A Dynamic Model of Characteristic-Based Return Predictability

Abstract

We present a model where investors have incomplete information about the “climate of disruptive innovation,” which influences the arrival rate of new projects and the exit rate of existing businesses. Information about the disruption climate is a source of systematic risk that generates abnormal returns to value, profitability, and asset growth strategies along some sample paths. While the null hypothesis of zero return predictability is rejected at the appropriate rate if investors are rational Bayesians, it is rejected too often when investors are subject to information processing biases such as overconfidence. In the calibrated model, the magnitudes of the historically observed Sharpe ratios of the above-mentioned investment strategies can be generated in more than 10% of the sample paths when investors exhibit strong overconfidence.