Investor heuristics measurement and return predictability - a behavioural finance study
Fábio Rafael Morais Zeferino
Abstract
Open-access reader
Fábio Rafael Morais Zeferino
Abstract
Open-access reader
This paper presents literature-based investor heuristic measurement variables to explain and predict excess returns in the U.S market. These variables are part of a behavioural model that aims to measure the anchoring, availability, confirmation, overconfidence, and representativeness heuristics. Empirical evidence, based on the NASDAQ100 index, suggests that the behavioural model is able to explain excess returns and that it can be incorporated in the Fama-French Three-Factor Model (hybrid model)to enhance the traditional models’ explanatory capabilities of regular stock returns. Lastly ,this paper presents several in-and out-of-sample forecasts that support the return predictability of the behavioural and hybrid models.
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This paper presents literature-based investor heuristic measurement variables to explain and predict excess returns in the U.S market. These variables are part of a behavioural model that aims to measure the anchoring, availability, confirmation, overconfidence, and representativeness heuristics. Empirical evidence, based on the NASDAQ100 index, suggests that the behavioural model is able to explain excess returns and that it can be incorporated in the Fama-French Three-Factor Model (hybrid model)to enhance the traditional models’ explanatory capabilities of regular stock returns. Lastly ,this paper presents several in-and out-of-sample forecasts that support the return predictability of the behavioural and hybrid models.
Key concepts: Predictability, Heuristics, Behavioral economics, Economics, Computer science, Econometrics, Finance, Financial economics