Fluctuating Confidence and Stock-Market Returns

Abstract

The average relative profitability of different firms in the economy changes erratically due to many factors. Investors are either unable to observe these productivity switches or are unable to determine which firms' returns were affected by observed events. They continuously update their beliefs regarding the high and low productivity firms by observing the total return on each firm which consists of the average productivity plus some noise. The learning behavior of investors is characterized by a belief process which exhibits fluctuating conditional variance. This is different from the learning behavior of the often used Kalman Filter in which the conditional variance is deterministic. I characterize the portfolio choices, interest-rate and total-returns processes in a Cox-Ingersoll-Ross[1985] style model where the productivities of assets are unobserved, but inferred as above. The portfolio choices are adjusted to hedge the risks introduced by `fluctuating confidence'. The model is capable of reproducing three stylized facts of stock-market returns and interest-rates. These are the skewness and kurtosis of returns and the `Predictive-Asymmetry' of returns: excess-returns and future changes in volatility are negatively correlated. Further negative returns cause reactions of larger magnitude. The success of the model in generating these features depends on the speed of learning about the productivity switches. Parameter values which lead to faster learning, are consistent with large negative skewness of returns and the Predictive Asymmetry property. The slower learning version leads to greater kurtosis of returns. I show that a model based on the same fundamentals but with observed productivity-switches is not reconcilable with these features. My analysis suggests that learning about the productivities of assets of the kind introduced here may be an important determinant of portfolio choices and observed asset returns.

JEL Classification: G21