Cameron Peng, London School of Economics and Political Science
Abstract: We build a model of the law of small numbers (LSN)---the incorrect belief that even small samples represent the properties of the underlying population—to study its implications for trading behavior and asset prices. In the model, a belief in the LSN induces investors to expect short-term price trends to revert and long-term price trends to continue, hence generating short-term momentum, long-term reversals, and excess volatility in asset prices. The model offers a natural reconciliation between the disposition effect and return extrapolation. In addition, it makes several predictions about investor behavior, including a weakened disposition effect for long-term holdings, "doubling down" in buying, consistency between doubling down and the disposition effect, and heterogeneous trading propensities to past returns. By testing these predictions using account-level transaction data, we show that the LSN provides a parsimonious way for understanding a variety of puzzles about investor behavior.
Discussant: Francesca Bastianello, University of Chicago
Abstract: We conduct an eye-tracking study to explore how investors allocate their attention across a price chart while predicting future stock prices. We confirm that attention allocation reflects expectation formation based on historical prices, as measures based on eye-tracking predict the forecasts submitted by subjects. Subjects rely on their perceptions of past trends and price levels when making forecasts. Recent and extreme returns, as well as price peaks and troughs, receive greater weight. Such heuristics are heterogeneous across subjects and result in inferior forecast performance. Our results provide neural evidence on beliefs about historical prices hypothesized by behavioral expectation models.
