Market-Adaptive Ratio for Portfolio Management

ArXiv ID: 2312.13719 “View on arXiv”

Authors: Unknown

Abstract

Traditional risk-adjusted returns, such as the Treynor, Sharpe, Sortino, and Information ratios, have been pivotal in portfolio asset allocation, focusing on minimizing risk while maximizing profit. Nevertheless, these metrics often fail to account for the distinct characteristics of bull and bear markets, leading to sub-optimal investment decisions. This paper introduces a novel approach called the Market-adaptive Ratio, which was designed to adjust risk preferences dynamically in response to market conditions. By integrating the $ρ$ parameter, which differentiates between bull and bear markets, this new ratio enables a more adaptive portfolio management strategy. The $ρ$ parameter is derived from historical data and implemented within a reinforcement learning framework, allowing the method to learn and optimize portfolio allocations based on prevailing market trends. Empirical analysis showed that the Market-adaptive Ratio outperformed the Sharpe Ratio by providing more robust risk-adjusted returns tailored to the specific market environment. This advance enhances portfolio performance by aligning investment strategies with the inherent dynamics of bull and bear markets, optimizing risk and return outcomes.

Keywords: Portfolio optimization, Reinforcement Learning, Risk-adjusted return, Market regime detection, Bull/Bear markets, Equity

Complexity vs Empirical Score

• Math Complexity: 6.0/10
• Empirical Rigor: 7.0/10
• Quadrant: Holy Grail
• Why: The paper introduces a novel mathematical ratio (Market-adaptive Ratio) involving a logistic transformation and dynamic exponents, requiring advanced calculus. It includes a full reinforcement learning framework, backtesting against Sharpe Ratio, and discussion of empirical results, demonstrating high data/implementation readiness.

  flowchart TD
    A["Research Goal:<br>Improve Risk-Adjusted Returns<br>in Varying Market Conditions"] --> B["Data Input:<br>Historical Equity Market Data<br>Bull & Bear Regime Data"]
    B --> C["Key Methodology:<br>Reinforcement Learning Framework<br>with Market-adaptive Ratio"]
    C --> D{"Computational Process"}
    D --> E["Detect Market Regime<br>Derive ρ parameter"]
    E --> F["Dynamic Risk Adjustment<br>Optimize Portfolio Allocation"]
    F --> G["Training Loop:<br>Maximize Risk-Adjusted Returns<br>vs. Sharpe Ratio"]
    G --> H["Key Finding:<br>Market-adaptive Ratio Outperforms<br>Sharpe Ratio"]