Animal Spirits

Does variation in the number of reported cases of COVID-19 predict near-term asset returns? To investigate, we look for a test acknowledging that the available sample is short and very noisy. Specifically:

• To suppress noise, we use the 7-day moving average of U.S. COVID-19 cases.
• To avoid measurement overlap, we calculate weekly changes in this average and compare these changes to next-week returns for SPDR S&P 500 Trust (SPY) and iShares Barclays 20+ Year Treasury Bond (TLT).
• To assess reliability of any relationship, we look at rolling 13-week correlations between weekly changes in COVID-19 data and next-week asset returns. While 13 weeks is a short measurement interval for noisy data, consistency in outputs would offer some confidence that there is a reliable relationship.

Using weekly (Friday) COVID-19 case data from the Centers for Disease Control (CDC) and weekly (Friday close) dividend-adjusted SPY and TLT levels during late January 2020 (limited by COVID-19 data) through mid-September 2021, we find that: Keep Reading

Is intensity of public interest in a “bear market” useful for predicting stock market return? To investigate, we download monthly U.S. Google Trends search intensity data for “bear market” and relate this series to monthly S&P 500 Index returns. For comparison with the “investor fear gauge,” we also relate search data to monthly CBOE option-implied S&P 500 Index volatility (VIX) levels. Google Trends analyzes a percentage of Google web searches to estimate the number of searches done over a certain period. “Each data point is divided by the total searches of the geography and time range it represents to compare relative popularity… The resulting numbers are then scaled on a range of 0 to 100 based on a topic’s proportion to all searches on all topics.” Using the specified data as of 9/14/2021 for the period January 2004 (earliest available on Google Trends) through August 2021, we find that: Keep Reading

Do motives of financial market researchers justify strong skepticism of their findings? In his brief August 2021 paper entitled “Be Skeptical of Asset Management Research”, Campbell Harvey argues that economic incentives undermine belief in findings of both academic and practitioner financial market researchers. Based on his 35 years as an academic, advisor to asset management companies and editor of a top finance journal, he concludes that: Keep Reading

How frequently and permanently do individual U.S. investors sell stocks in a panic? In their August 2021 paper entitled “When Do Investors Freak Out?: Machine Learning Predictions of Panic Selling”, Daniel Elkind, Kathryn Kaminski, Andrew Lo, Kien Wei Siah and Chi Heem Wong examine frequency, timing and duration of panic selling. They define panic selling as a drop of at least 90% in account equity value within a month, of which at least 50% is due to trading. They also estimate the opportunity of cost of panic selling. Finally, they apply deep neural network software to predict a month in advance which individuals will panic sell based on recent market conditions and investor demographics/financial history. Using account equity value and trade data for 653,455 individual U.S. brokerage accounts belonging to 298,556 households during January 2003 through December 2015, they find that:

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Does technology amplify adverse herding among inexperienced investors? In their October 2020 paper entitled “Attention Induced Trading and Returns: Evidence from Robinhood Users”, Brad Barber, Xing Huang, Terrance Odean and Christopher Schwarz test the relationship between episodes of intense stock buying by retail (Robinhood) investors and future returns. Their source for buying intensity is the stock popularity feature of Robintrack from May 2, 2018 until discontinuation August 13, 2020 (with 11 dates missing and two hours missing for 16 other dates), during which the number of Robinhood user-stock positions grows from about 5 million to over 42 million. They define intense stock buying (herding event) as a dramatic daily increase in number of Robinhood users owning a particular stock in two ways:

1. Among stocks with at least 100 owners at the start of the day, select those in the top 0.5% of ratio of owners at the end of the day to owners at the beginning of the day.
2. Select stocks with at least 1,000 new owners and at least a 50% increase in owners during the day.

Using Robintrack data supporting these definitions and associated daily stock returns, open and close prices, closing bid-ask spreads and market capitalizations, they find that: Keep Reading

Are typical investors persistently irrational in pursuit of wealth, or pursuing more than wealth? In his December 2019 book entitled Behavioral Finance: The Second Generation, Meir Statman describes and discusses second-generation behavioral finance, which replaces (1) pursuit of wealth persistently retarded by cognitive shortcuts and emotional biases with (2) pursuit of normal wants including financial security, success for children and families, adherence to values, high social status, inclusion, respect and fairness (with some shortcuts and errors). These normal wants, even more than cognitive shortcuts and emotional biases, explain saving and spending, portfolio construction, asset pricing and market efficiency. Based on the body of research and his long experience in behavioral finance, he concludes that:

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Is investor attraction to high-reward/high-risk (lottery) stocks a crucial contributor to stock return anomalies? In their May 2020 paper entitled “Lottery Preference and Anomalies”, Lei Jiang, Quan Wen, Guofu Zhou and Yifeng Zhu aggregate 16 measures of lottery preference into a single long-short factor via time-varying linear combination. Examples of the 16 measures are: maximum daily return last month; average of the five highest daily returns last month; difference between maximum and minimum daily returns last month; and, skewness of daily returns the past three months. They then test the ability of this lottery preference factor to help explain a set of 19 stock return anomalies previously unexplained by a widely used 4-factor (market, size, investment and profitability) model of stock returns. They further study interactions between the lottery preference factor and 11 well-known anomalies by each month during 1980-2018 double-sorting stocks first into fifths (quintiles) based on lottery preference and then within each lottery preference quintile into sub-quintiles based on each anomaly characteristic. Using firm/stock data for a broad sample of U.S. common stocks priced over $1 during January 1962 through December 2018, they find that:

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What does the U.S. stock market at industry/firm levels say about investor expectations during and after the 2019 coronavirus (COVID-19) pandemic? In the April 2020 update of their paper entitled “Feverish Stock Price Reactions to COVID-19”, Stefano Ramelli and Alexander Wagner examine and interpret industry/firm-level reactions to COVID-19 across three pandemic phases:

1. Incubation: January 2-17,
2. Outbreak: January 20-February 21,
3. Fever: February 24-March 20.

They estimate each stock’s abnormal return during these phases as its 1-factor (market) alpha minus its beta times the market excess return. They estimate alpha and beta via regression of daily excess stock returns on daily excess value-weighted market returns during 2019. They use the yield on 1-month U.S. Treasury bills (T-bill) as the risk-free rate for calculating excess return. Using daily dividend-adjusted stock prices for Russell 3000 stocks (excluding financial stocks for leverage-related analyses), market returns and T-bill yields during December 31, 2018 through March 20, 2020, they find that: Keep Reading

Are firms that engage the public via Twitter more expanding (via exposure) or shrinking (via adverse social media frenzy) their opportunity sets? In their January 2020 paper entitled “The Social Media Risk Premium”, Amin Hosseini, Gergana Jostova, Alexander Philipov and Robert Savickas investigate relationships between firm Twitter activity and stock return. Their data include firm Twitter presence, and level and nature of activity, as well as responses from followers. Using these Twitter data, accounting data and stock returns for all publicly held U.S. firms during 2007 through 2016 (33,445,318 tweets, generating 25,603,977 replies, 161,548,941 retweets and 265,738,508 likes), they find that: Keep Reading

Do investors systematically and exploitably underreact to deviations in firm fundamentals from recent averages? In their January 2020 paper entitled “Anchoring on Past Fundamentals”, Doron Avramov, Guy Kaplanski and Avanidhar Subrahmanyam investigate how deviations of quarterly firm accounting variables from averages over recent quarters relate to future returns across stocks. They first construct a stock performance deviation index (PDI) based on seven variables: (1) cash and short-term investments, (2) retained earnings, (3) operating income, (4) sales, (5) capital expenditures, (6) invested capital and (7) inventories. They then each month for each stock starting June 1977:

• Calculate the deviation for each variable as the difference between its most recent quarterly value and its average over the preceding three quarters, scaled by total assets.
• Rank each deviation (in percentiles) relative to deviations for the same variable for all stocks.
• Calculate PDI for a stock as the equally weighted average of percentile rankings across all seven variables.

They extend this approach to a more comprehensive fundamental-based deviation index (FDI) that considers deviations of all Compustat accounting variables plus 14 commonly used accounting ratios, with weights of deviation percentile rankings optimized via least absolute shrinkage and selection operator (LASSO) regression starting January 1979. For all variables, if the exact release date is unavailable, they assume a 60-day delay in release. For portfolio tests, they calculate returns to hedge portfolios that are long (short) stocks in the top (bottom) tenth, or decile, of PDIs or FDIs, with holding intervals ranging from one to 24 months. Using monthly data needed to construct PDI, FDI and 30 style, technical, fundamental and liquidity control variables across a broad sample of reasonably liquid U.S. common stocks with positive book values and prices over $5 during January 1976 through October 2017, they find that: Keep Reading