Selección de cartera: un enfoque de sesgos de comportamiento

Abstract

Portfolio Selection: A Behavior Biased Approach

Here the goal is to provide optimal financial portfolios based on a normal likelihood, an a priori distribution on the parameters of the asset valuation model, and the investor's opinion on how to weigh likelihood and a priori in portfolio construction. The biased Bayesian inference methodology is applied to the selection of mean-variance portfolios, with different bias or weighting configurations. The results show an effective proposal to find optimal portfolios that reflect weightings made on likelihood and a priori beliefs. In addition, including biases in portfolio selection can be relevant to portfolio optimization. The proposal contributes to the field of behavioral finance biases and can be easily applied to other financial models that have been treated from a Bayesian approach. In conclusion, the proposal provides optimal weights for the portfolio that reflect both data and beliefs, and the inclusion of biases in portfolio optimization can help build optimal portfolios that incorporate risk preferences and investment objectives.