quant research // summarizing the paperLOADING
Giacomo di Tollo, Massimiliano Kaucic, Filippo Piccotto · 2026-06-24
The paper builds a two-step system for picking long and short stock positions while accounting for ESG (environmental, social, governance) factors. First it ranks and sorts stocks using multi-criteria decision methods (TODIMSort with MEREC-derived weights); then it optimizes the portfolio to maximize the Omega ratio under budget, bound and leverage constraints using a custom adaptive particle-swarm algorithm. Tested on 421 European stocks (STOXX Europe 600), the ESG-aware long-short portfolios performed competitively with, and often better than, non-ESG versions and a market-cap-weighted benchmark.
Why it matters: It suggests that incorporating ESG scores into a systematic long-short selection process need not sacrifice returns and may sometimes help, at least in this European sample. Practitioners interested in sustainability-constrained strategies or in Omega-ratio-based optimization may find the framework a useful reference for combining screening and optimization.
⚠ Results are an ex-post backtest on one index and period using a complex custom optimizer, so real-world performance after costs and out-of-sample is unproven.
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.
Go deeper: a full research-committee breakdown of this paper, its assumptions and failure modes, and how its method would apply to a specific ticker or your watchlist. See StockTools AI →
AI summary generated from the paper’s public abstract via arXiv; it may miss nuance — read the source before relying on it. Thank you to arXiv for its open-access interoperability; StockTools is not affiliated with arXiv, and all rights remain with the authors. Educational only, not financial advice.