Machine Learning for Socially Responsible Portfolio Optimisation

• URL: http://arxiv.org/abs/2305.12364v1
• Date: Sun, 21 May 2023 06:28:53 GMT
• Title: Machine Learning for Socially Responsible Portfolio Optimisation
• Authors: Taeisha Nundlall, Terence L Van Zyl
• Abstract summary: Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. This study implements portfolio optimisation for socially responsible investors.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Socially responsible investors build investment portfolios intending to incite social and environmental advancement alongside a financial return. Although Mean-Variance (MV) models successfully generate the highest possible return based on an investor's risk tolerance, MV models do not make provisions for additional constraints relevant to socially responsible (SR) investors. In response to this problem, the MV model must consider Environmental, Social, and Governance (ESG) scores in optimisation. Based on the prominent MV model, this study implements portfolio optimisation for socially responsible investors. The amended MV model allows SR investors to enter markets with competitive SR portfolios despite facing a trade-off between their investment Sharpe Ratio and the average ESG score of the portfolio.

Related papers

• InvestESG: A multi-agent reinforcement learning benchmark for studying climate investment as a social dilemma [8.867831781244575]
  InvestESG is a novel multi-agent reinforcement learning (MARL) benchmark designed to study the impact of ESG disclosure mandates on corporate climate investments. Our experiments show that without ESG-conscious investors with sufficient capital, corporate mitigation efforts remain limited under the disclosure mandate. Providing more information about global climate risks encourages companies to invest more in mitigation, even without investor involvement.
  arXiv  Detail & Related papers  (2024-11-15T00:31:45Z)
• Explainable Post hoc Portfolio Management Financial Policy of a Deep Reinforcement Learning agent [44.99833362998488]
  We develop a novel Explainable Deep Reinforcement Learning (XDRL) approach for portfolio management. By executing our methodology, we can interpret in prediction time the actions of the agent to assess whether they follow the requisites of an investment policy.
  arXiv  Detail & Related papers  (2024-07-19T17:40:39Z)
• When AI Meets Finance (StockAgent): Large Language Model-based Stock Trading in Simulated Real-world Environments [55.19252983108372]
  We have developed a multi-agent AI system called StockAgent, driven by LLMs. The StockAgent allows users to evaluate the impact of different external factors on investor trading. It avoids the test set leakage issue present in existing trading simulation systems based on AI Agents.
  arXiv  Detail & Related papers  (2024-07-15T06:49:30Z)
• Deep Reinforcement Learning and Mean-Variance Strategies for Responsible Portfolio Optimization [49.396692286192206]
  We study the use of deep reinforcement learning for responsible portfolio optimization by incorporating ESG states and objectives. Our results show that deep reinforcement learning policies can provide competitive performance against mean-variance approaches for responsible portfolio allocation.
  arXiv  Detail & Related papers  (2024-03-25T12:04:03Z)
• Enhancing Profitability and Investor Confidence through Interpretable AI Models for Investment Decisions [0.14999444543328289]
  This paper presents an interpretable decision-making model leveraging the SHAP-based explainability technique to forecast investment recommendations. The proposed solution not only provides valuable insights into the factors that influence forecasted recommendations but also caters the investors of varying types.
  arXiv  Detail & Related papers  (2023-12-24T15:05:53Z)
• HireVAE: An Online and Adaptive Factor Model Based on Hierarchical and Regime-Switch VAE [113.47287249524008]
  It is still an open question to build a factor model that can conduct stock prediction in an online and adaptive setting. We propose the first deep learning based online and adaptive factor model, HireVAE, at the core of which is a hierarchical latent space that embeds the relationship between the market situation and stock-wise latent factors. Across four commonly used real stock market benchmarks, the proposed HireVAE demonstrate superior performance in terms of active returns over previous methods.
  arXiv  Detail & Related papers  (2023-06-05T12:58:13Z)
• Bayesian Optimization of ESG Financial Investments [0.0]
  ESG (Economic, Social and Governance) criteria have become more significant in finance. This paper combines mathematical modelling, with ESG and finance.
  arXiv  Detail & Related papers  (2023-02-10T15:17:36Z)
• Model-based Multi-agent Policy Optimization with Adaptive Opponent-wise Rollouts [52.844741540236285]
  This paper investigates the model-based methods in multi-agent reinforcement learning (MARL) We propose a novel decentralized model-based MARL method, named Adaptive Opponent-wise Rollout Policy (AORPO)
  arXiv  Detail & Related papers  (2021-05-07T16:20:22Z)
• Learning Risk Preferences from Investment Portfolios Using Inverse Optimization [25.19470942583387]
  This paper presents a novel approach of measuring risk preference from existing portfolios using inverse optimization. We demonstrate our methods on real market data that consists of 20 years of asset pricing and 10 years of mutual fund portfolio holdings.
  arXiv  Detail & Related papers  (2020-10-04T21:29:29Z)
• Reinforcement-Learning based Portfolio Management with Augmented Asset Movement Prediction States [71.54651874063865]
  Portfolio management (PM) aims to achieve investment goals such as maximal profits or minimal risks. In this paper, we propose SARL, a novel State-Augmented RL framework for PM. Our framework aims to address two unique challenges in financial PM: (1) data Heterogeneous data -- the collected information for each asset is usually diverse, noisy and imbalanced (e.g., news articles); and (2) environment uncertainty -- the financial market is versatile and non-stationary.
  arXiv  Detail & Related papers  (2020-02-09T08:10:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.