[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123229-en":3,"doc-seo-123229-105":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},123229,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Creating alpha with Machine Learning algorithms - Master’s thesis in Business administration","This thesis investigates how machine learning methods and traditional financial models can be combined for portfolio optimization centered on the OBX index. It tests whether ML algorithms provide better forecasting of returns, estimation of volatility, and portfolio weight decisions than established econometric approaches. Random Forest, Support Vector Machines, Gradient Boosting Machines, and k-Nearest Neighbors are compared with ARIMA and multiple GARCH specifications. Model performance is assessed using risk-adjusted measures and alpha evaluation via Fama-French-Carhart regressions.","Faculty of bioscience, fisheries and economics  \nCreating alpha with Machine Learning algorithms  \nSondre Teigen  \nMaster’s thesis in Business administration , BED-3901, December 2024  \ni. Acknowledgments  \nWriting this thesis has been an interesting and challenging journey, and it would not have been possible without the support and guidance of many individuals. I would like to take this opportunity to express my gratitude to those who have contributed to the successful completion of this work.  \nFirst and foremost, I am grateful to my supervisor, Thomas Leirvik for their guidance and constructive feedback. I am also thankful to my colleagues and peers at The University of Tromso, whose discussions and shared experiences have been most appreciated.  \nA special note of appreciation goes to Tore Teigen for his insightful participation in the more difficult aspects of Machine learning and economic theory in general. And at last I would thank Liv Mathisen for being able to listen to financial talk for half a year.  \nThis thesis is the culmination of collective efforts, and I am deeply thankful to everyone who has played a part in this journey.  \nTromsø, December 1􀯦􀯧 , 2024  \nSondre Johann Teigen  \nii. Abstract  \nThis thesis investigates the application of machine learning (ML) and traditional financial models in portfolio optimization, focusing on the OBX index. The research aims to determine whether ML algorithms can outperform traditional models in forecasting returns, estimating volatility, and optimizing portfolio weights.  \nThe study employs advanced ML techniques such as Random Forest, Support Vector Machines, Gradient Boosting Machines, and k-Nearest Neighbors alongside traditional models, including ARIMA for return prediction and various GARCH frameworks for volatility modeling. Performance is evaluated using risk-adjusted metrics such as the Sharpe Ratio, Sortino Ratio, and Fama-French-Carhart regressions to assess the alpha generated by each model.  \nResults reveal that ML-based portfolios significantly outperform the benchmark OBX index in both risk and return. Notably, the Random Forest model with a nine-week rolling window achieved the highest annualized return of 17.83% and a cumulative total return of 97.71% over 200 weeks, while maintaining lower volatility than the benchmark. Traditional models also performed well, with the IGARCH-based portfolio showing strong results, although they fell short of ML-based approaches.  \nContents  \ni. Acknowledgments .............................................................................................................. 1  \nii. Abstract .................................................................................................................................. 2  \n1 Introduction: ....................................................................................................................... 7  \n2 Theoretical framework: ...................................................................................................... 9  \n2.1 Theoretical Framework – Introduction ...................................................................... 9  \n2.2 Risk / Return............................................................................................................. 10  \n2.3 GARCH Models for volatility and correlations ....................................................... 12  \n2.4 Minimum Variance Portfolio: .................................................................................. 13  \n2.5 Machine learning in financial markets: .................................................................... 14  \n2.5.1 Random Forrest Model: ................................................................................... 16  \n2.5.2 Support Vector Machines:................................................................................ 18  \n2.5.3 K-Nearest-Neighbour: ...................................................................................... 18  \n2.5.4 Grad","cbCaitvG71KtzJcW","https://ap.wps.com/l/cbCaitvG71KtzJcW","pdf",1210259,1,61,"English","en",105,"# 1 Introduction\n# 2 Theoretical framework\n## 2.1 Theoretical Framework – Introduction\n## 2.2 Risk / Return\n## 2.3 GARCH Models for volatility and correlations\n## 2.4 Minimum Variance Portfolio\n## 2.5 Machine learning in financial markets\n## 2.6 ARIMA\n## 2.7 Rolling window Volatility\n## 2.8 Portfolio construction\n## 2.9 Model evaluation\n## 2.10 Sortino Ratio\n## 2.11 Fama French Carhart\n## 2.12 Potential Pitfalls or Risks of the Theories/Models\n# 3 Methodology","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To determine whether machine learning algorithms can outperform traditional financial models in forecasting returns, estimating volatility, and optimizing portfolio weights for the OBX index.\"},{\"question\":\"Which machine learning and traditional models are compared?\",\"answer\":\"The study uses Random Forest, Support Vector Machines, Gradient Boosting Machines, and k-Nearest Neighbors, and compares them with traditional approaches including ARIMA for return prediction and GARCH-type frameworks for volatility modeling.\"},{\"question\":\"How is performance evaluated and how is alpha assessed?\",\"answer\":\"Performance is measured with risk-adjusted metrics such as the Sharpe Ratio and Sortino Ratio, and alpha is assessed using Fama-French-Carhart regressions alongside information ratio.\"}]","Creating alpha with Machine Learning algorithms - Master’s thesis in Business administration | PDF",1785815346,154,{"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},"creating-alpha-with-machine-learning-algorithms-masters-thesis-in-business-administration","",{"@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/creating-alpha-with-machine-learning-algorithms-masters-thesis-in-business-administration/123229/",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},"What is the main goal of the thesis?","Question",{"text":75,"@type":76},"To determine whether machine learning algorithms can outperform traditional financial models in forecasting returns, estimating volatility, and optimizing portfolio weights for the OBX index.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning and traditional models are compared?",{"text":80,"@type":76},"The study uses Random Forest, Support Vector Machines, Gradient Boosting Machines, and k-Nearest Neighbors, and compares them with traditional approaches including ARIMA for return prediction and GARCH-type frameworks for volatility modeling.",{"name":82,"@type":73,"acceptedAnswer":83},"How is performance evaluated and how is alpha assessed?",{"text":84,"@type":76},"Performance is measured with risk-adjusted metrics such as the Sharpe Ratio and Sortino Ratio, and alpha is assessed using Fama-French-Carhart regressions alongside information ratio.","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"]