Case-Based Investing: Stock Selection Under Uncertainty
Case-based decision theory (Gilboa and Schmeidler, 1995) predicts that given a new problem, a decision maker will act based on the memory of actions and outcomes in past similar situations. The concept of similarity plays a central role in explaining behavior. Unlike expected utility theory, case-based decision theory (CBDT) does not require a decision maker to know alternative courses of action or all possible outcomes associated with each action. Fitting a CBDT model to data on stock transactions of retail investors in the Philippines, the author analyzed whether past personal trading experience on a stock is applied to an objectively similar stock. Results show a significant similarity effect consistent with the prediction of CBDT. Past personal trading gains spillover to stocks within the same industry sector, which may preclude portfolio diversification. Investors also apply past outcomes on a recommended stock to similar stocks. This underscores the strong influence of analyst recommendation on investor decisions.
Radoc, B. (2017). Case-based investing: Stock selection under uncertainty. Journal of Behavioral and Experimental Finance, 17, 53–59. https://doi.org/10.1016/j.jbef.2017.12.007