Notes
1. The Subtleties of Asset Management
1. J. Grantham, “Silver Linings and Lessons Learned,” GMO Quarterly Letter (2008).
2. L. H. Pedersen, Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined (Princeton, NJ: Princeton University Press, 2015).
3. D. Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux, 2011), 177.
2. Understanding the Playing Field
1. The terms specified in the example are inspired by products sold in Canada. Products sold in the United States have different terms, which are often more favorable than in Canada. For example, in Canada the guaranteed bonus of the benefit base is usually linear (such as 5 percent × 10 years = 50 percent), while in the United States, it is usually compounded (1.0510 − 1 = 62.9 percent).
2. A. Ang, Asset Management: A Systematic Approach to Factor Investing (Oxford: Oxford University Press, 2014).
3. See, for example, S. J. Grossman and J. E. Stiglitz, “On the Impossibility of Informationally Efficient Markets,” American Economic Review 70 (1980): 393–408.
4. Investment Company Institute, 2013 Investment Company Fact Book: A Review of Trends and Activity in the U.S. Investment Company Industry, 53rd edition (2013).
5. E. F. Fama and K. R. French, “Luck Versus Skill in the Cross-Section of Mutual Fund Returns,” Journal of Finance 65 (2010): 1915–47.
6. J. A. Busse, A. Goyal, and S. Wahal, “Performance and Persistence in Institutional Investment Management,” Journal of Finance 65 (2010): 765–90.
7. J. A. Busse, A. Goyal, and S. Wahal, “Investing in a Global World,” Review of Finance 18 (2014): 561–90.
8. Investment Company Institute, 2013 Investment Company Fact Book: A Review of Trends and Activity in the U.S. Investment Company Industry, 53rd edition (2013).
9. SPIVA U.S. Scorecard, Year-End 2014.
10. D. diBartolomeo and E. Witkowski, “Mutual Fund Misclassification: Evidence Based on Style Analysis,” Financial Analysts Journal 53 (1997): 32–43; M. Kim, R. Shukla, and M. Tomas, “Mutual Fund Objective Misclassification,” Journal of Economic and Business 52 (2000): 309–23.
11. J. Lussier and S. Monciaud, “Developing and Investment Culture,” Desjardins Global Asset Management (2007).
3. Skill, Scale, and Luck in Active Fund Management
1. M. J. Mauboussin, The Success Equation: Untangling Skill and Luck in Business, Sports and Investing (Boston: Harvard Business Review Press, 2012).
2. Ibid.
3. R. C. Grinold, “The Fundamental Law of Active Management,” Journal of Portfolio Management 15 (1989): 30–37.
4. We simulate the monthly market returns from a normal distribution with annual mean of 5 percent and volatility of 20 percent. We simulate monthly returns of 100,000 funds with a 2 percent alpha, a 5 percent tracking error, and a beta normally distributed around one with volatility 0.2. Funds’ idiosyncratic risk is also normally distributed.
5. R. Kosowski, A. Timmermann, R. Wermers, and H. White, “Can Mutual Fund Stars Really Pick Stocks? New Evidence from a Bootstrap Analysis,” Journal of Finance 61 (2006): 2551–96.
6. L. Barras, O. Scaillet, and R. Wermers, “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas,” Journal of Finance 65 (2010): 179–216.
7. L. Pastor, R. F. Stambaugh, and L. A. Taylor, “Scale and Skill in Active Management,” Journal of Financial Economics 116 (2015): 23–45.
8. J. B. Berk and J. H. van Binsbergern, “Measuring Skill in the Mutual Fund Industry,” Journal of Financial Economics 118 (2015): 1–20.
9. See, for example, L. Barras, O. Scaillet, and R. Wermers, “False Discoveries in Mutual Fund Performance: Measuring Luck in Estimated Alphas,” Journal of Finance 65 (2010): 179–216, and Kosowski, Timmermann, Wermers, and White, “Can Mutual Funds Stars Really Pick Stocks?”
10. M. Kacperczyk, S. Van Nieuwerburgh, and L. Veldkamp, “Time-Varying Fund Manager Skill,” Journal of Finance 69 (2014): 1455–84.
11. K. J. M. Cremers and A. Petajisto, “How Active Is Your Fund Manager? A New Measure that Predicts Performance,” Review of Financial Studies 22 (2009): 3329–65.
12. A. Frazzini, J. Friedman, and L. Pomorski, “Deactivating Active Share” (AQR Capital Management white paper, 2015).
13. H. Doshi, R. Elkamhi, and M. Simutin, “Managerial Activeness and Mutual Fund Performance,” Forthcoming in The Review of Asset Pricing Studies (2016).
14. For an excellent and useful survey, see R. C. Jones and R. Wermers, “Active Management in Mostly Efficient Markets,” Financial Analyst Journal 67 (2011): 29–45.
15. L. Pastor and R. F. Stambaugh, “On the Size of the Active Management Industry,” Journal of Political Economy 120 (2012): 740–81.
16. C. D. Ellis, “Murder on the Orient Express: The Mystery of Underperformance,” Financial Analysts Journal 68 (2012): 13–19.
17. J. C. Bogle, “The Clash of Cultures,” Journal of Portfolio Management 37 (2011): 14–28.
4. What May and Can Be Forecasted?
1. N. N. Taleb, 2014, Antifragile: Things That Gain from Disorder (New York: Random House, 2014), 348.
2. P. E. Tetlock, Expert Political Judgment: How Good Is It? How Can We Know? (Princeton, NJ: Princeton University Press, 2006).
3. N. Silver, The Signal and the Noise: Why So Many Predictions Fail But Some Don’t (New York: Penguin, 2012), 49.
4. Survey of Professional Forecasters, November 13, 2007, Federal Reserve Bank of Philadelphia.
5. Wall Street Economists Institute, Economics Prediction Research Project.
6. Fox News Debate, December 16, 2006, and August 8, 2007.
7. R. Greszler and R. Boccia, Social Security Trustees Report: Unfunded Liabilities of $1.1 Trillion and Projected Insolvency in 2033 (Washington, DC: Heritage Foundation, 2014).
8. Bloomberg News, March 26, 2009.
9. Bloomberg Television, August 31, 2011.
10. Tweet from Nouriel Roubini, September 2011.
11. Nouriel Roubini, “The Global Stock Market Rally Is Over…& The Worst Is Yet To Come,” Economywatch, 2012.
12. J. Sommer, “An Ugly Forecast that Has Been Right Before,” New York Times, October 18, 2011.
13. P. Loungani, “The Arcane Art of Predicting Recessions,” Financial Times, December 18, 2000.
14. I. Ben-David, J. Graham, and C. R. Harvey, “Managerial Miscalibration,” Quarterly Journal of Economics 128 (2013): 1547–84.
15. M. Glaser, T. Langer, and M. Weber, “True Overconfidence in Interval Estimates: Evidence Based on a New Measure of Miscalibration,” Journal of Behavioral Decision Making 26 (2013): 405–17.
16. W. P. Dukes, J. Peng, and P. C. English II, “How Do Practitioners Value Common Stock?” Journal of Investing 15 (2006): 90–104.
17. J. Montier, Behavioural Investing: A Practitioner’s Guide to Applying Behavioural Finance (Chichester, England: Wiley Finance, 2007).
18. J. Montier J., The Little Book of Behavioural Investing: How Not to Be Your Worst Enemy (Hoboken, NJ: Wiley, 2010).
19. Transcript of Chairman Bernanke’s Press Conference, June 19, 2013.
20. B. Appelbaum, “Two Economies in Turmoil for Different Reasons,” New York Times, June 21, 2013.
21. Monthly Labor Review, Bureau of Labor Statistics, April 2015.
22. M. L. Finucane and C. M. Gullion, “Developing a Tool for Measuring the Decision-Making Competence of Older Adults,” Psychology and Aging 25 (2010): 271–88.
23. Edge, “How to Win at Forecasting: A Conversation with Philip Tetlock,” 2015, edge.org/conversation/how-to-win-at-forecasting.
24. M. J. Mauboussin, Think Twice: Harnessing the Power of Counterintuition (Boston: Harvard Business School Publishing, 2012), 46.
25. K. Storchmann, 2011, “Wine Economics: Emergence, Developments, Topics” (American Association of Wine Economists, AAWE Working Paper No. 85).
26. I. Ayres, Super Crunchers: Why Thinking-by-Numbers Is the New Way to Be Smart (New York: Bantam Books, 2007).
27. Orley Ashenfelter, David Ashmore, and Robert Lalonde, “Bordeaux Wine Vintage Quality and the Weather,” Chance 8 (1995): 7–14.
28. F. Prial, “Wine Talk,” New York Times, May 23, 1990.
29. J. Y. Campbell and J. H. Cochrane, “By Force of Habit: A Consumption-Based Explanation of Aggregate Stock Market Behavior,” Journal of Political Economy 107 (1999): 205–51.
30. See, for example, R. F. Stambaugh, “Predictive Regressions,” Journal of Financial Economics 54 (1999): 375–421.
31. A. Goyal and I. Welch, “A Comprehensive Look at the Empirical Performance of Equity Premium Prediction,” Review of Financial Studies 21 (2008): 1455–508.
32. J. H. Cochrane, “Presidential Address: Discount Rates,” Journal of Finance 66 (2011): 1047–108.
33. Ibid.
34. D. Rapach and G. Zhou, “Forecasting Stock Returns,” Handbook of Econometric Forecasting 2A (2013): 327–83.
35. Note that we employ a simple predictive model based on the combination of the following work: M. I. Ferreira and P. Santa-Clara, “Forecasting Stock Market Returns: The Sum of the Parts Is More Than the Whole,” Journal of Financial Economics 100 (2011): 514–37, and J. Y. Campbell and S. B. Thompson, “Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?” Review of Financial Studies 21 (2008): 1509–31.
36. Each period, we find the optimal allocation of an investor with logarithmic utility.
37. P. E. Meehl, Clinical vs. Statistical Prediction: A Theoretical Analysis and a Review of the Evidence (Minneapolis: University of Minnesota Press, 1954).
38. W. M. Grove, D. H. Zald, A. M. Hallberg, B. Lebow, E. Snitz, and C. Nelson, “Clinical Versus Mechanical Prediction: A Meta-Analysis,” Psychological Assessment 12 (2000): 19–30.
39. See L. Landro, “The Secret to Fighting Infection,” Wall Street Journal, March 27 2011, and “Three Years Out, Safety Checklists Keeps Hospital Infections in Check,” Journal of Nursing 40, (2010): 21–23.
40. W. M. Grove and P. E. Meehl, “Comparative Efficiency of Formal (Mechanical, Algorithmic) and Informal (Subjective, Impressionistic) Prediction Procedures: The Clinical/Statistical Controversy,” Psychology, Public Policy, and the Law 2 (1997): 293–323.
41. N. N. Taleb, 2014, Antifragile: Things that Gain from Disorder (New York: Random House, 2014): 11.
42. We use a NGARCH model on all daily returns available at each point in time to ensure our results are out-of-sample. The monthly volatility predictions are implied by the model estimates and the last filtered conditional volatility.
43. To obtain time-varying volatility, we fit a NGARCH on monthly returns of the S&P 500 and on monthly returns of the portfolio that targets a 10 percent annual volatility.
5. The Blueprint to Long-Term Performance
1. J. Montier, The Little Book of Behavioural Investing: How Not to Be Your Worst Enemy (Hoboken, NJ: Wiley, 2010), 70.
2. C. B. Erb and C. R. Harvey, “The Strategic and Tactical Value of Commodity Futures,” Financial Analysts Journal 62 (2006): 69–97.
3. G. Gorton and G. Rouwenhorst, “Facts and Fantasies About Commodity Futures,” Financial Analysts Journal 62 (2006): 47–68.
4. We use the fact that the geometric mean of a portfolio with an allocation of ω to one risky asset is given by Rf + ωΕ[R – Rf] – ½ ω2Var[R] so ω* = (Ε[r – Rf] / Var[R]).
5. We estimate a bivariate GARCH on monthly returns for the S&P 500 Index (from Bloomberg) and the ten-year U.S. government bond (from CRSP) from February 1971 to March 2015. The 60/40 portfolio is rebalanced every month.
6. We use monthly returns for the S&P 500 Index (from Bloomberg) and the ten-year U.S. government bond (from CRSP) from February 1971 to March 2015. The 60/40 portfolio is rebalanced every month. We use the monthly returns of the market, SMB, HML, and UMD factors and the one-month U.S. T-Bill rate of return obtained from Kenneth French’s website. The factor exposure is obtained by estimating an AR(1)-NGARCH for each asset to obtain the conditional variances and a dynamic normal copula model to obtain the dynamic correlations.
7. R. S. J. Koijen, H. Lustig, and S. Van Nieuwerburgh, “The Cross-Section and Time-Series of Stock and Bond Returns” (NBER Working paper, New York University, 2015).
8. L. Zhang, “The Value Premium,” Journal of Finance 60 (2005): 67–103.
9. See, for example, R. Jagannathan and Z. Wang, “The Conditional CAPM and the Cross-Section of Expected Returns,” Journal of Finance 51 (1996): 3–53, and S. Betermier, L. E. Calvet, and P. Sodini, “Who Are the Value and Growth Investors?” Forthcoming in The Journal of Finance (2016).
10. J. Berk, “A Critique of Size-Related Anomalies,” Review of Financial Studies 8 (1995): 275–86.
11. See A. Perold, “Fundamentally Flawed Indexing,” Financial Analyst Journal 68 (2007): 31–37.
12. M. J. Brennan and A. W. Wang, “The Mispricing Return Premium,” Review of Financial Studies 23 (2010): 3437–68.
13. J. H. Cochrane, “Presidential Address: Discount Rates,” Journal of Finance 66 (2011): 1047–108.
14. C. S. Asness, T. J. Moskowitz, and L. H. Pedersen, “Value and Momentum Everywhere,” Journal of Finance 68 (2013): 929–85.
15. H. Lustig, N. Roussanov, and A. Verdelhan, “Common Risk Factors in Currency Markets,” Review of Finance Studies 24 (2011): 3731–77.
16. C. R. Harvey, Y. Liu, and H. Zhu, “…and the Cross-Section of Expected Returns” Review of Financial Studies 29 (2016): 5–68.
17. D. McLean and J. Pontiff, “Does Academic Research Destroy Stock Return Predictability?” Journal of Finance 71 (2016): 5–32.
6. Building Better Portfolios
1. We use the term factor somewhat loosely here. Risk factors usually refer to sources of expected returns linked to a rational compensation for risk, not to mispricings. But as we discussed in chapter 5, building a strategy that tries to exploits mispricings is similar to building risk factors. From investors’ perspective, as opposed to the academic asset pricers’ perspective, the difference is less important.
2. Available on Professor Ken French’s website at http://mba.tuck.dartmouth.edu/pages/faculty/ken.french/data_library​.xhtml.
3. https://www.aqr.com/library/data-sets.
4. Some time series will unfortunately require the reader to have subscription to data providers such as Bloomberg and Datastream.
5. V. Acharya and L. H. Pedersen, “Asset Pricing with Liquidity Risk,” Journal of Financial Economics 77 (2005): 375–410.
6. See M. Rubinstein, “The Fundamental Theorem of Parameter-Preference Security Valuation,” Journal of Financial and Quantitative Analysis 8 (1973): 61–69; A. K. Kraus and R. H. Litzenberger, “Skewness Preference and the Valuation of Risk Assets,” Journal of Finance 31 (1976): 1085–100; and C. R. Harvey and A. Siddique, “Conditional Skewness in Asset Pricing Tests,” Journal of Finance 55 (2000): 1263–95.
7. C. Asness and A. Frazzini, “The Devil in HML’s Details,” Journal of Portfolio Management 39 (2013): 49–68.
8. P. Christoffersen and H. Langlois, “The Joint Dynamics of Equity Market Factors,” Journal of Financial and Quantitative Analysis 48 (2013): 1371–404.
9. J. H. Cochrane, “A Mean-Variance Benchmark for Intertemporal Portfolio Theory,” Journal of Finance 69 (2014): 1–49.
10. The optimal mean-variance allocation is given by ω = Σ−1μ / γ, where Σ is the covariance matrix of returns, μ is the vector of expected excess returns, and γ is the investor’s degree of risk aversion.
11. We use an exponentially weighted average on daily returns with a parameter of 0.94 as in the earlier J. P. Morgan RiskMetrics model.
12. See, for example, R. C. Merton, “On Estimating the Expected Return on the Market,” Journal of Financial Economics 8 (1980): 323–61.
13. Note here that we use the term leverage somewhat loosely to mean the sum of factor weights. As four of these factors involve short positions, and as many positions could cancel out between factors, the gross leverage could actually be different from 200 percent.
14. We use the FTSE RAFI US 1000 Total Return Index (Bloomberg ticker FR10XTR Index) and the S&P 500 Equal Weight Total Return Index (Bloomberg ticker SPXEWTR Index). The FTSE TOBAM MaxDiv USA $ Index was provided by TOBAM. The risk-free rate and all factor returns are from AQR’s data library. We use Newey-West standard errors. ** indicates statistical significance at the 1 percent level and * at the 5 percent level.
15. Presentation to the Q-Group Spring 2007 Seminar, “Non-Cap Weighted Indices,” available at https://www.aqr.com/~/media/files/perspectives/q-group-presentation-fundamental-investing.pdf?la=en.
16. See R. Kan and G. Zhou, “Optimal Portfolio Choice with Parameter Uncertainty,” Journal of Financial and Quantitative Analysis 42 (2007): 621–56, and J. Tu and G. Zhou, “Markowitz Meets Talmud: A Combination of Sophisticated and Naïve Diversification Strategies,” Journal of Financial Economics 99 (2011): 204–15.
17. Y. Choueifaty and Y. Coignard, “Toward Maximum Diversification,” Journal of Portfolio Management 35 (2008): 40–51.
18. To see this, note that the maximum Sharpe ratio portfolio maximize the ratio ωTμ / √(ωTΣω), where μ is the vector of expected excess returns, ω is the vector of portfolio weights, and Σ is the matrix of covariances. If we assume that expected excess returns are proportional to volatilities, μ = Kσ, where K is a constant and σ is the vector of volatilities, the ratio becomes KωTσ / √(ωTΣω). Because K has no incidence on the maximization, the ratio is the same as the one the maximum diversification portfolio maximizes.
19. Countries in Europe include Austria, Belgium, Switzerland, Germany, Denmark, Spain, Finland, France, United Kingdom, Greece, Ireland, Israel, Italy, Netherlands, Norway, Portugal, and Sweden. North American countries include Canada and the United States. The Pacific region is composed of Australia, Hong Kong, Japan, New Zealand, and Singapore.
20. E. F. Fama and K. R. French, “Size, Value, and Momentum in International Stock Returns,” Journal of Financial Economics 105 (2012): 457–72.
21. U.S. Treasury fixed term bond indices are available from CRSP for maturities of 1, 2, 5, 7, 10, 20, and 30 years. These test portfolios are strictly speaking not portfolios because a single representative bond is chosen each month. At the end of each month, a representative bond for each maturity is chosen and held for the next month. A representative bond is the most recently issued among all that are fully taxable, noncallable, nonflower, and at least six months from but closest to the maturity date. Flower bonds are considered if no bond meets these criteria. The details for constructing the value-weighted market portfolio of all bonds can be found in H. Langlois, “Asset Pricing with Return Asymmetries: Theory and Tests” (Working paper, HEC Paris, 2014).
22. See, for example, C. R. Harvey, “The Real Term Structure and Consumption Growth,” Journal of Financial Economics 22 (1988): 305–33.
23. We use the composition of the S&P Goldman Sachs Commodity Index as our sample of commodity futures. This index contains twenty-four traded contracts covering six categories: energy (WTI crude oil, Brent crude oil, RBOB gasoline, heating oil, gas oil, and natural gas), industrial metals (aluminum, copper, lead, nickel, and zinc), precious metals (gold and silver), agriculture: grains and oil seeds (wheat, Kansas wheat, corn, and soybeans), agriculture: softs (cotton, sugar, coffee, and cocoa), and livestock (feeder cattle, live cattle, and lean hogs). We use contracts as they become available. We obtain futures quotes from Bloomberg for all available maturities. For each commodity futures, we construct a time series of total returns by investing each month in the futures contract whose maturity is the nearest maturity coming later than the end of the month.
24. We obtain spot exchange rates and one-month forward rates from Datastream for sixteen countries: Belgium, France, Germany, Italy, the Netherlands (all replaced by the Euro starting in January 1999), Australia, Canada, Denmark, Hong Kong, Japan, New Zealand, Norway, Singapore, Sweden, Switzerland, and the United Kingdom. All rates are expressed in U.S. dollars per unit of foreign currency and are collected by Barclays Bank International. Not all exchange rates are available each month; we have a minimum of seven and a maximum of fifteen over time. The introduction of the Euro is responsible for a decrease in the number of currencies available.
25. Basis is computed as the negative of the logarithm-difference of the futures with a maturity closest to one year ahead and the futures with the closest maturity.
26. The interest rates differential is the one implied by spot and forward rates. We average the daily differentials over the previous month to control for outliers.
27. See F. Yang, “Investment Shocks and the Commodity Basis Spread,” Journal of Financial Economics 110 (2013): 164–84, and G. Gorton, F. Hayashi, and G. Rouwenhorst, “The Fundamentals of Commodity Futures Returns,” Review of Finance 17 (2013): 35–105.
28. H. Lustig, N. Roussanov, and A. Verdelhan, “Common Risk Factors in Currency Markets,” Review of Financial Studies 24 (2011): 3731–77.
29. A. Illmanen and J. Kizer, “The Death of Diversification Has Been Greatly Exaggerated,” Journal of Portfolio Management 38 (2012): 15–27.
30. M. A. Ferreira and P. Santa-Clara, “Forecasting Stock Market Returns: The Sum of the Parts Is More than the Whole,” Journal of Financial Economics 100 (2011): 514–37.
31. For each asset, we compute the one month ahead prediction, which is the payout yield scaled to a monthly horizon. Over the longest return history available for each asset, we compute the out-of-sample R2 as in J. Y. Campbell and S. B. Thompson, “Predicting Excess Stock Returns Out of Sample: Can Anything Beat the Historical Average?” Review of Financial Studies 21 (2008): 1509–31. R2 vary from 1 percent to 11 percent for all equity and bond indices, meaning that the predictions have lower mean squared errors than using the sample average excess return as a prediction.
32. R. Jagannathan and T. Ma, “Risk Reduction in Large Portfolios: Why Imposing the Wrong Constraints Helps,” Journal of Finance 58 (2003): 1651–83.
33. V. DeMiguel, L. Garlappi, F. J. Nogales, and R. Uppal, “A Generalized Approach to Portfolio Optimization: Improving Performance by Constraining Portfolio Norms,” Management Science 55 (2009): 798–812.
34. See O. Ledoit and M. Wolf, “Improved Estimation of the Covariance Matrix of Stock Returns with an Application to Portfolio Selection,” Journal of Empirical Finance 10 (2003): 603–21, and O. Ledoit and M. Wolf, “A Well-Conditioned Estimator for Large-Dimensional Covariance Matrices,” Journal of Multivariate Analysis 88 (2004): 365–411.
35. We may say here that our rebalancing frequency is exogenously determined by our preferences, not endogenously determined by asset price dynamics.
36. This fact may be due to our current state of knowledge about volatility and correlation modeling. It is entirely possible that as we become better at estimating time-varying volatilities and correlations that nonnormal features such as fat tails and asymmetries would become less important. This is not the case for the time being.
37. P. Christoffersen and H. Langlois, “The Joint Dynamics of Equity Market Factors,” Journal of Financial and Quantitative Analysis 48 (2013): 1371–404.
38. See, for example, M. W. Brandt, “Estimating Portfolio and Consumption Choice: A Conditional Euler Equations Approach,” Journal of Finance 54 (1999): 1609–46; Y. Ait-Sahalia and M. W. Brandt, “Variable Selection for Portfolio Choice,” Journal of Finance 56 (2001): 1297–351; and M. W. Brandt, P. Santa-Clara, and R. Valkanov, “Parametric Portfolio Policies: Exploiting Characteristics in the Cross Section of Equity Returns,” Review of Financial Studies 22 (2009): 3411–47.
39. Y. Ait-Sahalia and M. W. Brandt, “Variable Selection for Portfolio Choice,” Journal of Finance 56 (2001): 1297–351.
40. See, for example, A. Ang, D. Papanikolaou, and M. M. Westerfield, “Portfolio Choice with Illiquid Assets” (Working paper, Columbia Business School, 2014).
41. A. Ang, Asset Management: A Systematic Approach to Factor Investing (Oxford: Oxford University Press, 2014).
42. From Datastream, we obtain the one- to three-year Canadian Government Bond Index (ACNGVG1) and the over ten-year Canadian Government Bond Index (TCNGVG5).
43. We compute beta using an exponentially weighted moving average on monthly portfolio and currency returns.
44. C. B. Erb and C. R. Harvey, “The Golden Dilemma,” Financial Analyst Journal 69 (2013): 10–42.
7. We Know Better, But…
1. N. Silver, The Signal and the Noise (New York: Penguin, 2012), 324.
2. S. Lack, The Hedge Fund Mirage: The Illusion of Big Money and Why It’s Too Good to Be True (Hoboken, NJ: Wiley, 2012).
3. C. Ellis, Winning the Loser’s Game: Timeless Strategies for Successful Investing, 6th ed. (New York: McGraw-Hill, 2013).
4. B. Englich and T. Mussweiler, “Sentencing Under Uncertainty: Anchoring Effects in the Courtroom,” Journal of Applied Social Psychology 31 (2001): 1535–51.
5. Daniel Kahneman, Thinking, Fast and Slow (New York: Farrar, Straus and Giroux, 2011).
6. A. Tversky and D. Kahneman, “Judgment Under Uncertainty: Heuristics and Biases,” Sciences, n.s. 185 (1974): 1124–31.
7. M. G. Haselton, D. Nettle, and P. W. Andrews, “The Evolution of Cognitive Biases,” in The Handbook of Evolutionary Psychology, ed. D. M. Buss (Hoboken, NJ: Wiley, 2005), 724–46.
8. J. Montier, The Little Book of Behavioural Investing: How Not to Be Your Own Worst Enemy (Hoboken, NJ: Wiley, 2010), 6.
9. Investment Innovation Conference, Scottsdale, November 6–8, 2013.
10. J. Montier, Behavioural Investing: A Practitioners Guide to Applying Behavioural Finance (Chichester, England: Wiley Finance, 2007), 129.
11. C. M. Reinhart and V. R. Reinhart, “After the Fall” (NBER Working Paper No. 16334, 2010).
12. M. G. Haselton, D. Nettle, and P. W. Andrews, “The Evolution of Cognitive Biases,” in The Handbook of Evolutionary Psychology, ed. David M. Buss (Hoboken, NJ: Wiley, 2005), 724–746.
13. C. MacKay, Extraordinary Popular Delusions and the Madness of Crowds (with a foreword by Andrew Tobias, 1841) (New York: Harmony Books, 1980).
14. Z. Kunda, “The Case for Motivated Reasoning,” Psychological Bulletin 108 (1990): 480–98.
15. R. Nickerson, “Confirmation Bias: A Ubiquitous Phenomenon on Many Guises,” Review of General Psychology 2, no. 2 (2009): 175–220.
16. Based on citations reported on Google Scholar.
17. B. Gaylord, “Nate Silver’s Model vs. The Morris Law,” TownHall.com, 2012.
18. A. Khorona, H. Sarvaes, and P. Tufano, “Mutual Fund Fees Around the World,” Review of Financial Studies 21 (2009): 2379–416.
19. http://www.bnn.ca/News/2014/11/5/Trailing-fees-What-your-mutual-fund-advisor-now-has-to-tell-you.aspx.
20. Investor Economics and Strategic Insight, Monitoring Trends in Mutual Fund Cost of Ownership and Expense Ratios: A Canada–U.S. Perspective (Toronto: Investment Funds Institute of Canada, 2012).
21. H. R. Arkes, R. M. Dawes, and C. Christensen, “Factors Influencing the Use of a Decision Rule in a Probabilistic Task,” Organizational Behavior and Human Decision Processes 37 (1986): 93–110.
22. Michael J. Mauboussin, Think Twice: Harnessing the Power of Counterintuition (Boston: Harvard Business Press, 2012), 33.
23. R. Briner, D. Denyer, and D. M. Rousseau, “Evidence-Based Management: Concept Clean-Up Time?” Academy of Management Perspectives 23 (2009): 5–18.
24. J. Pfeffer and R. I. Sutton, Management Half-Truths and Nonsense: How to Practice Evidence-Based Management, Adapted from Hard Facts, Dangerous Half-Truths, and Total Nonsense: Profiting from Evidence-Based Management (Cambridge: Harvard Business School Press, 2006).
25. H. Berman, Making a Difference: The Management and Governance of Non-Profit Enterprises (Rochester: Rochester Institute of Technology, 2010).
26. S. Efron and R. Gerlsbeck, “MoneySense Answers Your 20 Burning RRSP Questions,” Maclean’s, February 9, 2015. http://www.macleans.ca/economy/money-economy/moneysense-answers-20-burning-rrsp-questions/.
27. For an excellent overview of the literature on liquidity risk, we refer the reader to Y. Amihud, H. Mendelson, and L. H. Pedersen, Market Liquidity: Asset Pricing, Risk, and Crises (Cambridge: Cambridge University Press, 2013), and T. Foucault, M. Pagano, and A. Röell, Market Liquidity: Theory, Evidence, and Policy (Oxford: Oxford University Press, 2013).
28. G. Klein, “Performing a Project Premortem,” Harvard Business Review 85 (2007): 18–19.
29. K. Savitsky, L. Van Boven, N. Epley, and W. Wight, “The Unpacking Effect in Responsibility Allocations for Group Tasks,” Journal of Experimental Social Psychology 41 (2005): 447–57.
30. A. Ang, Asset Management: A Systematic Approach to Factor Investing (Oxford: Oxford University Press, 2014).
31. J. Ciolli, “Dumb Beta Strikes Back for U.S. Stocks Starved of Breadth,” Bloomberg News, August 4, 2015.
32. M. Rice and G. Strotman, The Next Chapter in the Active vs. Passive Management Debate (Chicago: DiMeo Schneider and Associates, LLC Research and Associates, 2007).
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