Investing Expertise

Are newsletters good sources of stock picks? Specifically, do their recommendations persistently generate excess returns? In their October 1998 paper entitled “The Performance of Investment Newsletters”, Jeffrey Jaffe and James Mahoney tackle these questions. Using the investment newsletter archive of the Hulbert Financial Digest for 1980-1996, they determine that: Keep Reading

Is success as an entrepreneur all luck, or is there a provable contribution from skill? Do winners win just because they are willing to roll the dice, or because they consistently bring innovative insights to the market? In their July 2006 paper entitled “Skill vs. Luck in Entrepreneurship and Venture Capital: Evidence from Serial Entrepreneurs”, Paul Gompers, Anna Kovner, Josh Lerner and David Scharfstein pit skill against luck by investigating the persistence of success among serial entrepreneurs. Focusing on the founders of companies listed by Venture Source as recipients of venture capital during the period 1975-2000, they conclude that: Keep Reading

Suppose investors/traders were to apply the same standards to a stock market guru that federal courts apply to an expert witness. What, if anything, would they find admissible? Using as a guide the 2003 paper by Jennifer Mnookin and Samuel Gross entitled “Expert Information and Expert Evidence: A Preliminary Taxonomy”, we conclude that: Keep Reading

Does a natural selection metaphor apply to stock picking models? In other words, can competition among a large set of dynamic models to mimic historical stock performance data evolve the most fit models? In their May 2006 paper entitled “Stock Selection – An Innovative Application of Genetic Programming Methodology”, Ying Becker, Peng Fei and Anna Lester address these questions by applying genetic programming to stock picking. Genetic programming enables the testing of a wide range of stock performance indicators in linear, non-linear and non-obvious combinations. The authors choose the S&P 500, excluding financials and utilities, as their universe of stocks and define two distinct types of stock-picking model fitness: (1) risk-adjusted outperformance compared to a traditional stock-picking model; and, (2) highest possible return independent of risk. They construct for comparison a traditional stock return forecasting model based on a linear combination of four composite factors: valuation, quality, analyst expectations and price. They use monthly data (65 variables for each of about 350 stocks) over the period January 1990 through December 2005 to create environments for model development and out-of-sample testing. They show that: Keep Reading

In his article entitled “The Seer-Sucker Theory: The Value of Experts in Forecasting” from the June/July 1980 issue of Technology Review, Scott Armstrong investigates the general supply of and demand for expertise across several disciplines. Based upon his survey of decades of research in multiple fields (including financial markets, psychology, health care, politics, sports), he concludes that: Keep Reading

In his 2005 book Expert Political Judgment: How Good is It? How Can We Know?, Philip Tetlock describes the results of his long-term systematic measurement of the forecasting abilities of political experts. These results include insights into the critical success factors of forecasting. Making the very small leap that these insights apply also to experts in economics and financial markets, we offer here a chapter-by-chapter review of the insights in this book: Keep Reading

We get a varying flow of traffic from Yahoo! message boards, usually from boards of “Cramerized” stocks to either our review of Jim Cramer’s market timing record or our evaluation of his stock picking ability. Who reads these stock boards? Using a sample of 212 unique Internet Protocol (IP) addresses for readers recently visiting from five different Yahoo! message boards, we find that: Keep Reading

What makes a guru, or analyst, good? The research on this question is predominantly “technical” rather than “fundamental,” focusing on performance and performance persistence rather than process (hence, the frequent use of the word guru, implying mystical insight). In their preliminary and incomplete paper of November 2004 entitled “Determinants of Superior Stock Picking Ability”, Michael Mikhail, Beverly Walther, Xin Wang and Richard Willis seek to identify the determinants of consistent analyst stock picking outperformance. Using a sample encompassing 268,170 recommendations issued by 4,923 analysts for 7,845 firms during 1985-1999 from Zacks Investment Research, they tentatively find that the best analysts tend to: Keep Reading

Recent research on the (stock picking and market timing) abilities of experts to generate excess returns has focused mostly on mutual funds and hedge funds. This focus stems from data availability (mutual funds via SEC filings) and headline value (hedge funds). Where is the research on investment newsletters? How do they rate in terms of excess returns? Digging deeper than usual, we find two on-target papers: (1) the February 1995 paper entitled “Market Timing Ability and Volatility Implied in Investment Newsletters’ Asset Allocation Recommendations” by John Graham and Campbell Harvey; and, (2) the November 1997 paper entitled “The Equity Performance of Investment Newsletters” by Andrew Metrick. Both papers draw upon the investment newsletter archive of the Hulbert Financial Digest. Using different aspects of this archive, they determine that: Keep Reading

Can investors count on the widely cited Morningstar mutual fund rating system as an investment screener? In their recent paper “Morningstar Mutual Fund Ratings Redux”, Matthew Morey and Aron Gottesman investigate the relationship between number of Morningstar stars and future performance of mutual funds since June 30, 2002, when Morningstar overhauled their rating system in terms of granularity, risk measurement and treatment of share classes. Focusing on the three-year performance of domestic equity funds that were rated by Morningstar as of 6/30/02 (1,902 funds) and adjusting for fund loads and survivorship bias, they conclude that: Keep Reading