On Unified Adaptive Portfolio Management

TL;DR

This paper presents a unified adaptive portfolio management framework that combines dynamic Black-Litterman, factor models, Elastic Net, and mean-variance optimization, using a novel sliding window algorithm for improved market responsiveness and performance.

Contribution

It introduces a dynamic sliding window algorithm for adaptive portfolio management that integrates multiple models to enhance robustness and responsiveness to market changes.

Findings

Improved portfolio performance over ten years in S&P 500 assets.

Reduced estimation errors through integrated modeling approach.

Enhanced computational efficiency and trading results.

Abstract

This paper introduces a unified framework for adaptive portfolio management, integrating dynamic Black-Litterman (BL) optimization with the general factor model, Elastic Net regression, and mean-variance portfolio optimization, which allows us to generate investors views and mitigate potential estimation errors systematically. Specifically, we propose an innovative dynamic sliding window algorithm to respond to the constantly changing market conditions. This algorithm allows for the flexible window size adjustment based on market volatility, generating robust estimates for factor modeling, time-varying BL estimations, and optimal portfolio weights. Through extensive ten-year empirical studies using the top 100 capitalized assets in the S&P 500 index, accounting for turnover transaction costs, we demonstrate that this combined approach leads to computational advantages and promising trading performances.