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Investing Expertise

Can analysts, experts and gurus really give you an investing/trading edge? Should you track the advice of as many as possible? Are there ways to tell good ones from bad ones? Recent research indicates that the average “expert” has little to offer individual investors/traders. Finding exceptional advisers is no easier than identifying outperforming stocks. Indiscriminately seeking the output of as many experts as possible is a waste of time. Learning what makes a good expert accurate is worthwhile.

Mutual Fund Investors Irrationally Naive?

Do retail investors rationally account for risks as modeled in academic research when choosing actively managed equity mutual funds? In their March 2019 paper entitled “What Do Mutual Fund Investors Really Care About?”, Itzhak Ben-David, Jiacui Li, Andrea Rossi and Yang Song investigate whether simple, well-known signals explain active mutual fund investor behavior better than academic asset pricing models. Specifically, they compare abilities of Morningstar’s star ratings and recent returns versus formal pricing models to predict net fund flows. They consider the Capital Asset Pricing Model (CAPM) and alphas calculated with 1-factor (or market-adjusted), 3-factor (plus size and book-to-market) and 4-factor (plus momentum) models of stock returns. They consider degree of agreement between signals for a fund (such as number of Morningstar stars and sign of a factor model alpha) and the sign of net capital flow for that fund. They also analyze spreads between net flows to top and bottom funds ranked according to Morningstar stars and fund alphas, taking the number of 5-star and 1-star funds to determine the number of top-ranked and bottom-ranked funds, respectively. Using monthly returns and Morningstar ratings for 3,432 actively managed U.S. equity mutual funds and contemporaneous market, size, book-to-market and momentum factor returns during January 1991 through December 2011 (to match prior research), they find that:

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Alternative Beta Live

Have long-short alternative beta (style premium) strategies worked well in practice? In their February 2019 paper entitled “A Decade of Alternative Beta”, Antti Suhonen and Matthias Lennkh use actual performance data to assess alternative beta strategies across asset classes from the end of 2007 through the end of 2017, including quantification of fees and potential survivorship bias in public data. Specifically, they form three equal volatility weighted (risk parity) composite portfolios of strategies at the ends of each year during 2007-2016, 2007-2011 and 2012-2016. Each portfolio includes all the strategies launched during the first year and then adds strategies launched each following year at the end of that year. When a strategy dies (is discontinued by the offeror), they reallocate its weight to surviving strategies within the portfolio. They also create two additional portfolios for each period/subperiod that segregate equities and non-equities. They further evaluate alternative beta strategy diversification benefits by comparing them to conventional asset class portfolios. Using weekly post-launch excess returns in U.S. dollars for 349 reasonably unique live and dead alternative beta strategies offered by 17 global investment banks, spanning 14 styles and having at least one year of history during 2008 through 2017, they find that:

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Machine Learning Factor?

What are potential monthly returns and alphas from applying machine learning to pick stocks? In their February 2019 paper entitled “Machine Learning for Stock Selection”, Keywan Rasekhschaffe and Robert Jones summarize basic concepts of machine leaning and apply them to select stocks from U.S. and non-U.S. samples, focusing on the cross-section of returns (as in equity factor studies). To alleviate overfitting in an environment with low signal-to-noise ratios, they highlight use of: (1) data feature engineering, and (2) combining outputs from different machine learning algorithms and training sets. Feature engineering applies market/machine learning knowledge to select the forecast variable, algorithms likely to be effective, training sets likely to be informative, factors likely to be informative and factor standardization approach. Their example employs an initial 10-year training period and then walks forecasts forward monthly (as in most equity factor research) for each stock, as follows:

  • Employ 194 firm/stock input variables.
  • Use three rolling training sets (last 12 months, same calendar month last 10 years and bottom half of performance last 10 years), separately for U.S. and non-U.S. samples.
  • Apply four machine learning algorithms, generating 12 signals (three training sets times four algorithms) for each stock each month, plus a composite signal based on percentile rankings of the 12 signals.
  • Rank stocks into tenths (deciles) based on each signal, which forecasts probability of next-month outperformance/underperformance.
  • Form two hedge portfolios that are long the decile of stocks with the highest expected performance and short the decile with the lowest, one equal-weighted and one risk-weighted (inverse volatility over the past 100 trading days), with a 2-day lag between forecast and portfolio reformation to accommodate execution.
  • Calculate gross and net average excess (relative to U.S. Treasury bill yield) returns and 4-factor (market, size, book-to-market, momentum) alphas for the portfolios. To estimate net performance, they assume 0.3% round trip trading frictions. 

They consider two benchmark portfolios that pick long and short side using non-machine learning methods. Using a broad sample of small, medium and large stocks (average 5,907 per month) spanning 22 developed markets, and contemporaneous values for the 194 input variables, during January 1994 through December 2016, they find that: Keep Reading

Sloppy Selling of Expert Traders?

Do expert investors (institutional stock portfolio managers) add value both by buying future outperforming stocks and by selling future underperforming stocks? In their December 2018 paper entitled “Selling Fast and Buying Slow: Heuristics and Trading Performance of Institutional Investors”, Klakow Akepanidtaworn, Rick Di Mascio, Alex Imas and Lawrence Schmidt examine trade decisions of experienced institutional (e.g., pension fund) stock portfolio managers to determine whether they buy and sell shrewdly. In their main tests, they evaluate: (1) positions added versus randomly buying more shares of some stock already in the portfolio: and, (2) positions liquidated versus randomly selling some other holding that was not traded on that date. Using data for 783 portfolios involving 4.4 million trades (2.0 million sells and 2.4 million buys), and prices for assets held and traded in U.S. dollars, during January 2000 through March 2016, they find that:

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Stopping Tests after Lucky Streaks?

Might purveyors of trading strategies be presenting performance results biased by stopping them when falsely successful? In other words, might they be choosing lucky closing conditions for reported positions? In the December 2018 revision of their paper entitled “p-Hacking and False Discovery in A/B Testing”, Ron Berman, Leonid Pekelis, Aisling Scott and Christophe Van den Bulte investigate whether online A/B experimenters bias results by stopping monitored commercial (marketing) experiments based on latest p-value. They hypothesize that such a practice may exist due to: (1) poor training in statistics; (2) self-deception motivated by desire for success; or, (3) deliberate deception for selling purposes. They employ regression discontinuity analysis to estimate whether reaching a particular p-value causes experimenters to end their tests. Using data from 2,101 online A/B experiments with daily tracking of results during 2014, they find that:

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Should the “Anxious Index” Make Investors Anxious?

Since 1990, the Federal Reserve Bank of Philadelphia has conducted a quarterly Survey of Professional Forecasters. The American Statistical Association and the National Bureau of Economic Research conducted the survey from 1968-1989. Among other things, the survey solicits from economic experts probabilities of U.S. economic recession (negative GDP growth) during each of the next four quarters. The survey report release schedule is mid-quarter. For example, the release date of the fourth quarter 2018 report is November 13, 2018, with forecasts for the four quarters of 2019. The “Anxious Index” is the probability of recession during the next quarter. Are these forecasts meaningful for future U.S. stock market returns? Rather than relate the probability of recession to stock market returns, we instead relate one minus the probability of recession (the probability of good times). If forecasts are accurate, a relatively high (low) forecasted probability of good times should indicate a relatively strong (weak) stock market. Using survey results and quarterly S&P 500 Index levels (on survey release dates as available, and mid-quarter before availability of release dates) from the fourth quarter of 1968 through the fourth quarter of 2018 (201 surveys), we find that:

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Unbiased Performance of Endowment Investments

Do non-profit endowments beat the market with their investments? In their November 2018 paper entitled “Investment Returns and Distribution Policies of Non-Profit Endowment Funds”, Sandeep Dahiya and David Yermack estimate investment returns and distribution rates for a broad and unbiased (not self-reported or self-selected) sample of U.S. non-profit endowment funds. Using annual IRS Form 990 filings for 28,696 organizations and annual total returns for a capitalization-weighted U.S. Stock market index and a U.S. Treasuries index during 2009-2016, they find that:

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Personal Trading Performance of Financial Intermediaries

Do employees of financial intermediaries such as brokers, financial analysts and fund managers take advantage of their access to private information? In their March 2018 paper entitled “Personal Trading by Brokers, Analysts, and Fund Managers”, Henk Berkman, Paul Koch and Joakim Westerholm examine the personal trading of employees at Finnish financial intermediaries (experts) who have regular access to material private information. In Finland, regulations require that these experts disclose personal trades in any stock listed on the Nasdaq OMX Helsinki Exchange. Using  personal trading data for 1,249 experts at 40 Finnish financial intermediaries representing 90% of the Finnish fund management industry and 99% of the Finnish brokerage industry, plus aggregated trading data of Finnish retail investors, during August 2006 through August 2011, they find that: Keep Reading

Free Data and the Collapse of Trading Costs

How have costs of U.S. stock trading data evolved in recent years? In his October 2018 paper entitled “Retail Investors Get a Sweet Deal: The Cost of a SIP of Stock Market Data”, James Angel examines costs of U.S. stock market data. He also describes the production of these data and their consolidation/distribution via Securities Information Processors (SIP). Using data for U.S. trading costs as far back as 1987, he finds that:

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How Financial Journalists Work

How do journalists develop the information that appears in the financial media? In their November 2018 paper entitled “Meet the Press: Survey Evidence on Financial Journalists As Information Intermediaries”, Andrew Call, Scott Emett, Eldar Maksymov and Nathan Sharp report results of a survey of and follow-up interviews with financial journalists on inputs, incentives and beliefs that shape their reporting. Using 462 responses to a 14-question survey (emailed to 4,590 financial journalists) received during April 3, 2018 to May 3, 2018 and 18 follow-up interviews, they find that:

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