Examining the Sources of Excess Return Predictability: Stochastic Volatility or Market Inefficiency?
Kevin Lansing (),
Stephen LeRoy and
Jun Ma ()
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Jun Ma: Northeastern University, Postal: Department of Economics, Northeastern University, Boston, MA 02115, https://cssh.northeastern.edu/people/faculty/jun-ma/
No 2018-14, Working Paper Series from Federal Reserve Bank of San Francisco
We use a consumption based asset pricing model to show that the predictability of excess returns on risky assets can arise from only two sources: (1) stochastic volatility of model variables, or (2) departures from rational expectations that give rise to predictable investor forecast errors and market inefficiency. From an empirical perspective, we investigate whether 1-month ahead excess returns on stocks can be predicted using measures of consumer sentiment and excess return momentum, while controlling directly and indirectly for the presence of stochastic volatility. A variable that interacts the 12-month sentiment change with recent return momentum is a robust predictor of excess stock returns both in-sample and out-of-sample. The predictive power of this variable derives mainly from periods when sentiment has been declining and return momentum is negative, forecasting a further decline in the excess stock return. We show that the sentiment-momentum variable is positively correlated with fluctuations in Google searches for the term “stock market,” suggesting that the sentiment-momentum variable helps to predict excess returns because it captures shifts in investor attention, particularly during stock market declines.
JEL-codes: E44 G12 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-for, nep-mac and nep-ore
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