Zhi Chen, Zachary Feinstein, Ionut Florescu, Papa Momar Ndiaye
{"title":"确定ESG原始分数与ESG综合分数对年度公司股票收益和波动性预测能力的差异","authors":"Zhi Chen, Zachary Feinstein, Ionut Florescu, Papa Momar Ndiaye","doi":"arxiv-2312.00202","DOIUrl":null,"url":null,"abstract":"Investors are increasingly incorporating Environmental, Social, and\nGovernance (ESG) ratings into their investment strategies to evaluate and\nmanage potential risks and sustainability of companies. ESG ratings typically\nfollow a hierarchical structure, where raw data points are progressively\naggregated, leading to individual E, S, G scores and ultimately aggregating in\na broad, consolidated ESG score. While many studies have investigated the\nrelationship between stock performance and individual or overall ESG scores,\nfew have used raw ESG data into their analyses. Therefore, this paper aims to\nexplore the difference in predictive capabilities of ESG raw scores versus\naggregated scores on annual company stock returns and volatility. Our findings\nreveal a trend where the predictive power is strongest at the raw data level,\nand it gradually weakens through successive stages of aggregation, with the\noverall scores exhibiting the weakest predictive capability. This result\nhighlights the effectiveness of raw ESG data in capturing the complex dynamics\nbetween ESG factors and financial performance, making it the superior choice in\nfurther study.","PeriodicalId":501045,"journal":{"name":"arXiv - QuantFin - Portfolio Management","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2023-11-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Determining the Difference in Predictive Capabilities of ESG Raw Scores versus ESG Aggregated Scores on Annual Company Stock Returns And Volatility\",\"authors\":\"Zhi Chen, Zachary Feinstein, Ionut Florescu, Papa Momar Ndiaye\",\"doi\":\"arxiv-2312.00202\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Investors are increasingly incorporating Environmental, Social, and\\nGovernance (ESG) ratings into their investment strategies to evaluate and\\nmanage potential risks and sustainability of companies. ESG ratings typically\\nfollow a hierarchical structure, where raw data points are progressively\\naggregated, leading to individual E, S, G scores and ultimately aggregating in\\na broad, consolidated ESG score. While many studies have investigated the\\nrelationship between stock performance and individual or overall ESG scores,\\nfew have used raw ESG data into their analyses. Therefore, this paper aims to\\nexplore the difference in predictive capabilities of ESG raw scores versus\\naggregated scores on annual company stock returns and volatility. Our findings\\nreveal a trend where the predictive power is strongest at the raw data level,\\nand it gradually weakens through successive stages of aggregation, with the\\noverall scores exhibiting the weakest predictive capability. This result\\nhighlights the effectiveness of raw ESG data in capturing the complex dynamics\\nbetween ESG factors and financial performance, making it the superior choice in\\nfurther study.\",\"PeriodicalId\":501045,\"journal\":{\"name\":\"arXiv - QuantFin - Portfolio Management\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-11-30\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Portfolio Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2312.00202\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Portfolio Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2312.00202","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Determining the Difference in Predictive Capabilities of ESG Raw Scores versus ESG Aggregated Scores on Annual Company Stock Returns And Volatility
Investors are increasingly incorporating Environmental, Social, and
Governance (ESG) ratings into their investment strategies to evaluate and
manage potential risks and sustainability of companies. ESG ratings typically
follow a hierarchical structure, where raw data points are progressively
aggregated, leading to individual E, S, G scores and ultimately aggregating in
a broad, consolidated ESG score. While many studies have investigated the
relationship between stock performance and individual or overall ESG scores,
few have used raw ESG data into their analyses. Therefore, this paper aims to
explore the difference in predictive capabilities of ESG raw scores versus
aggregated scores on annual company stock returns and volatility. Our findings
reveal a trend where the predictive power is strongest at the raw data level,
and it gradually weakens through successive stages of aggregation, with the
overall scores exhibiting the weakest predictive capability. This result
highlights the effectiveness of raw ESG data in capturing the complex dynamics
between ESG factors and financial performance, making it the superior choice in
further study.