Winners vs. Losers: Momentum-based Strategies with Intertemporal Choice for ESG Portfolios

2025-05-30

AI Summary

This paper addresses two critical limitations in ESG investing: sentiment-driven pricing distortions and inadequate capture of downside risk. To this end, we propose a state-dependent momentum investment framework. Methodologically, the framework integrates ESG sentiment cycle identification, tail-risk-aware performance measures—specifically the Stable Tail-Adjusted Return Ratio (STARR) and the Rachev Ratio—and dynamically optimizes a multi-asset long-short portfolio via a finite-horizon Bellman equation. Our key contributions include: (i) empirical evidence that ESG “loser” portfolios significantly outperform “winner” portfolios during pro-ESG market regimes, revealing short-term inefficiencies induced by sentiment overheating; and (ii) the first institutional-aware, tail-robust ESG momentum paradigm. Extensive out-of-sample tests across the Russell 3000, Dow Jones Industrial Average, and major cryptocurrencies—under a two-week formation and two-week holding period—demonstrate substantially improved annualized alpha and superior tail-risk control relative to conventional momentum strategies.

Technology Category

• Intelligent Robots: State Estimation
• Multiagent Systems: Mechanism Design
• Reasoning under Uncertainty: Stochastic Optimization

Application Category

• Search and Retrieval-Augmented AI: Web evaluation methodologies and metrics
• Graph Algorithms and Modeling for the Web: Efficient manipulation of static and dynamic Web-related graphs
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper introduces a state-dependent momentum framework that integrates ESG regime switching with tail-risk-aware reward-risk metrics. Using a dynamic programming approach and solving a finite-horizon Bellman equation, we construct long-short momentum portfolios that adjust to changing ESG sentiment regimes. Unlike traditional momentum strategies based on historical returns, our approach incorporates the Stable Tail Adjusted Return ratio and Rachev ratio to better capture downside risk in turbulent markets. We apply this framework across three asset classes, Russell 3000 equities, Dow Jones~30 stocks, and cryptocurrencies, under both pro- and anti-ESG market regimes. We find that ESG-loser portfolios significantly outperform ESG-winner portfolios in pro-ESG regimes, a counterintuitive result suggesting that market overreaction to ESG sentiment creates short-term pricing inefficiencies. This pattern is robust across tail-sensitive performance metrics and is most pronounced under a two-week formation and holding period. Our framework highlights how ESG considerations and sentiment regimes alter return dynamics, offering practical guidance for investors seeking to implement responsive momentum strategies under sustainability constraints. These findings challenge conventional assumptions about ESG investing and underscore the importance of dynamic, regime-aware portfolio construction in environments shaped by regulatory signals, investor flows, and behavioral biases.

Problem

• Develops ESG momentum portfolios using regime switching and tail-risk metrics
• Compares performance of ESG-loser vs. winner portfolios across asset classes
• Challenges conventional ESG investing assumptions with dynamic programming approach

Innovation

• State-dependent momentum with ESG regime switching
• Dynamic programming for finite-horizon Bellman equation
• Tail-risk-aware metrics like STAR and Rachev ratios