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Combination of strategies

Combination of strategies

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Institutional investors, aware of the need to incorporate climate change as an additional risk factor into portfolio management, show a growing appetite for integrating Sustainable and Responsible Investment (SRI) criteria into their investment processes. Within a passive management context, this paper analyses, from a practical point of view, the...

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The rapidly growing research landscape in finance, encompassing environmental, social, and governance (ESG) topics and associated Artificial Intelligence (AI) applications, presents challenges for both new researchers and seasoned practitioners. This study aims to systematically map the research area, identify knowledge gaps, and examine potential research areas for researchers and practitioners. The investigation focuses on three primary research questions: the main research themes concerning ESG and AI in finance, the evolution of research intensity and interest in these areas, and the application and evolution of AI techniques specifically in research studies within the ESG and AI in finance domain. Eight archetypical research domains were identified: (i) Trading and Investment, (ii) ESG Disclosure, Measurement and Governance, (iii) Firm Governance, (iv) Financial Markets and Instruments, (v) Risk Management, (vi) Forecasting and Valuation, (vii) Data, and (viii) Responsible Use of AI. Distinctive AI techniques were found to be employed across these archetypes. The study contributes to consolidating knowledge on the intersection of ESG, AI, and finance, offering an ontological inquiry and key takeaways for practitioners and researchers. Important insights include the popularity and crowding of the Trading and Investment domain, the growth potential of the Data archetype, and the high potential of Responsible Use of AI, despite its low publication count. By understanding the nuances of different research archetypes, researchers and practitioners can better navigate this complex landscape and contribute to a more sustainable and responsible financial sector.
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The use of quantitative methods constitutes a standard component of the institutional investors’ portfolio management toolkit. In the last decade, several empirical studies have employed probabilistic or classification models to predict stock market excess returns, model bond ratings and default probabilities, as well as to forecast yield curves. To the authors’ knowledge, little research exists into their application to active fixed-income management. This paper contributes to filling this gap by comparing a machine learning algorithm, the Lasso logit regression, with a passive (buy-and-hold) investment strategy in the construction of a duration management model for high-grade bond portfolios, specifically focusing on US treasury bonds. Additionally, a two-step procedure is proposed, together with a simple ensemble averaging aimed at minimising the potential overfitting of traditional machine learning algorithms. A method to select thresholds that translate probabilities into signals based on conditional probability distributions is also introduced. A large set of financial and economic variables is used as an input to obtain a signal for active duration management relative to a passive benchmark portfolio. As a first result, most of the variables selected by the model are related to financial flows and economic fundamentals, but the parameters seem to be unstable over time, thereby suggesting that the variable relevance may be time dependent. Backtesting of the model, which was carried out on a sovereign bond portfolio denominated in US dollars, resulted in a small but statistically significant outperformance of benchmark index in the out-of-sample dataset after controlling for overfitting. These results support the case for incorporating quantitative tools in the active portfolio management process for institutional investors, but paying special attention to potential overfitting and unstable parameters. Quantitative tools should be viewed as a complementary input to the qualitative and fundamental analysis, together with the portfolio manager’s expertise, in order to make better-informed investment decisions.