Paper: arXiv 2303.01485

Authors: Eduardo C. Garrido-Merchán, Gabriel González Piris, Maria Coronado Vaca

Abstract

Financial experts and analysts seek to predict the variability of financial markets. In particular, the correct prediction of this variability ensures investors successful investments. However, there has been a big trend in finance in the last years, which are the ESG criteria. Concretely, ESG (Economic, Social and Governance) criteria have become more significant in finance due to the growing importance of investments being socially responsible, and because of the financial impact companies suffer when not complying with them. Consequently, creating a stock portfolio should not only take into account its performance but compliance with ESG criteria. Hence, this paper combines mathematical modelling, with ESG and finance. In more detail, we use Bayesian optimization (BO), a sequential state-of-the-art design strategy to optimize black-boxes with unknown analytical and costly-to compute expressions, to maximize the performance of a stock portfolio under the presence of ESG criteria soft constraints incorporated to the objective function. In an illustrative experiment, we use the Sharpe ratio, that takes into consideration the portfolio returns and its variance, in other words, it balances the trade-off between maximizing returns and minimizing risks. In the present work, ESG criteria have been divided into fourteen independent categories used in a linear combination to estimate a firm total ESG score. Most importantly, our presented approach would scale to alternative black-box methods of estimating the performance and ESG compliance of the stock portfolio. In particular, this research has opened the door to many new research lines, as it has proved that a portfolio can be optimized using a BO that takes into consideration financial performance and the accomplishment of ESG criteria.

Complexity vs Empirical Score

  • Math Complexity: 5.5/10
  • Empirical Rigor: 4.5/10
  • Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper presents an innovative application of Bayesian Optimization to portfolio construction subject to multi-category ESG soft constraints. While the methodology is conceptually strong and well-explained, the empirical validation relies on simplified illustrative experiments rather than rigorous, long-term historical out-of-sample backtesting.

Research Flowchart

  flowchart TD
    A["Research Goal: Maximize Portfolio Sharpe Ratio with ESG Constraints"] --> B["Inputs: Stock Returns, Variance & 14 ESG Categories"]
    B --> C["Model Construction: Combine Performance Metric with ESG Soft Constraints"]
    C --> D["Bayesian Optimization: Black-Box Sequential Search Strategy"]
    D --> E["Key Outcome: Optimized Portfolio Balancing Financial Returns, Risk & ESG Compliance"]