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.

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Real-world Equity Fund Performance Benchmarks

Do equity style mutual funds look more attractive when benchmarked to matched style stock indexes than to more theoretical factor models of stock returns? In their April 2015 paper entitled “On Luck versus Skill When Performance Benchmarks are Style-Consistent”, Andrew Mason, Sam Agyei-Ampomah, Andrew Clare and Steve Thomas compare alphas for U.S. equity style mutual funds as calculated with conventional factor models and as calculated with matched Russell style indexes. The factor models they consider are the 1-factor capital asset pricing model (CAPM), the Fama-French 3-factor model (market, size, book-to-market) and the Carhart 4-factor model (adding momentum). They consider both value (net asset value)-weighted and equal-weighted portfolios of mutual funds. They also perform simulations to control for differences in the precision of alpha estimates due to differences in fund sample sizes. Using monthly gross and net returns and equity styles for 2,384 surviving and dead U.S. diversified equity funds, and returns for Russell equity style indexes and market/size/value/momentum factors, during January 1990 through December 2011, they find that: Keep Reading

Fund Activeness Predicts Performance?

Are mutual fund managers whose holdings deviate most from their benchmarks the best performers? In their April 2015 paper entitled “Deactivating Active Share”, Andrea Frazzini, Jacques Friedman and Lukasz Pomorski investigate whether Active Share is a reliable indicator of future mutual fund performance. Active Share measures the distance between a portfolio and its benchmark, ranging from zero for a portfolio that is identical to its benchmark to one for a portfolio with no holdings in common with its benchmark. They consider both theoretical arguments and empirical analysis, with the latter focused on disentangling Active Share and benchmark effects. Using holdings and performance data for actively managed U.S. equity mutual funds during 1980 through 2009, they find that: Keep Reading

Can Investors Outsmart Smart Beta ETFs?

Do smart beta exchange-traded funds (ETF), which systematically tilt holdings to capture one or more factor premiums (such as size, value, momentum, quality, beta and volatility), offer net value to investors? In the April 2015 initial draft of his paper entitled “How Smart are ‘Smart Beta’ ETFs? Analysis of Relative Performance and Factor Timing”, Denys Glushkov assesses whether smart beta funds beat their benchmarks and whether they effectively time targeted factor premiums. After categorizing smart beta ETFs into 15 portfolios based on factor themes and weighted by fund capitalizations (rebalanced monthly), he evaluates performance of these portfolios versus three types of benchmarks: (1) passive benchmarks chosen by the ETF providers, capitalization-weighted within 15 matching portfolios (rebalanced monthly to corresponding smart beta ETF weights); (2) risk-adjusted versions of these self-declared benchmarks; and, (3) tradable capitalization-weighted blends of market, value and size funds, matched to smart beta ETF portfolios based on inception dates (rebalanced annually to original weights). The blended benchmark addresses the concern that the returns of self-declared benchmarks are realistic/gross of reformation costs and tests whether smart beta funds add value to a simple and relatively passive capitalization-weighted portfolio with size and value tilts. Using monthly returns for 164 U.S. equity smart beta ETFs and 49 distinct benchmarks during 2003 through 2014, and detailed historical holdings of most of these funds, he finds that: Keep Reading

Incorporating the Experience of the Financial Crisis

How should financial education incorporate the experience of the 2007-2009 financial crisis? In their May 2014 publication entitled Investment Management: A Science to Teach or an Art fo Learn?, Frank Fabozzi, Sergio Focardi and Caroline Jonas summarize the current approach to teaching finance theory and examine post-crisis criticisms and defenses of this approach via review of textbooks and studies and through interviews with finance professors, asset managers and other market players. Based on these sources, they conclude that: Keep Reading

Exploiting Unusual Changes in Hedge Fund Holdings and Short Interest

Can investors exploit the combination of unusual changes in hedge fund long positions and unusual changes in short interest for individual stocks? In the February 2015 version of their paper entitled “Arbitrage Trading: The Long and the Short of It”, Yong Chen, Zhi Da and Dayong Huang examine the power of three variables to predict stock returns:

  1. Abnormal hedge fund holdings (AHF), the current quarter aggregate hedge fund long positions in a stock divided by the total shares outstanding minus the average of this ratio over the four prior quarters.
  2. Abnormal short interest (ASR), the current quarter short interest in a stock divided by the total number of shares outstanding minus the average of this ratio over the four prior quarters.
  3. The difference between AHF and ASR as a measure of imbalance in hedge fund trading.

They also examine how AHFSR interacts with ten widely used stock return predictors: book-to-market ratio; gross profitability; operating profit; momentum; market capitalization; asset growth; investment growth; net stock issuance; accruals; and, net operating assets. To measure the effectiveness of each predictor, they each quarter rank stocks into fifths (quintiles) based on the predictor and then calculate the difference in average gross excess (relative to the risk-free rate) returns of extreme quintiles. Using quarterly hedge fund SEC Form 13F filings and short interest data for a broad sample of U.S. stocks (excluding small and low-priced stocks), along with data required to compute stock return predictors and risk factors for these stocks, during 1990 through 2012, they find that: Keep Reading

Exploiting Interaction of Hedge Fund Holdings and Short Interest

Do changes in hedge fund holdings and short interest in a stock together predict its returns? In their January 2015 paper entitled “Short Selling Meets Hedge Fund 13F: An Anatomy of Informed Demand”, Yawen Jiao, Massimo Massa and Hong Zhang test whether joint changes in aggregate hedge fund holdings and short interest of a stock relate to its future returns. They define a contemporaneous increase (decrease) in aggregate hedge fund holdings and decrease (increase) in short interest as indicative of informed long (short) demand for a stock. They relate informed demand to abnormal return, the return of the stock relative to that of its style benchmark based on size, book-to-market and prior-period return. Using size/value characteristics, monthly returns, quarterly short interest and holdings from quarterly SEC Form 13F filings of 1,397 hedge funds for 5,357 U.S. stocks during 2000 through 2012, they find that: Keep Reading

Mutual Fund Trading Drives Performance?

Should investors expect mutual fund managers to generate value via timely trades? In their November 2014 paper entitled “Do Funds Make More When They Trade More?”, Lubos Pastor, Robert Stambaugh and Lucian Taylor investigate the relationship between mutual fund turnover and performance. They measure mutual fund performance at a monthly frequency as gross fund return minus the return on its Morningstar benchmark index. They measure turnover over the last 12 months via a methodology that largely excludes trading due solely to fund inflows and outflows. Using returns and turnovers for 3,126 active U.S. equity mutual funds during 1979 through 2011, they find that: Keep Reading

When Consensus Earnings Forecast and Stock Return Diverge

Do changes in consensus analyst earnings forecasts that disagree with contemporaneous stock returns signal exploitable mispricings? In their November 2014 paper entitled “To Follow or Not to Follow – An Analysis of the Profitability of Portfolio Strategies Based on Analyst Consensus EPS Forecasts”, Rainer Baule and Hannes Wilke investigate the power of a variable that relates consensus earnings forecast momentum to stock price momentum to predict stock returns. Specifically, the variable is the ratio of (1+change in consensus earnings forecast) to (1+stock return) over the last six months. Their consensus earnings forecast metric is a rolling average of consensus estimates for the current and next years weighted according to proximity of the current-year forecast to the end of the firm’s fiscal year (for example, three months before the end of the fiscal year, the rolling 12-month metric is 3/12 of the forecast for the current year plus 9/12 of the forecast for next year). They measure predictive power via a portfolio that is each month long (short) the fifth of stocks with the highest (lowest) last-month variable values. They evaluate both raw excess portfolio performance (relative to the risk-free rate) and four-factor portfolio alpha (adjusting for market, size, book-to-market and momentum factors). They limit the stock universe to the widely covered and very liquid components of the S&P 100 Index. Using monthly analyst consensus earnings forecasts and total returns for S&P 100 stocks during February 1978 through December 2013 (a total of 278 stocks listed for at least one month), they find that: Keep Reading

Why Stock Gurus Warn?

Does a need to attract attention distort the information offered by online stock bloggers? Does competition among them suppress or amplify this distortion? In their November 2014 paper entitled “Guru Dreams and Competition: An Anatomy of the Economics of Blogs”, Yi Dong, Massimo Massa and Hong Zhang investigate whether: (1) stock bloggers are informative; and, (2) competition among them enhances the quality of information provided. They start by relating blog activity to two proxies for informed versus liquidity trading. They then test the relationship between future stock returns and blog tone, with focus on tone extremism. Finally, they assess the impact of competition among stock bloggers, defining blog activity as competitive when the number of bloggers covering a stock is among the top fourth across all stocks. Using a hand-collected sample of blog articles covering S&P 1500 stocks during 2006 through 2011, they find that:

Keep Reading

Crowds of Experts Are Poor Market Timers Everywhere

Do expected investment returns as predicted by experts in surveys reliably predict actual future returns? In the October 2014 version of their preliminary paper entitled “Survey Expectations of Returns and Asset Pricing Puzzles”, Ralph Koijen, Maik Schmeling and Evert Vrugt compare survey-based expected returns to actual future returns for three major asset classes encompassing: 13 country equity market indexes; 19 currencies (versus the U.S. dollar); and, 10-year government bonds in 10 countries. They measure actual asset returns in U.S. dollars based on futures prices for equities and bonds (actual or synthetic) and forward returns for currencies. Survey-based expected returns derive from the quarterly World Economic Survey of experts, which solicits six-month expectations (“higher” or “about the same” or “lower”) for local equity prices, currency value versus the U.S. dollar and long-term government bond yield. The currency survey series commences the first quarter of 1989, while the equity and bond series commence the second quarter of 1998. They test the accuracy of survey expectations in two ways:

  1. Cross-sectional hedge portfolios that are each month long (short) the rank-weighted assets with the highest (lowest) survey expectations.
  2. Time series portfolios that are each month long (short) each asset depending on whether respective survey expectations indicate a positive (negative) return.

Analyses include testing of different lags between survey month and actual future return measurement, noting that a reliably executable strategy requires a lag of at least three months. Using quarterly survey response data and monthly futures/forward returns for the specified assets as available through September 2012, they find that: Keep Reading

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