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Estimating real‐world probabilities: A forward‐looking behavioral framework
Author(s) -
Crisóstomo Ricardo
Publication year - 2021
Publication title -
journal of futures markets
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.88
H-Index - 55
eISSN - 1096-9934
pISSN - 0270-7314
DOI - 10.1002/fut.22248
Subject(s) - preference , consistency (knowledge bases) , econometrics , probabilistic logic , computer science , risk aversion (psychology) , asset (computer security) , behavioral economics , economics , artificial intelligence , expected utility hypothesis , financial economics , microeconomics , computer security
We show that disentangling sentiment‐induced biases from fundamental expectations significantly improves the accuracy and consistency of probabilistic forecasts. Using data from 1994 to 2017, we analyze 15 stochastic models and risk‐preference combinations and in all possible cases a simple behavioral transformation delivers substantial forecast gains. Our results are robust across different evaluation methods, risk‐preference hypotheses, and sentiment calibrations, demonstrating that behavioral effects can be effectively used to forecast asset prices. We also implement a trading strategy that shows how behavioral biases can be exploited to generate trading profits. Further analyses confirm that our real‐world densities outperform forecasts recalibrated to avoid past mistakes and improve predictive models where risk aversion is dynamically estimated from option prices.