Machine Learning for Socially Responsible Portfolio Optimisation
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.
Code (0)
등록된 구현이 없습니다.
Similar Papers 제목 키워드 기반
Passive ESG Portfolio Management—The Benchmark Strategy for Socially Responsible Investors
In this article, we investigate the notion of doing well while doing good from the perspective of passive portfolio strategies. We analyze a number of asset allocation strategies based on ESG- weighting and compare the…
ManagementESG-Valued Portfolio Optimization and Dynamic Asset Pricing
ESG ratings provide a quantitative measure for socially responsible investment. We present a unified framework for incorporating numeric ESG ratings into dynamic pricing theory. Specifically, we introduce an ESG-valued r…
Portfolio OptimizationQuantum computing approach to realistic ESG-friendly stock portfolios
Finding an optimal balance between risk and returns in investment portfolios is a central challenge in quantitative finance, often addressed through Markowitz portfolio theory (MPT). While traditional portfolio optimizat…
Portfolio OptimizationOptimising Credit Portfolio Using a Quadratic Nonlinear Projection Method
A novel optimisation framework through quadratic nonlinear projection is introduced for credit portfolio when the portfolio risk is measured by Conditional Value-at-Risk (CVaR). The whole optimisation procedure to search…
Robust Portfolio Optimisation with Specified Competitors
We extend Relative Robust Portfolio Optimisation models to allow portfolios to optimise their distance to a set of benchmarks. Portfolio managers are also given the option of computing regret in a way which is more in li…