Investing Expertise

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

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:

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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

How successful are active equity mutual fund managers in timing their domestic markets worldwide? In their August 2014 paper entitled “Market Timing Around the World”, Javier Vidal-Garcia, Marta Vidal and Duc Khuong Nguyen employ daily returns to measure the effectiveness of mutual fund market exposure adjustments made more frequently than monthly. They also examine fund timing performance under different economic conditions. Their fund universe consists of 8,680 actively managed, open-end, diversified, domestic live and dead equity mutual funds registered in 35 countries (about 69% are U.S.-registered). Using daily total returns in local currencies and characteristics for these funds, along with contemporaneous country economic data, during January 1990 through December 2013, they find that: Keep Reading

A subscriber proposed a simple test of the concept underlying the First Trust Dorsey Wright Focus 5 ETF (FV). This exchange-traded fund (ETF) intends to track the Dorsey Wright Focus Five Index, an equally weighted and weekly reformed portfolio of the five First Trust sector and industry ETFs with the highest price momentum according to the Dorsey, Wright & Associates relative strength ranking system. In the absence of a detailed specification for this ranking system, the subscriber proposed a conceptual test applying the rules for the “Simple Asset Class ETF Momentum Strategy” to the FV universe, which consists of the following 23 ETFs:

First Trust NASDAQ-100-Technology Sector Index Fund (QTEC))
First Trust NYSE Arca Biotechnology Index Fund (FBT)
First Trust Dow Jones Internet Index Fund (FDN)
First Trust ISE-Revere Natural Gas Index Fund (FCG)
First Trust ISE Water Index Fund (FIW)
First Trust S&P REIT Index Fund (FRI)
First Trust Consumer Discretionary AlphaDEX Fund (FXD)
First Trust Consumer Staples AlphaDEX Fund (FXG)
First Trust Health Care AlphaDEX Fund (FXH)
First Trust Technology AlphaDEX Fund (FXL)
First Trust Energy AlphaDEX Fund (FXN)
First Trust Financials AlphaDEX Fund (FXO)
First Trust Industrials/Producer Durables AlphaDEX Fund (FXR)
First Trust Utilities AlphaDEX Fund (FXU)
First Trust Materials AlphaDEX Fund (FXZ)
First Trust FTSE EPRA/NAREIT Developed Markets Real Estate Index Fund (FFR)
First Trust NASDAQ ABA Community Bank Index Fund (QABA)
First Trust NASDAQ Clean Edge Smart Grid Infrastructure Index Fund (GRID)
First Trust ISE Global Copper Index Fund (CU)
First Trust ISE Global Platinum Index Fund (PLTM)
First Trust NASDAQ CEA Smartphone Index Fund (FONE)
First Trust ISE Cloud Computing Index Fund (SKYY)
First Trust NASDAQ Technology Dividend Index Fund (TDIV)

At the end of each month, we allocate all funds to the equally weighted set of the five of these 23 ETFs with the highest total return over the past five months. Using monthly dividend-adjusted closing prices for these ETFs during May 2007 (when 15 of the ETFs are available) through August 2014 (88 months), we find that: Keep Reading

Should investors view financial experts as individuals who, through years of study and experience, overcome behavioral biases and reliably add value to investment decisions? In his May 2014 essay entitled “Are You Trying Too Hard?”, Wesley Gray summarizes research that compares the decision-making of experts to the performance of mechanical models across many fields. He highlights the relevance to of this research to investment decision-making. Based on the body of research pitting expert judgment against mechanical models, he concludes that: Keep Reading

How should investors assess systematic trading programs? In his August 2014 paper entitled “Evaluation of Systematic Trading Programs”, Mikhail Munenzon offers a non-technical overview of issues involved  in evaluating systematic trading programs. He defines such programs as automated processes that generate signals, manage positions and execute orders for exchange-listed instruments or spot currency rates with little or no human intervention. He states that the topics he covers are not exhaustive but should be sufficient for an investor to initiate successful relationships with systematic trading managers. Based on his years of experience as a systematic trader and as a large institutional investor who has evaluated many diverse systematic trading managers on a global scale, he concludes that: Keep Reading

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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