Investing Expertise

How should investors use Morningstar mutual fund ratings/grades to select mutual funds? In his July 2014 paper entitled “Morningstar Mutual Fund Measures and Selection Model”, John Haslem surveys the five kinds of Morningstar mutual fund ratings and grades: (1) Morningstar star ratings (one to five stars); (2) analyst ratings (gold, silver, bronze, neutral and negative); (3) total pillar ratings (positive, neutral or negative for fund people, process, parent, performance and price); (4) upside/downside capture ratios; and, (5) stewardship ratings (culture, incentives, fees, board quality and regulatory history). Based on the body of research about the predictive power of Morningstar ratings/grades, he chooses three criteria for screening mutual funds:

1. Star rating of 4 or 5 and analyst rating of gold or silver.
2. Upside capture ratios greater than downside capture ratios for all three of 3-year, 5-year and 10-year past performance intervals.
3. Total stewardship grade of A.

He applies these criteria to the set of Vanguard actively managed diversified (not sector) U.S. equity mutual funds. His selections are current winners, with empirical testing requiring future performance data. Applying the chosen criteria to the specified set of Vanguard funds (about 20 funds), he finds that: Keep Reading

Should investors believe that they can usefully time the stock market? If so, how big might “usefully” be? In their July 2014 paper entitled “Can Individual Investors Time Bubbles?”, Jussi Keppo, Tyler Shumway and Daniel Weagley investigate persistence in the ability of individual Finnish investors to time the stock market, with focus on timing of two bubbles/crashes. They measure investor timing performance by relating monthly flows into and out of the investor’s portfolio to next-month and next-quarter returns of the value-weighted HEX 25 Index (now the OMX Helsinki 25). They test for persistence by comparing an investor’s relative timing performance in the first half of the sample period (January 1995 through March 2002) to that in the second half (April 2002 through June 2009). They treat January 2000 and October 2007 as beginnings of market crashes and focus on whether an investor performed well during the 12 months before and after each peak. Using data on all trades by 1,386,540 individual Finnish investors during January 1995 through June 2009, they find that: Keep Reading

Are Morningstar’s Ultimate Stock-Pickers good stock pickers? In his June 2014 paper entitled “Using Random Portfolios to Evaluate the Performance of the Ultimate Stock-Pickers Index”, Stefaan Pauwels compares the quarterly volatility-adjusted performances of the Morningstar Ultimate Stock-Pickers (USP) top buys, top holdings and top sells to those of many randomly generated (zero-skill) portfolios. Morningstar specifies USP members as fund managers across a range of equity styles with: (1) tenure longer than average within style category; and, (2) 1-year, 3-year, 5-year and 10-year returns exceeding that of the broad equity market. Each quarter, Morningstar generates lists of top ten USP buys, holdings and sells. The study compares the volatility-adjusted returns of these equally weighted lists to those of 1,000 equally weighted portfolios of ten stocks randomly selected each quarter from the S&P 500 Index. He performs volatility adjustment by dividing quarterly return by the standard deviation of daily returns during the quarter. Using quarterly USP lists from the end of November 2010 through early September 2013 and contemporaneous quarterly total returns and daily returns for associated stocks and the stocks in the S&P 500 Index, he finds that:

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Is past performance a useful indicator of future performance for some kinds of mutual funds? In their April 2014 paper entitled “Differences in Short-Term Performance Persistence by Mutual Fund Equity Class”, Larry Detzel and Andrew Detzel evaluate performance persistence among diversified U.S. equity mutual funds categorized per the Morningstar Equity Style Box: Large Value (LV), Large Blend (LB), Large Growth (LG), Mid-Cap Value (MV), Mid-Cap Blend (MV), Mid-Cap Growth (MG), Small Value (SV), Small Blend (SB) or Small Growth (SG). Each quarter, they sort funds into styles and then rank them into fifths (quintiles) based on four-factor alpha (adjusting for market, size, book-to-market and momentum risks) calculated with daily returns. They then calculate average four-factor alphas for these quintiles during the next four quarters. Using quarterly Morningstar style assignments and daily returns for a large sample of live and dead diversified U.S. equity mutual funds, along with data for associated stocks and contemporaneous returns for risk factors, during January 1999 through December 2011, they find that: Keep Reading

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 ratings, to 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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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

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

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

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 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