Animal Spirits

How do so many active managers who underperform passive investment alternatives continue to attract and retain investors? In their June 2018 paper entitled “How Active Management Survives”, J.B. Heaton and Ginger Pennington test the hypothesis that investors fall prey to the  conjunction fallacy, believing that hard work should generate outperformance. Specifically, they conduct two online surveys:

• Sample 1: 1,004 respondents over 30 with household income over $100,000 choosing which of two propositions is mostly likely true: “(1) ABC Fund will earn a good return this year for its investors. (2) ABC Fund will earn a good return this year for its investors and ABC Fund employs investment analysts who work hard to identify the best stocks for ABC Fund to invest in.”
• Sample 2: 1,001 respondents over 30 with household income over $100,000 choosing which of two propositions is mostly likely true: “(1) ABC Fund will earn a good return this year for its investors. (2) ABC Fund will earn a good return this year for its investors and ABC Fund was founded by a successful former Goldman Sachs trader and employs Harvard-trained physicists and Ph.D. economists and statisticians.”

Second choices are inherently less likely because they include the first choices and add conditions to them. The authors further ask in both surveys the degree to which respondents agree that a “person or business can achieve better results on any task by working harder than its competitors.” Using responses to these surveys, they find that: Keep Reading

Do firms with predictable sales seasonality continually “surprise” investors with good high season (bad low season) sales and thereby have predictable stock return patterns? In their May 2018 paper entitled “When Low Beats High: Riding the Sales Seasonality Premium”, Gustavo Grullon, Yamil Kaba and Alexander Nuñez investigate firm sales seasonality as a stock return predictor. Specifically, for each quarter, after excluding negative and zero sales observations, they divide quarterly sales by annual sales for that year. To mitigate impact of outliers, they then average same-quarter ratios over the past two years. They then each month:

1. Use the most recent average same-quarter, two-year sales ratio to predict the ratio for next quarter for each firm.
2. Rank firms into tenths (deciles) based on predicted sales ratios.
3. Form a hedge portfolio that is long (short) the market capitalization-weighted stocks of firms in the decile with the lowest (highest) predicted sales ratios.

Their hypothesis is that investors undervalue (overvalue) stocks experiencing seasonally low (high) sales. They measure portfolio monthly raw average returns and four alphas based on 1-factor (market), 3-factor (market, size, book-to-market), 4-factor (adding momentum to the 3-factor model) and 5-factor (adding profitability and investment to the 3-factor model) models of stock returns. Using data for a broad sample of non-financial U.S common stocks during January 1970 through December 2016, they find that: Keep Reading

Does retail investor preference for stocks with skewed return distributions explain stock return anomalies? In their April 2018 paper entitled “Skewness Preference and Market Anomalies”, Alok Kumar, Mehrshad Motahari and Richard Taffler investigate whether investor preference for positively-skewed payoffs is a common driver of mispricing as indicated by a wide range of market anomalies. They each month measure the skewness of each stock via four metrics: (1) jackpot probability (probability of a return greater than 100% the next 12 months); (2) lottery index (with high relating to low price, high volatility and high skewness; (3) maximum daily return the past month; and, (4) expected idiosyncratic skewness. They also each month measure aggregate mispricing of each stock as its average decile rank when sorting all stocks into tenths on each of 11 widely used anomaly variables. They assess the role of retail investors based on 1991-1996 portfolio holdings data from a large U.S. discount broker. Using a broad sample of U.S. common stocks (excluding financial stocks, firms with negative book value and stocks priced less than $1) during January 1963 through December 2015, they find that: Keep Reading

Does investing “leader” overconfidence (self-attribution bias) transfer bad trading practices to other non-professional investors who participate in a social trading platform? In their March 2018 paper entitled “Self-Attribution Bias and Overconfidence Among Nonprofessional Traders”, Daniel Czaja and Florian Röder employ data from a large European social trading platform to examine: (1) how self-enhancement (attributing successes to self) and self-protection (attributing failures to external factors) components of self-attribution bias affect non-professional trading performance; and, (2) how social trading platforms transfer any such effects to other non-professional traders. The selected platform lets traders (leaders) manage and comment on virtual portfolios publicly. When enough other traders (followers) express interest in such a portfolio, a business partner of the platform offers a product that replicates its performance. After excluding portfolios managed by professional asset management firms, the authors perform content analysis on leader trading comments to measure the difference between first-person pronouns and third-person pronouns as indicators of self-enhancement and self-protection biases. They then relate leader bias to leader future performance and to inflows of associated investable portfolios from followers. Using daily transaction and performance data for 3,519 social trading portfolios managed by 2,010 European non-professional traders and available for investment for at least 360 days, including 45,623 leader comments, during June 2012 through November 2016, they find that:

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In his January 2018 retrospective “Richard Thaler and the Rise of Behavioral Economics”, Nicholas Barberis reviews the development of behavioral (less than fully rational) models of economics and finance, with focus on Richard Thaler’s contributions. This retrospective summarizes key models that make psychology-based assumptions about: individual preferences; individual beliefs; and, the process by which individuals make decisions. He further segments work on individual preferences into: preferences over riskless choices; preferences over risky choices; time preferences; and, social preferences. From the body of behavioral finance ideas and research since the 1970s, he highlights:

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Does appearance-based masculinity predict hedge fund manager performance? In their January 2018 paper entitled “Do Alpha Males Deliver Alpha? Testosterone and Hedge Funds”, Yan Lu and Melvyn Teo use facial width-to-height ratio (fWHR) as a positively related proxy for testosterone level to investigate the relationship between male hedge fund manager testosterone level and hedge fund performance. They each year in January sort hedge funds into tenths (deciles) based on fund manager fWHR and then measure the performance of these decile portfolios over the following year. Their main performance metric is 7-factor hedge fund alpha, which corrects for seven risks proxied by: (1) S&P 500 Index excess return; (2) difference between Russell 2000 Index and S&P 500 Index returns; (3) 10-year U.S. Treasury note (T-note) yield, adjusted for duration, minus 3-month U.S. Treasury bill yield; (4) change in spread between Moody’s BAA bond and T-note, adjusted for duration; and, (5-7) excess returns on straddle options portfolios for currencies, commodities and bonds constructed to replicate trend-following strategies in these asset classes. They collect 3,228 hedge fund manager photographs via Google image searches, choosing the best for each manager based on resolution, degree of forward facing and neutrality of expression. They use these photographs to measure fWHR as the distance between the two zygions (width) relative to the distance between the upper lip and the midpoint of the inner ends of the eyebrows (height). Using these fWHRs, monthly net-of-fee returns and assets under management of 3,868 associated live and dead hedge funds, and monthly risk factor values during January 1994 through December 2015, they find that:

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Are financial advisors expert guides for their client investors? In their December 2017 paper entitled “The Misguided Beliefs of Financial Advisors”, Juhani Linnainmaa, Brian Melzer and Alessandro Previtero compare investing practices/results of Canadian financial advisors to those of their clients, including trading patterns, fees and returns. They estimate account alphas via multi-factor models. Using detailed data from two large Canadian mutual fund dealers (accounting for about 5% of their sector) for 3,276 Canadian financial advisors and their 488,263 clients, and returns and fees for 3,023 associated mutual funds, during January 1999 through December 2013, they find that:

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Are there rational ways to decide whether cryptocurrencies such as Bitcoin are in bubbles? In their December 2017 paper entitled “Datestamping the Bitcoin and Ethereum Bubbles”, Shaen Corbet, Brian Lucey and Larisa Yarovaya test for bubbles in Bitcoin and Ethereum price series. For valuation, they consider three potential cyrptocurrency price drivers:

1. Blockchain length, reflecting difficulty of finding a new block and receiving payment relative to past difficulty. As more miners engage, the rate of block creation increases, raising the level of difficulty.
2. Hash rate, indicating speed of blockchain code execution during mining. A higher hash rate increases probability of finding the next block and receiving payment. 
3. Liquidity, measuring the relationship between cryptocurrency daily returns and volatilities. 

They then apply ratios constructed from these variables to detect times when price series are substantially disconnected from fundamental drivers. Using Bitcoin data since July 18, 2010 and Ethereum data since July 30, 2015, both through November 9, 2017, they find that: Keep Reading

Should investors view stock returns around recurring firm events in aggregate as an exploitable anomaly? In their October 2017 paper entitled “Recurring Firm Events and Predictable Returns: The Within-Firm Time-Series”, Samuel Hartzmark and David Solomon review the body of research on relationships between recurring firm events and future stock returns. They classify events as predictable (1) releases of information or (2) corporate distributions, with some overlap. Information releases include earnings announcements, dividend announcements, earnings seasonality and predictable increases in dividends. Corporate distributions cover dividend ex-days, stock splits and stock dividends. They specify a general trading strategy to exploit these events that is long (short) stocks of applicable firms during months with (without) predictable events. They use market capitalization weighting but, since there are often more stocks in the short side, they scale short side weights downward so that overall long and short sides are equal in dollar value. Based on the body of research and updated analyses based on firm event data and associated stock prices from initial availabilities through December 2016, they conclude that:

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Does financial market volatility identify bubbles and predict subsequent crashes? In their April 2017 paper entitled “Can We Use Volatility to Diagnose Financial Bubbles? Lessons from 40 Historical Bubbles”, Didier Sornette, Peter Cauwels and Georgi Smilyanov examine price volatility before, during and after 40 financial market bubbles to determine whether realized and/or implied volatility warn of bubble conditions and subsequent crashes. They focus on the following questions:

1. Does volatility tend to increase during bubble maturation?
2. Does volatility surge towards the end of a bubble?
3. Can investors use volatility to diagnose bubbles and forecast their collapse?

They also evaluate credit conditions before and after bubbles to estimate whether leverage generally drives them. Their approach is event-oriented and graphical, centered on index level peaks preceding crashes. Using prices, realized volatilities, implied volatilities and ratios of credit from banks to the private non-financial sector to country GDP as available before, during and after 40 bubbles involving stock indexes, singles stocks, exchange-traded funds, commodity futures and currencies from the October 1929 Dow Jones Industrial Average crash through the September 2011 gold crash, they find that: Keep Reading