Integrating Different Informations for Portfolio Selection

ArXiv ID: 2305.17881 “View on arXiv”

Authors: Unknown

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.

Keywords: Bayesian Learning, Gaussian Mixture Model, Market Efficiency, Forecasting, Backward-looking/Forward-looking Data

Complexity vs Empirical Score

• Math Complexity: 8.5/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper employs advanced Bayesian statistics via Gaussian mixture models and solves inverse optimization problems with complex derivations, while it includes simulation experiments and out-of-sample testing across 35 global markets, making it both mathematically dense and empirically substantial.

  flowchart TD
    A["Research Goal:<br>Improve Portfolio Selection by<br>Integrating Backward & Forward-Looking Info"] --> B["Data Inputs:<br>Historical Asset Returns<br>Market Portfolio Weights"]
    B --> C["Methodology:<br>Bayesian Learning via<br>Gaussian Mixture Model"]
    C --> D["Computational Process:<br>Adaptive Harmonization<br>Based on Market Efficiency"]
    D --> E["Outcome:<br>Dynamic Combined Estimation<br>that Reacts Quickly at Turning Points"]
    E --> F["Validation:<br>Simulations & Global<br>Empirical Test"]
    F --> G["Final Finding:<br>Flexible & Robust<br>Forecasting Tool"]