Working Paper: NBER ID: w10013
Authors: Harrison Hong; Jeremy C. Stein
Abstract: We study the implications of learning in an environment where the true model of the world is a multivariate one, but where agents update only over the class of simple univariate models. If a particular simple model does a poor job of forecasting over a period of time, it is eventually discarded in favor of an alternative yet equally simple model that would have done better over the same period. This theory makes several distinctive predictions, which, for concreteness, we develop in a stock-market setting. For example, starting with symmetric and homoskedastic fundamentals, the theory yields forecastable variation in the size of the value/glamour differential, in volatility, and in the skewness of returns. Some of these features mirror familiar accounts of stock-price bubbles.
Keywords: learning; forecasting models; stock market; value-glamour differential
JEL Codes: D83; G12
Edges that are evidenced by causal inference methods are in orange, and the rest are in light blue.
| Cause | Effect |
|---|---|
| Model A (initially used) (Y20) | Forecast Errors (negative) (C53) |
| Forecast Errors (negative) (C53) | Model B (adopted) (Y20) |
| Model A (initially used) (Y20) | Model B (adopted) (Y20) |
| Increased likelihood of paradigm shifts (D80) | Elevated volatility in returns (G17) |
| Increased likelihood of paradigm shifts (D80) | Negative skewness in returns (C46) |