Problem
Research questions and friction points this paper is trying to address.
portfolio optimization
ESG
long-short strategy
sustainability
Omega ratio
A Two-Stage Decision Support System for Sustainability-Aware Long Short Portfolio Optimization
🤖 AI Summary
This study addresses the challenge of constructing long–short portfolios that simultaneously achieve sustainability and superior risk-adjusted returns by integrating environmental, social, and governance (ESG) factors in a market-regime-adaptive manner. The authors propose a two-stage decision framework: first, assets are selected for long and short positions using a hybrid approach combining TODIMSort multi-criteria classification with MEREC-based weighting; second, a non-convex optimization model maximizing the Omega ratio is formulated and solved via a novel particle swarm optimization algorithm featuring operator self-adaptation and constraint-projection repair mechanisms. Empirical analysis on 421 constituents of the STOXX Europe 600 index demonstrates that the proposed ESG-enhanced strategy significantly outperforms both conventional non-ESG approaches and the market-capitalization-weighted benchmark, delivering higher risk-adjusted returns while advancing sustainable investment objectives.
📝 Abstract
This paper proposes a two-stage decision support system for long-short portfolio optimization under environmental, social, and governance (ESG) considerations. In the first stage, assets are evaluated using a multi-criteria procedure based on TODIMSort, with criterion weights derived using the MEREC (Removal Effects of Criteria) method. This allows assets to be assigned to classes ordered according to preferences that respond to market conditions and investor priorities, thus generating sets of long and short opportunities that dynamically adapt to the prevailing regime. In the second stage, we formulate a non-convex portfolio optimization problem that maximizes the Omega ratio while respecting budget, bound and leverage constraints. To solve it, we introduce an adaptive particle swarm solver equipped with a controller that selects, at each iteration, the most suitable recombination operator from a diverse pool of operators and combines it with a projection-based repair mechanism for constraint management. The empirical study, conducted on 421 stocks in the STOXX Europe 600 index, examines both the exploration capabilities and solution quality of the proposed solver compared to state-of-the-art benchmarks, as well as the ex post profitability of the resulting portfolio strategies. The results show that ESG-enhanced long-short portfolios offer competitive and often superior performance compared to their non-ESG counterparts and the market-value-weighted benchmark.
Innovation
Methods, ideas, or system contributions that make the work stand out.
Two-stage decision support system
TODIMSort
MEREC
Adaptive particle swarm optimization
Omega ratio
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