Integrating Different Informations for Portfolio Selection

TL;DR

This paper proposes a Bayesian Gaussian mixture model approach that combines historical data and market-implied information for improved portfolio selection, adapting to market efficiency and turning points.

Contribution

It introduces a novel adaptive method that integrates backward-looking and forward-looking data for portfolio optimization, enhancing robustness across different market conditions.

Findings

The approach outperforms traditional models in simulations.

It effectively captures market turning points.

It is applicable across various global markets.

Abstract

Following the idea of Bayesian learning via Gaussian mixture model, we organically combine the backward-looking information contained in the historical data and the forward-looking information implied by the market portfolio, which is affected by heterogeneous expectations and noisy trading behavior. The proposed combined estimation adaptively harmonizes these two types of information based on the degree of market efficiency and responds quickly at turning points of the market. Both simulation experiments and a global empirical test confirm that the approach is a flexible and robust forecasting tool and is applicable to various capital markets with different degrees of efficiency.