[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121976-en":3,"doc-seo-121976-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121976,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Tail-Risk and Sustainability: Can ESG scores accurately predict Value at Risk? - A machine learning based approach","The thesis investigates whether ESG scores can serve as an accurate predictor of Value at Risk (VaR) at the 1% and 5% confidence levels using machine learning models applied to ESG-related measures. Results indicate low predictive accuracy overall, with Random Forest Regressors showing the strongest performance among tested algorithms. ESG and especially the Environmental Score correlate most strongly with VaR, while findings suggest that ESG scores alone lack reliability across significance levels. Data covers S&P 500 firms from 2000 to 2024, and machine learning outperforms benchmark linear regression.","Tail-Risk and Sustainability: Can ESG scores accurately predict Value at Risk?– A machine  \nlearning based approach  \nUlrich Mohme  \nDissertation written under the supervision of Paul Karehnke  \nDissertation submitted in partial fulfilment of requirements for the International MSc in Finance, at Universidade Católica Portuguesa and for the Master in Management at ESCP Business School, 20.05.2024.  \nTable of Contents  \n1. Introduction ......................................................................................................................4  \n2. Literature Review.............................................................................................................9  \n2.1. Importance of ESG Factors ............................................................................................. 9  \n2.2. ESG Facors, Firm Valuation and Stock Returns ............................................................ 9  \n2.3. Executive Behaviour towards ESG Risk........................................................................ 11  \n2.4. Link: Carbon, Climate and Financial Risk ................................................................... 12  \n2.5. ESG, Volatility and Tail Risk ........................................................................................ 13  \n3. Data.................................................................................................................................. 15  \n4. Methodology ................................................................................................................... 18  \n5. Results .............................................................................................................................22  \n5.1. Descriptive Statistics .....................................................................................................22  \n5.2. Descriptive Chart Analysis ............................................................................................ 26  \n5.3. Correlation Analysis ..................................................................................................... 30  \n5.4. Findings from machine learning based predictions ...................................................... 32  \n6. Conclusion.......................................................................................................................34  \nPublication bibliography ...................................................................................................36  \nAppendix .............................................................................................................................39  \nList of Tables  \nTable 1: Descriptive statistics of all variables, cleaned from null variables but not from outliers......................................................................................................................................23  \nTable 2: Results from machine learning based predictions on VaR at the 1% and 5% confidence level ....................................................................................................................... 32  \nList of Figures  \nFigure 1: Histograms of the distributions with a bin size of 50 ...............................................26  \nFigure 2: Density Plot of VaR at 1% and VaR at 5% stacked .................................................27  \nFigure 3: Density plots of the distributions of the variables ....................................................28  \nFigure 4: Box plot of the distributions of all observe variables...............................................29  \nFigure 5: Correlation Matrix of the analysed variables in style of a heat map ........................30  \nFigure 6: Regression plot of the ESG related scores and VaR at 1% confidence level...........31  \nFigure 7: Regression plot of the ESG related scores and VaR at 5% confidence level...........32  \nList of Equations  \nEquation 1: Return Index excluding dividends..................................","cbCais5iwl3eZ9mg","https://ap.wps.com/l/cbCais5iwl3eZ9mg","pdf",1408098,1,41,"English","en",105,"# Introduction\n# Literature Review\n## Importance of ESG Factors\n## ESG Factors, Firm Valuation and Stock Returns\n## Executive Behaviour towards ESG Risk\n## Link: Carbon, Climate and Financial Risk\n## ESG, Volatility and Tail Risk\n# Data\n# Methodology\n# Results\n## Descriptive Statistics\n## Descriptive Chart Analysis\n## Correlation Analysis\n## Findings from machine learning based predictions\n# Conclusion\n# Publication bibliography\n# Appendix","[{\"question\":\"How is Value at Risk (VaR) predicted in this thesis?\",\"answer\":\"VaR at the 1% and 5% confidence levels is predicted by applying multiple machine learning algorithms to ESG scores and related measures.\"},{\"question\":\"Which model shows the highest predictive accuracy?\",\"answer\":\"Random Forest Regressors achieve the highest degree of accuracy among the machine learning algorithms used.\"},{\"question\":\"Do the findings support ESG scores as a reliable predictor of VaR?\",\"answer\":\"Overall, ESG scores alone are not found to be a reliable predictor of VaR across the tested significance levels, despite detecting some linear correlation and stronger correlations with certain ESG components.\"}]","Tail-Risk and Sustainability: Can ESG scores accurately predict Value at Risk? - A machine learning based approach | PDF",1785808106,103,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"tail-risk-and-sustainability-can-esg-scores-accurately-predict-value-at-risk-a-machine-learning-based-approach","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/tail-risk-and-sustainability-can-esg-scores-accurately-predict-value-at-risk-a-machine-learning-based-approach/121976/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"How is Value at Risk (VaR) predicted in this thesis?","Question",{"text":75,"@type":76},"VaR at the 1% and 5% confidence levels is predicted by applying multiple machine learning algorithms to ESG scores and related measures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which model shows the highest predictive accuracy?",{"text":80,"@type":76},"Random Forest Regressors achieve the highest degree of accuracy among the machine learning algorithms used.",{"name":82,"@type":73,"acceptedAnswer":83},"Do the findings support ESG scores as a reliable predictor of VaR?",{"text":84,"@type":76},"Overall, ESG scores alone are not found to be a reliable predictor of VaR across the tested significance levels, despite detecting some linear correlation and stronger correlations with certain ESG components.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]