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Mood Beta as Stock Return Predictor

Posted in Animal Spirits, Calendar Effects, Equity Premium

Do individual stocks react differently and persistently to aggregate investor mood changes? In their December 2016 paper entitled “Mood Beta and Seasonalities in Stock Returns”, David Hirshleifer, Danling Jiang and Yuting Meng investigate whether some stocks have higher sensitivities to investor mood changes (higher mood betas) than others, thereby inducing calendar effects in the cross-section of returns. They specify mood based on three calendar-based U.S. stock market return anomalies:

  1. January (highest average excess return of all months) represents good mood, while October (lowest average excess return of all months) represents bad mood.
  2. Friday (highest average excess return of all days) represents good mood, while Monday (lowest average excess return of all days) represents bad mood.
  3. The two days before holidays (abnormally high average excess return) represent good mood, while the two days after holidays (abnormally low average excess return) represent bad mood.

They structure their investigation via a factor model of stock returns, with mood as a factor. They measure a stock’s mood beta by regressing its returns during high and low mood intervals versus contemporaneous equal-weighted market returns over a rolling historical window. Each year, they regress a stock’s monthly January and October returns versus monthly equal-weighted market returns for those months over the last 10 years. Each week, they regress a stock’s daily Friday and Monday returns versus contemporaneous equal-weighted market returns for those days over the last ten weeks. Each holiday, they regress a stocks pre-holiday and post-holiday daily returns versus versus equal-weighted market returns for those days over the last year (including the same holiday the previous year. They then use the stock’s mood betas to predict its returns during subsequent times of good and bad mood. Using daily and monthly stock returns for a broad sample of U.S. common stocks during January 1963 through December 2015, they find that:

  • Relative performance of stocks during good mood times (January, Friday, pre-holiday) tends to persist during future good mood times and reverse during future bad mood times (October, Monday, post-holiday).
  • Consequently, high (low) mood beta stocks tend to outperform during future Januarys, Fridays and pre-holiday days (Octobers and Mondays), and underperform during future Octobers and Mondays (Januarys, Fridays and pre-holiday days). However, there is little evidence that mood beta predicts post-holiday returns. Specifically:
    • A stock with January-October mood beta one standard deviation higher (lower) than another stock tends to outperform (underperform) the latter stock by 1.5% during January (October) each of the next ten years.
    • A stock with Friday-Monday mood beta one standard deviation higher (lower) than another stock tends to outperform (underperform) the latter stock by 0.5% on Friday (Monday) each of the next 10 weeks.
    • A stock with pre/post-holiday mood beta one standard deviation higher than another stock tends to outperform the latter stock by 0.3% higher during pre-holidays each of the next 13 holidays.

In summary, evidence indicates that sensitivity differences of individual U.S. stocks to investor mood persist. A stock that exhibits abnormally high or low returns during recent good (bad) mood times tends to exhibit high or low (low or high) returns during future good mood times and low or high (high or low) returns during future bad mood times. 

Cautions regarding findings include:

  • The specification of mood as times of high or low returns seems circular.
  • The study is academic. To exploit findings, investors holding broad stock portfolios would have to (1) continually recalculate one or more mood betas and (2) rebalance portfolios based on these betas at the beginning and end of each interval of good or bad expected mood. Quantification of exploitability is difficult.
  • Reported performance metrics are gross, not net. Accounting for annual, weekly and/or holiday portfolio rebalancing frictions would reduce these values. Extreme mood beta stocks may be relatively illiquid, such that concentrating them in a portfolio with continual rebalancing would be costly. Extreme mood beta stocks may also be costly/difficult to short.
  • Cross-sectional stock return factors based on U.S. data arguably involve aggregate/cumulative snooping bias as addressed in “Taming the Factor Zoo”, thereby overstating statistical significance. There may be specific snooping bias in the selected mood beta measurement lookback intervals.
  • Results are for U.S. stocks only.
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