Investing Expertise

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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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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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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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

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 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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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

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

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

In the June 2018 draft of their paper entitled “An Information Factor: Can Informed Traders Make Abnormal Profits?”, Matthew Ma, Xiumin Martin, Matthew Ringgenberg and Guofu Zhou construct and test a long-short information factor (INFO) based on observed trading of firm insiders, short sellers and option traders. Specifically, the INFO portfolio:

• Is each month long the 10% (decile) of stocks with the highest levels of net buying (purchases minus sales) by top managers scaled by the average number of shares held by all top managers over the calendar year.
• Is each month short stocks based on both short interest (number of shares short divided by shares outstanding) and associated option trading activity (volume of liquid put and call options divided by volume of associated stock). They sort stocks independently on short interest and option trading activity, add the two ranks for each stock and short the decile of stocks with the highest combined ranks.

They further examine whether INFO is a key driver of hedge fund returns. Using monthly data for specified variables, monthly returns for a broad sample of U.S. stocks priced over $5 and monthly returns for 13 hedge fund indexes and 5,565 individual U.S. equity hedge funds during February 1996 (limited by options data) through December 2015, they find that: Keep Reading