On Unified Adaptive Portfolio Management

ArXiv ID: 2307.03391 “View on arXiv”

Authors: Unknown

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.

Keywords: Black-Litterman, Elastic Net, Factor Models, Adaptive Portfolio Management, S&P 500

Complexity vs Empirical Score

  • Math Complexity: 7.5/10
  • Empirical Rigor: 7.0/10
  • Quadrant: Holy Grail
  • Why: The paper involves advanced mathematical formulations including dynamic Black-Litterman extensions with Elastic Net regularization, factor models, and convex optimization, indicating high mathematical complexity. It also demonstrates empirical rigor through a ten-year backtest on S&P 500 assets with transaction costs, providing performance metrics and a structured algorithmic approach.
  flowchart TD
    A["Research Goal:<br>Unified Adaptive Portfolio<br>Management Framework"] --> B["Data Source:<br>S&P 500 Top 100 Assets<br>10-Year Empirical Study"]
    B --> C["Methodology:<br>Dynamic Sliding Window<br>Adjusts for Market Volatility"]
    C --> D["Core Modeling Steps:<br>1. Factor Model<br>2. Elastic Net Regression<br>3. Time-Varying Black-Litterman"]
    D --> E["Optimization:<br>Mean-Variance Optimization<br>with Transaction Costs"]
    E --> F["Key Outcomes:<br>Computational Efficiency<br>Robust Performance<br>Error Mitigation"]