Investing Expertise

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

Is Warren Buffett’s track record explicable and replicable? In the June 2018 update of their paper entitled “Buffett’s Alpha”, Andrea Frazzini, David Kabiller and Lasse Pedersen model Warren Buffett’s exceptional investing performance based on replicating exposures of Berkshire Hathaway overall and of its publicly traded holdings to six factors. Four of the factors are those conventionally used to explain stock returns: market return, size, book-to-market ratio and momentum. The other two factors are betting-against-beta (buy low beta and avoid high beta) and quality (profitable, growing, dividend-paying). They further create portfolios that track Berkshire Hathaway’s factor exposures, leveraged to the same active risk as Berkshire Hathaway. Using monthly stock returns and accounting data for a broad sample of U.S. stocks, quarterly Berkshire Hathaway SEC Form 13F holdings and monthly returns for six factors specified above during October 1976 through March 2017, along with contemporaneous open-end active mutual fund performance data, they find that:

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How do so many active managers who underperform passive investment alternatives continue to attract and retain investors? In their June 2018 paper entitled “How Active Management Survives”, J.B. Heaton and Ginger Pennington test the hypothesis that investors fall prey to the  conjunction fallacy, believing that hard work should generate outperformance. Specifically, they conduct two online surveys:

• Sample 1: 1,004 respondents over 30 with household income over $100,000 choosing which of two propositions is mostly likely true: “(1) ABC Fund will earn a good return this year for its investors. (2) ABC Fund will earn a good return this year for its investors and ABC Fund employs investment analysts who work hard to identify the best stocks for ABC Fund to invest in.”
• Sample 2: 1,001 respondents over 30 with household income over $100,000 choosing which of two propositions is mostly likely true: “(1) ABC Fund will earn a good return this year for its investors. (2) ABC Fund will earn a good return this year for its investors and ABC Fund was founded by a successful former Goldman Sachs trader and employs Harvard-trained physicists and Ph.D. economists and statisticians.”

Second choices are inherently less likely because they include the first choices and add conditions to them. The authors further ask in both surveys the degree to which respondents agree that a “person or business can achieve better results on any task by working harder than its competitors.” Using responses to these surveys, they find that: Keep Reading

A reader asked: “The American Association of Individual Investors (AAII) has a lot of strategies they have been paper-trading over many years at Stock Screens. It seems like every strategy builds upon a well-known investing book or otherwise publicized strategy from the last 40 years. Have you ever done an evaluation of those performance results?” According to AAII: “These approaches run the full spectrum, from those that are value-based to those that focus primarily on growth. Some approaches are geared toward large-company stocks, while others uncover micro-sized firms. Most fall somewhere in the middle.” AAII provides performance histories, risk-return statistics and characteristics for all screens. AAII cautions that: “The impact of factors such as commissions, bid-ask spreads, cash dividends, time-slippage (time between the initial decision to buy a stock and the actual purchase) and taxes is not considered. This overstates the reported performance…” Using monthly returns and turnovers for the equally weighted portfolios generated by the 60 screens presented during January 1998 through March 2018 (243 months), along with contemporaneous returns for SPDR S&P 500 (SPY), Vanguard Small Cap Index Fund (NAESX) and Vanguard Total Stock Market Index Fund (VTSMX), we find that: Keep Reading

Are financial advisors expert guides for their client investors? In their December 2017 paper entitled “The Misguided Beliefs of Financial Advisors”, Juhani Linnainmaa, Brian Melzer and Alessandro Previtero compare investing practices/results of Canadian financial advisors to those of their clients, including trading patterns, fees and returns. They estimate account alphas via multi-factor models. Using detailed data from two large Canadian mutual fund dealers (accounting for about 5% of their sector) for 3,276 Canadian financial advisors and their 488,263 clients, and returns and fees for 3,023 associated mutual funds, during January 1999 through December 2013, they find that:

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How can machine investors beat humans? In the introductory chapter of his January 2018 book entitled “Financial Machine Learning as a Distinct Subject”, Marcos Lopez de Prado prescribes success factors for machine learning as applied to finance. He intends that the book: (1) bridge the divide between academia and industry by sharing experience-based knowledge in a rigorous manner; (2) promote a role for finance that suppresses guessing and gambling; and, (3) unravel the complexities of using machine learning in finance. He intends that investment professionals with a strong machine learning background apply the knowledge to modernize finance and deliver actual value to investors. Based on 20 years of experience, including management of several multi-billion dollar funds for institutional investors using machine learning algorithms, he concludes that: Keep Reading

Want your machine to excel in investing? In his January 2018 paper entitled “The 10 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado examines common errors made by machine learning experts when tackling financial data and proposes correctives. Based on more than two decades of experience, he concludes that: Keep Reading

Why don’t machines rule the financial world? In his September 2017 presentation entitled “The 7 Reasons Most Machine Learning Funds Fail”, Marcos Lopez de Prado explores causes of the high failure rate of quantitative finance firms, particularly those employing machine learning. He then outlines fixes for those failure modes. Based on more than two decades of experience, he concludes that: Keep Reading

How accurate are consensus firm earnings forecasts worldwide at a 12-month horizon? In his May 2016 paper entitled “An Empirical Study of Financial Analysts Earnings Forecast Accuracy”, Andrew Stotz measures accuracy of consensus 12-month earnings forecasts by financial analysts for the companies they cover around the world. He defines consensus as the average for analysts coverings a specific stock. He prepares data by starting with all stocks listed in all equity markets and sequentially discarding:

1. Stocks with market capitalizations less than $50 million (U.S. dollars) as of December 2014 or the last day traded before delisting during the sample period.
2. Stocks with no analyst coverage.
3. Stocks without at least one target price and recommendation.
4. The 2.1% of stocks with extremely small earnings, which may results in extremely large percentage errors.
5. All observations of errors outside ±500% as outliers.
6. Stocks without at least three analysts, one target price and one recommendation.

He focuses on scaled forecast error (SFE), 12-month consensus forecasted earnings minus actual earnings, divided by absolute value of actual earnings, as the key accuracy metric. Using monthly analyst earnings forecasts and subsequent actual earnings for all listed firms around the world during January 2003 through December 2014, he finds that: Keep Reading

What happens to the rankings of Guru Grades after weighting each forecast by forecast horizon and specificity? In their March 2017 paper entitled “Evaluation and Ranking of Market Forecasters”, David Bailey, Jonathan Borwein, Amir Salehipour and Marcos Lopez de Prado re-evaluate and re-rank market forecasters covered in Guru Grades after weighting each forecast by these two parameters. They employ original Guru Grades forecast data as the sample of forecasts, including assessments of the accuracy of each forecast. However, rather than weighting each forecast equally, they:

• Apply to each forecast a weight of 0.25, 0.50, 0.75 or 1.00 according to whether the forecast horizon is less than a month/indeterminate, 1-3 months, 3-9 months or greater than 9 months, respectively.
• Apply to each forecast a weight of either 0.5 for less specificity or 1.0 for more specificity.

Using a sample of 6,627 U.S. stock market forecasts by 68 forecasters from CXO Advisory Group LLC, they find that: Keep Reading