Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

Lanza, Ariel; Bernardini, Enrico; Faiella, Ivan

doi:10.2139/ssrn.3659584

Cited by 20 publications

(7 citation statements)

References 20 publications

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“…The climate data for structural data were obtained from the Climate Change Report (2014) [42]. This paper [39] offers a new method for addressing existing discrepancies in ESG ratings by using Machine Learning (ML) techniques to discover variables that contribute more to creating efficient portfolios. The research [40] examines a debate that focuses on policy implications for the three major ESG indicators that were shown to have the greatest impact on ROE and ROA: 1.…”

Section: Discussionmentioning

confidence: 99%

“…There has been a recent trend in investing in companies that support CSR activities and have a high SRI score. Therefore advanced machine learning [38] techniques, namely Genetic Algorithm [37], Neural Networks [39], [40], and Decision Trees [41] used to evaluate Responsible AI and the impact of SRI metrics in the financial sector shown briefly in Table I.…”

Section: Work Done On Socially Responsible Aimentioning

confidence: 99%

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A Brief Review of Responsible AI and Socially Responsible Investment in Financial and Stock Trading

Ullah¹,

Sultana²,

Faisal³

et al. 2021

Preprint

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Automated trading is used in most of the major markets of our world. In order to ensure sustainable development, incorporating ethical and socially responsible ideas while designing these Artificial Intelligence (AI) systems has become a necessity. Both the industry and the academia are working towards Responsible AI, which can make Socially Responsible Investments (SRI). This paper reviews the research on SRI investment in the financial sector and evaluates these methods, which can help find future research directions in Computational Finance. This survey looks at the machine learning techniques used for ethical decision-making while stock or forex trading, which will benefit any further research work on Responsible AI in Finance.<br>

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Section: Discussionmentioning

confidence: 99%

Section: Work Done On Socially Responsible Aimentioning

confidence: 99%

A Brief Review of Responsible AI and Socially Responsible Investment in Financial and Stock Trading

Ullah¹,

Sultana²,

Faisal³

et al. 2021

Preprint

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“…By means of machine learning, Lanza et al (2020) rebuild Fama-French fivefactor model, integrating the original factors with company scores based on socially responsible indicators (of which we will say more ahead). In the language of ML, the scores act as features used to classify portfolios.…”

Section: Multifactor Strategy and Related Literaturementioning

confidence: 99%

Mean-variance investing with factor tilting

Boido

Fasano

2023

Risk Manag

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Factor analysis proposes an alternative approach to standard portfolio theory: the latter is optimisation based, while the former is estimation based. Also, in standard portfolio theory, returns are only explained by the portfolio volatility factor, while factor analysis proposes a multiplicity of factors, which the managers can choose from to tilt their portfolios. In attempting to reconcile these alternative worlds, we propose a penalised utility function, incorporating both the Markowitzian risk-return trade-off and the manager’s preferences towards factors, and discriminating among losses and gains relative to a reference asset. The penalisation affects the optimisation process, favouring the selection of portfolios with less variance and more tilted towards the chosen risk factors. Penalty levels set by the manager generalise the traditional notion of risk aversion. We test our model by building an investment portfolio based on a combination of asset classes and selected investing factors, focussed on the eurozone. To identify the optimal portfolio, we adopt a set of three metaheuristic optimisation algorithms: the fitness function stochastic maximization using genetic algorithms, differential evolution algorithm for global optimisation, and the particle swarm optimisation, and dynamically choose the best solution. In this way, we can improve the Markowitzian optimisation by tilting the asset allocation with managers’ expectations and desired exposures towards designated factors.

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“…A report from Bloomberg Intelligence (2021) shows that forms of ESG investing have risen to almost USD 40 trillion in 2021. Moreover, central banks across EU are in the process of assimilating ESG evaluations into investment methods as a tool used to align with change to low-carbon economies (Bernardini et al, 2021;Bua et al, 2021;Lanza et al, 2020).…”

Section: Energy Efficiency Financing and Role Of Financial Institutionsmentioning

confidence: 99%

Energy Efficiency Renovation Financing Models for Homeowners and Net Present Value Analysis: Case Study from India

Basu

Syal

Paul

et al. 2022

INTREST

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In India, the lack of financing mechanisms has been identified as a major impediment in achieving energy efficiency (EE) and requires focused research to identify barriers and propose innovative financial models. This paper presents details of selected Government of India initiatives that highlight interlinking strategies of energy efficiency renovations, their financing models, and institutional policies needed for their implementation. These initiatives show that residential buildings have tremendous scope for retrofitting as they successfully attract finances through Energy Service Companies (ESCOs) and Renewable ESCO (RESCO) routes. This research highlights understanding of financial and technical barriers in energy efficiency retrofitting for existing residential buildings. Additionally, it discusses financial models and technical interventions being employed in energy efficiency residential retrofitting projects and demonstrates a retrofitting cost model for an actual case study project for composite climate using various physical and technological interventions. The work includes developing retrofitting scenarios through Government initiatives of technical interventions, then performing energy saving calculations, and finally developing cost model explaining the actual savings and payback periods for the potential intervention’s investments. These steps led to the development of a potential cost model which can assist both homeowners and energy professionals in identifying and implementing energy retrofitting measures in the residential building sector.

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Mind the gap! Machine Learning, ESG Metrics and Sustainable Investment

Cited by 20 publications

References 20 publications

A Brief Review of Responsible AI and Socially Responsible Investment in Financial and Stock Trading

A Brief Review of Responsible AI and Socially Responsible Investment in Financial and Stock Trading

Mean-variance investing with factor tilting

Energy Efficiency Renovation Financing Models for Homeowners and Net Present Value Analysis: Case Study from India

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