Objective research to aid investing decisions

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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.

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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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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Active Mutual Fund Management Still Worthless?

Does recent research on active mutual fund performance challenge conventional wisdom that: (1) the average fund underperforms passive benchmarks on a net basis; and, (2) individual fund outperformance does not persist. In their September 2018 paper entitled “Challenging the Conventional Wisdom on Active Management: A Review of the Past 20 Years of Academic Literature on Actively Managed Mutual Funds”, Martijn Cremers, Jon Fulkerson and Timothy Riley review academic research on active mutual funds from the last 20 years to assess the degree to which it supports this conventional wisdom. They focus on U.S. equity mutual funds but also consider bond funds, hybrid stock-bond funds, socially responsible funds, target date funds, real estate investment trust (REIT) funds, sector funds and international funds. Based on this research, they conclude that: Keep Reading

Active vs. Passive U.S. Equity Mutual Funds in Recent Years

Do active U.S. equity mutual funds beat their passive counterparts in recent years? In the September 2018 version of his paper entitled “The Historical Record on Active vs. Passive Mutual Fund Performance”, David Nanigian compares risk-adjusted annual performance of active versus passive U.S. equity mutual funds as categorized and monitored in the Morningstar Direct survivorship bias-free database. He measures rise-adjusted performance based on the Carhart 4-factor model (accounting for market, size, book-to-market and momentum factors) alpha. He considers both value-weighted (VW), based on fund assets under management at the end of the prior month, and equal-weighted (EW) combinations of funds. In addition to the full sample, he considers separately funds in the bottom fifth (quintile) of expense ratios. He also compares active and passive funds paired based on similar expense ratios. Using monthly fund data as specified during 2003 through 2017, he finds that: Keep Reading

A Few Notes on The Wealth Elite

Rainer Zitelmann prefaces his 2018 book, The Wealth Elite: A Groundbreaking Study of the Psychology of the Super Rich, as follows: “For this book, I succeeded in convincing 45 wealthy people to talk to me. …Without exception, the interviewees were entrepreneurs or investors… The interviews were conducted in person between September 2015 and March 2016, and each lasted between one and two hours. …every interviewee (with one exception) took a personality test consisting of 50 questions. …This work explores the personalities and patterns of behaviour exhibited by wealthy individuals. …their answers to my questions clearly demonstrate that the personality traits and patterns of behaviour described in this book have played a significant role in their extraordinary economic success. However, this is a study based on methods of qualitative social research and, as such, the interview subjects do not constitute a representative sample. Above all, their answers were not tested against a control group consisting of non-wealthy individuals.” Based on the body of wealth creation research and the set of in-depth interviews/personality tests, he concludes that: Keep Reading

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