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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.

Risk of Financial Advisor Misconduct

How should investors assess the risk of financial advisor misconduct? In their March 2016 paper entitled “The Market for Financial Adviser Misconduct”, Mark Egan, Gregor Matvos and Amit Seru investigate the recent extent of misconduct among registered financial advisors (“advisors”) and financial advisory firms in the U.S. Their data include employment history, customer disputes, disclosed investigations and disciplinary events (civil, criminal and regulatory). Using information on 1.2 million registered financial advisors (644,277 current and 638,528 former) during 2005 through 2015, they find that: Keep Reading

Pick the Worst-performing Funds?

Is selecting mutual funds based on strong performance over the last three years helpful (discovering fund manager skill) or harmful (signaling imminent fund strategy mean reversion)? In the February 2016 version of their paper entitled “The Harm in Selecting Funds that Have Recently Outperformed”, Bradford Cornell, Jason Hsu and David Nanigian investigate future mutual fund performance based on recent past performance relative to stated benchmarks. They focus on a past performance interval of three years because: institutional consultants cite this measurement as one of the most important criterion for fund selection; and, Morningstar’s rating algorithm emphasizes three-year past performance. Specifically, every three years they:

  1. Rank funds by expense ratio and exclude the highest tenth as likely poor choices.
  2. Define Winner, Median and Loser funds as the tenths of the rest with the highest, middle (centered on the 50th percentile) and lowest benchmark-adjusted returns the past three years.
  3. Track the performance of the equally weighted and monthly rebalanced Winner, Median and Loser groups over the next three years.

Using benchmark-adjusted returns for actively managed U.S. equity mutual funds during January 1994 through December 2015, they find that: Keep Reading

Hedge Funds vs. Mutual Funds: Give and Take

Who are the givers and who are the takers among mutual funds and hedge funds? In their January 2016 paper entitled “Style and Skill: Hedge Funds, Mutual Funds, and Momentum”, Mark Grinblatt, Gergana Jostova, Lubomir Petrasek and Alexander Philipov analyze quarter-to-quarter changes in Form 13F stock holdings to assess investment styles and sources of performance for hedge funds and mutual funds. They focus on the interaction between portfolio weight changes and future stock returns to measure investing skill. They calculate fund alpha via adjustments for stock size, book-to-market ratio and (when appropriate) momentum. Using quarterly 13F filings of 589 mutual funds and 1,342 hedge funds during 1998 to 2012, they find that: Keep Reading

Following the Leaders On SeekingAlpha and StockTwits

Do SeekingAlpha and StockTwits offer valuable stock-picking information? In their March 2015 paper entitled “Crowds on Wall Street: Extracting Value from Collaborative Investing Platforms”, Gang Wang, Tianyi Wang, Bolun Wang, Divya Sambasivan, Zengbin Zhang, Haitao Zheng and Ben Zhao evaluate the stock-picking expertise available via SeekingAlpha and StockTwits. They tailor stock sentiment measures for these sources and relate these measures to future stock and stock market performance. They test ranking of author informativeness both directly via future stock returns and indirectly by level of reader interaction (comments). They then test strategies for exploiting sentiments of top authors. Finally, they summarize responses to a May 2014 survey of 500 SeekingAlpha authors (95 responses) and 500 non-contributing SeekingAlpha users (104 responses). Using SeekingAlpha content from launch in 2004 through March 2014, StockTwits content from launch in 2009 through February 2014 and daily returns (not including dividends) from associated individual stocks and S&P 500 SPDR (SPY) as a market proxy, they find that: Keep Reading

Mark Hulbert’s Nasdaq Newsletter Sentiment Index

“Mark Hulbert’s NASDAQ Newsletter Sentiment Index” reviews the usefulness of the Hulbert Stock Newsletter Sentiment Index (HSNSI), which “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). In recent years, he has shifted emphasis in his MarketWatch columns from HSNSI to the Hulbert Nasdaq Newsletter Sentiment Index (HNNSI), stating that: “Since the Nasdaq responds especially quickly to changes in investor mood, and because those timers are themselves quick to shift their recommended exposure levels, the HNNSI is the Hulbert Financial Digest’s most sensitive barometer of investor sentiment.” Is HNNSI useful? Using a small sample of 38 values of HNNSI over the period April 2010 through September 2015 (generated by searching MarketWatch.com for “HNNSI”) and contemporaneous daily closes of the S&P 500 Index, we find that: Keep Reading

Exploiting Crowdsourced Earnings Estimates and Stock Sentiments

Are readily available crowdsourced firm earnings estimates and stock sentiment measurements exploitable? In the September 2015 revision of their paper entitled “Tweet Sentiments and Crowd-Sourced Earnings Estimates as Valuable Sources of Information Around Earnings Releases”, Jim Kyung-Soo Liew,  Shenghan Guo and Tongli Zhang investigate whether earnings estimates from Estimize and sentiment measurements from iSentium usefully predict stock behavior after earnings announcements. Estimize aggregates inputs from students, independent researchers, private investors, sell-side professionals and buy-side analysts to generate earnings estimates. iSentium derives sentiment scores (ranging from -30 to +30) from real-time natural language processing of Twitter texts about stocks, market indexes and exchange-traded funds. The authors relate pre-announcement earnings estimates and sentiment to post-earnings announcement stock returns. Using Estimize and iSentium data as available, Wall Street consensus earnings estimates, actual firm quarterly earnings and associated stock returns for 16,840 earnings announcements during November 2011 through December 2014, they find that: Keep Reading

Technical vs. Fundamental Investment Recommendations

Are expert technicians or fundamentalists better forecasters of short-term and intermediate-term asset returns? In the August 2015 version of their paper entitled “Talking Numbers: Technical versus Fundamental Recommendations”, Doron Avramov, Guy Kaplanski and Haim Levy assess the economic value of dual technical and fundamental recommendations presented simultaneously on “Talking Numbers”, a CNBC and Yahoo joint broadcast… “featuring fundamental and technical recommendations before and during the market open. Dual recommendations are made by highly experienced analysts representing prominent institutions.” Recommendations address both individual stocks and asset classes, including U.S. and foreign broad equity indexes, sector/industry equity indexes, bonds, commodities and exchange rates. Using 1,000 dual recommendations on 262 stocks and 620 dual recommendations on other assets, along with associated price data, during November 2011 through December 2014, they find that: Keep Reading

Debating Active Share as Fund Performance Predictor

“Measuring the Level and Persistence of Active Fund Management” (pro) and “Fund Activeness Predicts Performance?” (con) summarize debate on the ability of Active Share, how much portfolio holdings differ from a benchmark index, to predict mutual fund performance. The authors of the con paper summarized in the latter (principals of AQR Capital Management) assert that “neither theory nor data justify the expectation that Active Share might help investors improve their returns.” In his June 2015 paper entitled “AQR in Wonderland: Down the Rabbit Hole of ‘Deactivating Active Share’ (and Back Out Again?)”, Martijn Cremers rejoins the debate by examining the methodology and motives of the con paper. Using data on active U.S. equity mutual funds from the original research, and holdings/performance data for seven AQR Capital Management funds offered to retail investors that concentrate in U.S. stocks as available through December 2014, he finds that: Keep Reading

Competitive Market Perspective on Fund Manager Skill

Do any mutual funds reliably generate significant alpha and, if so, do fund investors receive this alpha? In their June 2015 paper entitled “Active Managers Are Skilled”, Jonathan Berk and Jules Van Binsbergen examine interactions among equity mutual fund gross alpha, assets under management, fees and net alpha. To measure a practical gross alpha, they benchmark active mutual fund gross performance against an historical best-fit linear combination of net returns from contemporaneously available Vanguard funds. To account for the effects of mutual fund size, they measure monthly dollar value added by the fund manager as gross alpha times assets under management. This approach accounts for competition among funds, whereby investors chase an outperforming fund until its alpha drops to zero. They then estimate fund manager skill as average monthly valued added divided by the standard error of the monthly value added series. Using gross monthly returns and fees for a broad survivorship bias-free sample of of active equity mutual funds and net monthly returns for Vanguard mutual funds during January 1977 through March 2011, they find that: Keep Reading

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

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