averse investors depends on the frequency with which they reset their reference point". Richard Thaler estimated that in order to "solve" the equity premium puzzle, the most prominent evaluation period for investors would need to be 13 months. In other words, if you had a portfolio consisting solely of say, the S&P Index and US T-Bills, and only looked at your returns every 13 months, you'd perceive them to be equally risky. If you did so, your allocations would be very different and so too would the returns on your portfolio, than if you observed them more frequently.
Myopic loss aversion is the term used to describe this behavior because the "frequent evaluations prevent the investor from adopting a strategy that would be preferred over an appropriately long time horizon." In other words, the longer your time horizon for investing, the less frequently you should be observing the returns in your portfolio. Simply by checking on your investments more frequently, you perceive risk to be greater which leads to more risk averse behavior. We tend to think we can control it, but enough research has been done to prove you'd be an extreme case if that were true. A simple study by Thaler and Benartzi proved this point quite well.
They asked two groups of university professors how they would invest their retirement money if they had to choose between two investment funds, one of which was based on stock returns and the other on bonds. To the first group they provided charts showing the distribution of one-year rates of return, and the other was shown the distribution of 30-year rates of return. Those in the first group elected to put the majority in bonds, while the other group invested 90% of their funds in stocks.
The Impact of Benchmarking With the proliferation of hedge funds came the proliferation of firms and products designed to help you assess them, leading to standardization. Benchmarking became common practice. Seemingly innocuous and arguably random selections were made, such as using monthly data to analyze returns andvolatility creating a standard for which all future analysis would be conducted and funds compared. This standardization of time bucketing then led to the unintended convergence in both the assessment, and just as importantly, the perception of risk among two otherwise disparate groups; short term and long-term investors. Now, even an investor who describes themselves as having a longer term horizon is likely to suffer from myopic loss aversion, even if they don't realize it.
Suggestions For the hedge fund manager who seeks to maximize expected returns, one solution is to release your returns on a frequency you believe would allow investors to more accurately assess your true inherent risk. If you offer quarterly liquidity, release your returns at the same time. For investors, do your own analysis of returns and the volatility of returns using a period better aligned with your investment horizon. In other words, if you're truly a long-term investor review the performance of your investments less frequently. Gather the data less frequently or have your analysts parse the monthly data into buckets more aligned with your objectives, before you see it. Ask yourself whether it makes sense to review and analyze return data with the same frequency as hot money investors. So long as you do, your resulting behavior will more closely match that of a hot money investor than the long-term investor you perceive yourself to be.
Tale of 2 Portfolio Managers Let’s consider the returns and p&l volatility profile for two portfolio managers by looking at their Sharpe Ratio for the past 5 years (see chart). By this metric alone, PM 2 is clearly is a more attractive investment manager. In reality, PM 1 and PM 2 are both the S&P 500 Index, but PM 1’s volatility is assessed on a monthly basis and PM 2’s is assessed on an annual basis. FYI: Rather than using the calendar year-end, I’ve used August 20th to mark the end of each year. If I’d chosen to shift the period covered by the analysis ahead by just 2 days and calculated the Sharpe using daily returns, PM 1 would have dropped to 0.8.
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About the Author For nearly thirty years, Stephen Duneier has applied cognitive science to investment and business management. The result has been the turnaround of numerous institutional trading businesses, career best returns for experienced portfolio managers who have adopted his methods, the development of a $1.25 billion dollar hedge fund and 20.3% average annualized returns as a global macro portfolio manager.
Mr. Duneier teaches graduate courses on Decision Analysis and Behavioral Investing in the College of Engineering at the University of California. His book, AlphaBrain, is due to be published in early 2017 (Wiley & Sons).
Through Bija Advisors' coaching, workshops and publications, he helps the world's most successful and experienced investment managers improve performance by applying proven, proprietary decision-making methods to their own processes.
Stephen Duneier was formerly Global Head of Currency Option Trading at Bank of America, Managing Director in charge of Emerging Markets at AIG International and founding partner of award winning hedge funds, Grant Capital Partners and Bija Capital Management. As a speaker, Stephen has delivered informative and inspirational talks to audiences around the world for more than 20 years on topics including global macro economic themes, how cognitive science can improve performance and the keys to living a more deliberate life. Each is delivered via highly entertaining stories that inevitably lead to further conversation, and ultimately, better results.
His artwork has been featured in international publications and on television programs around the world, is represented by the renowned gallery, Sullivan Goss and earned him more than 50,000 followers across social media. As Commissioner of the League of Professional Educators, Duneier is using cognitive science to alter the landscape of American K-12 education. He received his master's degree in finance and economics from New York University's Stern School of Business.
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