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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.

Usefulness of Morningstar’s Qualitative Fund Ratings

Do Morningstar’s analyst ratings predict which mutual funds will do best? In their January 2014 paper entitled “Going for Gold: An Analysis of Morningstar Analyst Ratings”, Will Armstrong, Egemen Genc and Marno Verbeek examine the performance of mutual funds after Morningstar assigns analyst ratings to them. Morningstar initiated these substantially qualitative ratings (Gold, Silver, Bronze, Neutral and Negative) in September 2011, as a supplement to star ratingsto convey expected risk-adjusted performance of funds with respect to peers over a full market cycle of at least five years. Ratings take into account past performance, fees and trading costs, quality of investment team, parent organization and investment process.  The study considers both raw returns and four-factor (market, size, book-to-market, momentum) alphas during intervals of one, three and six months after each rating initiation. It also takes into account differences in time frame, fund investment style and fund star rating. Using analyst ratings initiated during September 2011 through December 2012, associated fund characteristics and associated fund returns through June 2013, they find that:

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Assessing Active Investment Managers

Do active investment managers beat the market? In their January 2014 paper entitled “Active Manager Performance: Alpha and Persistence”, Frank Benham and Edmund Walsh assess the performance of active investment managers relative to appropriate benchmarks across asset classes over long periods. They consider six basic investment classes: core bonds; high-yield bonds; domestic large capitalization stocks; domestic small capitalization stocks; foreign large capitalization stocks; and, emerging markets stocks. They focus on whether investment managers beat benchmarks in the past and whether past outperformers become future outperformers. They take steps to avoid survivorship bias, selection bias and fund classification errors. Using a sample of 5,379 live and dead funds assembled from Morningstar Direct by filtering to avoid classification errors and to eliminate redundant funds run by the same manager from benchmark inceptions (ranging from January 1979 for domestic stocks to January 1988 for emerging markets stocks) through 2012, they find that: Keep Reading

Why Analysts Miss Targets?

Do professional analysts systematically miss target prices for individual stocks? In the November 2013 draft of their paper entitled “Understanding and Predicting Target Price Valuation Errors”, Patricia Dechow and Haifeng You measure the errors in returns implied by professional stock analyst consensus price targets and examine the sources of these errors. They further investigate whether investors can anticipate and exploit consensus target price errors. They construct consensus target prices at the end of each month as the simple average of the most recent target price forecasts issued by following analysts within the last 90 days. Using analyst stock price targets, actual monthly returns and trading volumes, firm accounting data and institutional ownership data spanning April 1999 through December 2011 (227,127 firm-month observations), they find that: Keep Reading

Room for Manipulation by Stock Analysts?

How do professional analysts value stocks? In their March 2014 paper entitled “Peering Inside the Analyst ‘Black Box’: How Do Equity Analysts Model Companies?”, Andreas Markou and Simon Taylor examine the private stock valuation models of a group of analysts working in research departments of large investment banks. They examine both modeling methods and inputs. Using 53 Excel-based valuation models from professional analysts covering the European healthcare and chemicals sectors acquired during the third quarter of 2009, they conclude that: Keep Reading

Guru Grades Project Milestones

As of the end of 2012, we stopped adding forecasts to the Guru Grades database and published a preliminary report on findings. As of the end of 2013, we have completed the grading of all forecasts added during 2012 and now publish a final Guru Grades report.

The final report encompasses 6,582 graded forecasts from 68 gurus, adding 123 forecasts to the preliminary study (an increment of about 2%). The final report also incorporates a few minor corrections. The relatively small number of additional grades and corrections do not materially affect preliminary findings. Specifically:

  • Terminal forecast accuracy is still 46.9%.
  • Averaged by guru rather than across all forecasts, terminal accuracy is still 47.4%.

Accuracy rates change for individual gurus involved in the 123 incremental forecasts. We have updated the detailed forecast grading for these individuals. The histogram of guru accuracies in the final report is somewhat more symmetric than that in the preliminary report.

The final Guru Grades report will remain available indefinitely as a caution to investors on: (1) the (un)predictability of complex systems such as financial markets; and, (2) the risk or relying on grades self-assigned by students of financial markets.

Do Ph.D. Holders Make Better Money Managers?

Do funds that have Ph.D. holders in key positions outperform those that do not? In their October 2013 paper entitled “What a Difference a Ph.D. Makes: More than Three Little Letters”, Ranadeb Chaudhuri, Zoran Ivkovich, Joshua Pollet and Charles Trzcinka investigate whether institutional money management firms that rely on key personnel holding Ph.D. degrees outperform those that do not. They also test publication of articles in leading economics and finance journals as a discriminator among Ph.D. holders. They focus on U.S. equity funds, which comprise over half of assets under institutional money management. They first match funds with and without Ph.D. holders in key positions by investment objective (12 styles), assets under management and past performance. They then examine the Ph.D./non-Ph.D. performance differential based on several performance measures. Using information self-reported by institutional money management firms for U.S. equity funds (performance, fee and key personnel biographies), and separate article searches in leading publications, during June 1993 through December 2007, they find that: Keep Reading

Buy and Sell Recommendations Around the World

Do equity analyst stock recommendations have the same post-publication predictive value worldwide? In the September 2013 version of their paper entitled “Where are the World’s Best Analysts?”, Campbell Harvey, Sam Radnor, Khalil Mohammed and William Ferreira assess the ability of equity analysts by global region (Asia ex-Japan, Japan, Europe ex-UK, the UK and the U.S.) to forecast returns of the stocks they cover by measuring average cumulative returns after buy and sell recommendations. They adjust all returns based on regional stock indexes. Using analyst forecasts during 2005 (after regulations regarding analyst research change) through 2012, they find that: Keep Reading

Investment Consultant Expertise?

Do expert investment consultants advising institutions recommend outperforming funds? In their September 2013 paper entitled “Picking Winners? Investment Consultants’ Recommendations of Fund Managers”, Tim Jenkinson, Howard Jones, and Jose Vicente Martinez analyze survey data to determine what factors drive consultant fund recommendations, what impact their recommendations have on fund flows and how well their recommendations perform. While individual investment consultants do not publicly disclose their recommendations, survey responses allow assessment of aggregate recommendations (a list of top-rated funds) from consultants accounting for over 90% of the U.S. consulting market. To assess recommended fund performance, the authors compare recommended funds to style-matched alternatives based on returns and three-factor (market, size, book-to-market) and four-factor (plus momentum) alphas. They measure performance net of trading frictions but gross of fees of both fund managers and the investment consultants themselves. Using responses from an average 29 investment consultants to annual survey questions on their recommendations of long-only active U.S. equity funds, along with size/performance data for the funds they rate and similar funds, during 1999 through 2011 (13 years), they find that: Keep Reading

Mark Hulbert’s Stock Newsletter Sentiment Index

A reader suggested a review of the stock market commentary of Mark Hulbert, editor of the Hulbert Financial Digest, which tracks the recommendations of a wide range of investing newsletters. He is also a regular columnist at MarketWatch. Because Mark Hulbert uses his Hulbert Stock Newsletter Sentiment Index (HSNSI) as a principal quantitative tool in formulating his market outlook, we evaluate the usefulness of that index in predicting stock market returns rather than his qualitative commentary. HSNSI “reflects the average recommended stock market exposure among a subset of short-term market timers tracked by the Hulbert Financial Digest.” Mark Hulbert presents HSNSI as a contrarian signal for future stock returns; when HSNSI is high (low), he views the outlook for stocks as materially bearish (bullish). Using a sample of 287 values of HSNSI over the period July 2002 through September 2013 (generated by searching MarketWatch.com for “HSNSI” and its predecessor “HSSI”) and contemporaneous daily closes of the S&P 500 Index, we find that: Keep Reading

Trading Habits of Highly Successful Hedge Fund Managers

What are the trading behaviors of the best-performing hedge funds? In his June 2013 paper entitled “How do Hedge Fund ‘Stars’ Create Value? Evidence from Their Daily Trades”, Russell Jame uses transaction-level data to investigate the magnitude and source of hedge fund equity trading profits. His sample includes name, equity trade dates (but not non-equity trades, if any), execution prices and transaction costs for 74 hedge funds and 579 other institutions over a 12-year period. He estimates performance by constructing buy and sell portfolios from trades and computing portfolio-level returns over intervals of the next 21, 63, 126 and 252 trading days (emphasizing 252 days as closest to the average holding period of a typical hedge fund). He excludes portfolios with fewer than ten stocks as too noisy. He considers gross return, gross DGTW-adjusted return (return on a stock less the value-weighted return on a benchmark portfolio with the same size, book-to-market and momentum characteristics as the stock) and net DGTW-adjusted return. Using detailed trading data as described during January 1999 through December 2010 and associated stock prices through December 2011, he finds that: Keep Reading

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