On Unified Adaptive Black-Litterman Mean-Variance Portfolio Management

Chi-Lin Li and Chung-Han Hsieh

Papers from arXiv.org

Abstract: This paper proposes a unified adaptive portfolio-management framework that combines factor-based view generation, Black-Litterman (BL) posterior estimation, EWMA covariance estimation, and mean-variance optimization. The key mechanism is a dynamic sliding window that adjusts the estimation horizon according to realized portfolio volatility, thereby updating factor estimates, BL posterior expected returns, and portfolio weights over time. In a ten-year empirical study of the top 100 market-capitalization constituents of the S&P 500 with turnover transaction costs, the proposed method outperforms dynamic mean-variance optimization without BL views and provides stronger downside risk control, while its relative performance remains benchmark-dependent.

Date: 2023-07, Revised 2026-05
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