Investing Expertise

Are robo-analysts, who apply technology to mass-produce recommendations with limited human intervention, better stock pickers than traditional human analysts? In their January 2020 preliminary (and incomplete) paper entitled “Man Versus Machine: A Comparison of Robo-Analyst and Traditional Research Analyst Investment Recommendations”, Braiden Coleman, Kenneth Merkley and Joseph Pacelli compare distribution, revision frequency and performance for stock recommendations from robo-analysts versus traditional analysts. In measuring performance, they consider 3-factor (adjusting for market, size and book-to-market factors) and 5-factor (additionally adjusting for profitability and investment factors) alphas of daily rebalanced portfolios of buy or sell recommendations, with a lag of one day between recommendations and trades. Using 134,781 reports issued by seven prominent Robo-Analyst firms and by traditional analysts for 1,002 stocks covered by at least three analysts for at least five years during 2003 through 2018, they find that:

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Are there any investors who have compellingly beaten the market? In his December 2019 paper entitled “Medallion Fund: The Ultimate Counterexample?”, Bradford Cornell reviews performance of the Medallion Fund from Renaissance Technologies as a clear refutation of market efficiency. He focuses on gross returns (including portfolio trading frictions, but not management fees), because they reflect the value added by the fund manager. Using Medallion Fund returns from its inception in 1988 through 2018, he finds that: Keep Reading

Do differences in expectations between institutional and individual investors in stocks and bonds, as quantified in weekly legacy Commitments of Traders (COT) reports, offer exploitable timing signals? In the February 2019 revision of his paper entitled “Want Smart Beta? Follow the Smart Money: Market and Factor Timing Using Relative Sentiment”, flagged by a subscriber, Raymond Micaletti tests a U.S. stock market-U.S. bond market timing strategy based on an indicator derived from aggregate equity and Treasuries positions of institutional investors (COT Commercials) relative to individual investors (COT Non-reportables). This Smart Money Indicator (SMI) has three relative sentiment components, each quantified weekly based on differences in z-scores between standalone institutional and individual net COT positions, with z-scores calculated over a specified lookback interval:

1. Maximum weekly relative sentiment for the S&P 500 Index over a second specified lookback interval.
2. Negative weekly minimum relative sentiment in the 30-Year U.S. Treasury bond over this second lookback interval.
3. Difference between weekly maximum relative sentiments in the 10-Year U.S. Treasury note and 30-year U.S. Treasury bond over this second lookback interval.

Final SMI is the sum of these components minus median SMI over the second specified lookback interval. He considers z-score calculation lookback intervals of 39, 52, 65, 78, 91 and 104 weeks and maximum/minimum relative sentiment lookback intervals of one to 13 weeks (78 lookback interval combinations). For baseline results, he splices futures-only COT data through March 14, 1995 with futures-and-options COT starting March 21, 1995. To account for changing COT reporting delays, he imposes a baseline one-week lag for using COT data in predictions. He focuses on the ability of SMI to predict the market factor, but also looks at its ability to enhance: (1) intrinsic (time series or absolute) market factor momentum; and, (2) returns for size, value, momentum, profitability, investment, long-term reversion, short-term reversal, low volatility and quality equity factors. Finally, he compares to several benchmarks the performance of an implementable strategy that invests in the broad U.S. stock market (U.S. Aggregate Bond Total Return Index) when a group of SMI substrategies “vote” positively (negatively). Using weekly legacy COT reports and daily returns for the specified factors/indexes during October 1992 through December 2017, he finds that: Keep Reading

Can investors exploit information about hedge fund stock holdings in SEC Form 13F filings? In their October 2019 paper entitled “Systematic 13F Hedge Fund Alpha”, Mobeen Iqbal, Farouk Jivraj and Luca Angelini investigate whether carefully culled “best ideas” of equity hedge funds produce significantly beat the S&P 500 Total Return (TR) Index. Using quarterly Form 13Fs for U.S. equity long-short, equity market neutral, equity long-only and equity event-driven hedge funds, they measure: individual hedge fund manager conviction regarding a stock based on size of position; and, hedge fund manager consensus regarding a stock based on the number of funds holding it. Using proprietary data, they identify hedge funds exhibiting long-term investment approaches. They then 47 days after the end of each quarter (to ensure availability of Form 13Fs), reform a portfolio from among long-term hedge funds holding at least five stocks, as follows:

1. Exploit conviction by identifying all stocks comprising at least 7.5% of a fund portfolio.
2. Exploit consensus by buying the equal-weighted top 50 of these stocks in terms of number of hedge managers holding them. 

Using processed quarterly data from hedge fund Form 13Fs, the specified proprietary data on hedge fund investment approaches and returns for associated stocks during the first quarter of 2004 through the second quarter of 2019, they find that:

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Who beats the stock market and why? In his October 2019 paper entitled “The Five Investor Camps That Try to Beat the Stock Market”, William Ziemba discusses how different categories of investors succeed. For investors pursuing active strategies, he addresses broadly the means of getting an edge and betting well. Based on his academic work and practical experience, he concludes that: Keep Reading

What are asset class return expectations and associated portfolio allocations of very sophisticated U.S. investors? In their February 2019 paper entitled “The Return Expectations of Institutional Investors”, Aleksandar Andonov and Joshua Rauh analyze disclosures of expected returns across asset classes among U.S. public pension funds, which hold assets of about $4 trillion (see the first chart below), including fixed income, cash, equities, real assets, hedge funds, private equity and other asset classes. Taking into account past fund performance, they investigate how fund managers estimate future returns. Disclosures also reveal target allocations to asset classes (see the second chart below). Together, expected asset class returns and target allocations allow calculation of expected portfolio returns. Using annual disclosures for 228 U.S. state and local government pension plans during 2014 through 2017, they find that:

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Are there relationships between (1) the stock market outlook expressed by a U.S. equity mutual fund manager in semi-annual reports and (2) positioning and performance of that fund? In his October 2019 preliminary paper entitled “Are Professional Investors Prone to Behavioral Biases? Evidence from Mutual Fund Managers”, Mehran Azimi examines these relationships. Specifically, for each such U.S. equity mutual fund semi-annual report, he:

1. Uses a word list to identify parts of fund reports that may contain stock market outlooks.
2. Applies machine learning to isolate sentences most likely to present outlooks.
3. Manually reads and rates these sentences as bearish, neutral or bullish.
4. Computes fund manager “Belief” as number of bullish sentences minus number of bearish sentences divided by the total number of sentences isolated. Positive (negative) Belief indicates a net bullish (bearish) outlook.

He then employs regressions to relate fund manager Belief to fund last-year return, asset allocation, portfolio risk and next-year 4-factor (adjusting for market, size, book-to-market and momentum) alpha. Using 40,731 semi-annual reports for U.S. equity mutual funds and associated fund characteristics, holdings and returns during February 2006 through December 2018, he finds that:

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What are ways to mitigate biases that interfere with rational investment decision-making? In their September 2019 paper entitled “The Psychology of Financial Professionals and Their Clients”, Kent Baker, Greg Filbeck and Victor Ricciardi describe common psychological biases and suggest ways to overcome them. Based on their knowledge and experience, they conclude that: Keep Reading

When media recommend stocks, should investors pay attention? To check, we look at performance of stock recommendations for 2019 from December 2018 articles in several publications. Specifically, we test:

• 10 buy recommendations from InvestorPlace published 12/13/2018.
• 19 buy recommendations from Kiplinger published 12/7/2018.
• 8 buy recommendations from Motley Fool published 12/13/2018, 12/21/2018 and 12/22/2018.
• 10 buy recommendations from TheStreet published 12/3/2018 and 12/14/2018.
• 19 buy recommendations from USA Today published 12/28/2018.
• 8 buy recommendations from US News published 12/14/2018.

For each source, we form equally weighted portfolios of recommended stocks at the close on 12/31/2018 and hold without rebalancing. For a broader perspective, we form an equally weighted portfolio of all recommended stocks (Overall). Because the sample period is very short, we focus on daily performance statistics, but also look at cumulative returns. We use SPDR S&P 500 (SPY) as a benchmark. Using daily dividend-adjusted prices of the 74 recommended stocks and SPY during 12/31/2018 through October 15, 2019, we find that: Keep Reading

Is there a way that asset managers can share knowledge/data across proprietary boundaries with many researchers to advance development of investment strategies? In their September 2019 paper entitled “Crowdsourced Investment Research through Tournaments”, Marcos Lopez de Prado and Frank Fabozzi describe highly structured tournaments as a crowdsourcing paradigm for investment research. In each such tournament, the organizer poses one investment challenge as a forecasting problem and provides abstracted and obfuscated data. Contestants pay an entry fee, develop models and provide forecasts, retaining model ownership by running calculations on their own hardware/software. Based on this hypothetical tournament setup and their experience, they conclude that:

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