Eeshaan Dutta, Sarthak Diwan, Siddhartha P. Chakrabarty
{"title":"ESG driven pairs algorithm for sustainable trading: Analysis from the Indian market","authors":"Eeshaan Dutta, Sarthak Diwan, Siddhartha P. Chakrabarty","doi":"arxiv-2401.14761","DOIUrl":null,"url":null,"abstract":"This paper proposes an algorithmic trading framework integrating\nEnvironmental, Social, and Governance (ESG) ratings with a pairs trading\nstrategy. It addresses the demand for socially responsible investment solutions\nby developing a unique algorithm blending ESG data with methods for identifying\nco-integrated stocks. This allows selecting profitable pairs adhering to ESG\nprinciples. Further, it incorporates technical indicators for optimal trade\nexecution within this sustainability framework. Extensive back-testing provides\nevidence of the model's effectiveness, consistently generating positive returns\nexceeding conventional pairs trading strategies, while upholding ESG\nprinciples. This paves the way for a transformative approach to algorithmic\ntrading, offering insights for investors, policymakers, and academics.","PeriodicalId":501478,"journal":{"name":"arXiv - QuantFin - Trading and Market Microstructure","volume":"38 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-01-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Trading and Market Microstructure","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2401.14761","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 0
Abstract
This paper proposes an algorithmic trading framework integrating
Environmental, Social, and Governance (ESG) ratings with a pairs trading
strategy. It addresses the demand for socially responsible investment solutions
by developing a unique algorithm blending ESG data with methods for identifying
co-integrated stocks. This allows selecting profitable pairs adhering to ESG
principles. Further, it incorporates technical indicators for optimal trade
execution within this sustainability framework. Extensive back-testing provides
evidence of the model's effectiveness, consistently generating positive returns
exceeding conventional pairs trading strategies, while upholding ESG
principles. This paves the way for a transformative approach to algorithmic
trading, offering insights for investors, policymakers, and academics.