[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-116979-en":3,"doc-seo-116979-105":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},116979,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Can machine learning beat the Norwegian stock market - Master’s Thesis in Business Administration","Multiple studies evaluate machine-learning stock portfolios on large markets, but evidence is limited for smaller, less-liquid cap markets such as Norway. This master’s thesis investigates whether machine-learning methods can beat the Norwegian stock market using eight portfolios built from probability outputs of support vector machines, random forests, and logistic regression implemented in R. Portfolios are tested from 2013 to 2022 with monthly and daily holding periods. Results show stronger performance before 2020, followed by loss of predictive power and negative returns from 2021 due to market regime changes and transaction costs.","School of Business and Economics  \nCan machine learning beat the Norwegian stock market?  \nA comparison of popular machine learning models Martin Aronsen and Christian Markussen  \nMaster’s Thesis in Business Administration, BED-3901, June 2023  \ni. Acknowledgments  \nThis thesis marks the end ofour academic journey at the School of Business and Economics at UiT. As the last academic work for our master’s degree, this thesis consists of 30-ECTS. We are writing this thesis for our minor subject, Finance. Dwelling into the field of machine-learning has been a pleasant experience, which has given us many challenges, leading to the lowest oflows and highest of highs. And we encourage anyone who is curious about machine-learning to write their thesis about it.  \nWe would like to thank our supervisor, Thomas Leirvik. He has been a great sparring partner when it comes to developing the machine-learning models, as well as giving us ideas on how to improve upon our work.  \nLastly, we would like to thank our fellow students, especially those who have joined us for excessive coffee breaks. If it weren’t for those excessive breaks, we would have had much less stress at the end ofour journey.  \nTromsø, June 1st, 2023  \nMartin Aronsen & Christian Markussen  \nKeywords: Finance, Machine Learning, Market Efficiency, Oslo Stock Exchange, Portfolio  \nii. Abstract  \nMultiple studies on the performance of machine-learning stock portfolios have shown the efficacy of machine-learning portfolios on large stock exchanges, especially the American-and Chinese market. Fewer studies have been conducted on smaller cap markets, which consists of smaller, less-liquid investment options. The purpose of this thesis is therefore to explore the possibilities to beat the Norwegian stock market using machine-learning modalities. Eight different machine-learning portfolios have been constructed based on probability outputs of support vector machines, random forests and logistic regression created using the R software and packages “e1071”,“randomForest”,“gbm” and “caret”.  \nPortfolios are tested from the end of 2013 to the end of 2022. Results of the thesis are in line with previous research that apply machine learning on the Oslo stock exchange for early periods in the sample, but find different results for the extended period. Machine-learning portfolios with monthly holding periods perform well before 2020, particularly the random forest portfolio.  \nThey do however lose their predictive power after this period and generate negative return beginning in 2021. Returns from daily portfolios are eaten up by transaction costs in multiple periods before 2020 and thus fail to consistently outperform the market. Some daily portfolios so show promise in the later period where the monthly portfolios underperform. The thesis therefore concludes that while machine-learning does show some promise on the Norwegian stock market, they cannot be relied upon to generate consistent outperformance over the benchmark index.  \nTable of Contents  \n1 Introduction ........................................................................................................................ 1  \n1.1 Problem statement ............................................................................................................ 2  \n2 Risk and return in the financial market .............................................................................. 3  \n2.1 Modern Portfolio Theory ................................................................................................. 3  \n2.2 Capital Asset Pricing Model ............................................................................................ 4  \n2.3 Three – and four-factor model ......................................................................................... 5  \n2.4 Sharpe-ratio ...................................................................................................................... 8  \n2.5 Beating the market ........","cbCaipwjUCIawKF6","https://ap.wps.com/l/cbCaipwjUCIawKF6","pdf",1429145,1,72,"English","en",105,"# Introduction\n## Problem statement\n# Risk and return in the financial market\n## Modern Portfolio Theory\n## Capital Asset Pricing Model\n## Three- and four-factor model\n## Sharpe-ratio\n## Beating the market\n# Machine-learning\n## Random forest\n## Logistic regression\n## Support vector machine\n# Methodology\n## Data\n## Preprocessing\n## Training and testing periods\n## Transaction cost","[{\"question\":\"What question does the thesis address about machine learning and the Norwegian market?\",\"answer\":\"It explores whether machine-learning portfolios can beat the Norwegian stock market, using a portfolio construction approach based on several popular machine-learning models.\"},{\"question\":\"How are the machine-learning portfolios constructed in this thesis?\",\"answer\":\"Eight portfolios are built from probability outputs of support vector machines, random forests, and logistic regression, implemented using R packages including e1071, randomForest, gbm, and caret.\"},{\"question\":\"What do the results indicate about performance across time periods?\",\"answer\":\"Monthly portfolios perform relatively well before 2020, especially random forests, but they lose predictive power after this period and start generating negative returns beginning in 2021, while daily portfolios are heavily affected by transaction costs.\"}]","Can machine learning beat the Norwegian stock market - Master’s Thesis in Business Administration | PDF",1785672944,181,{"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},"can-machine-learning-beat-the-norwegian-stock-market-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/can-machine-learning-beat-the-norwegian-stock-market-masters-thesis-in-business-administration/116979/",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","2026-08-02",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 question does the thesis address about machine learning and the Norwegian market?","Question",{"text":75,"@type":76},"It explores whether machine-learning portfolios can beat the Norwegian stock market, using a portfolio construction approach based on several popular machine-learning models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How are the machine-learning portfolios constructed in this thesis?",{"text":80,"@type":76},"Eight portfolios are built from probability outputs of support vector machines, random forests, and logistic regression, implemented using R packages including e1071, randomForest, gbm, and caret.",{"name":82,"@type":73,"acceptedAnswer":83},"What do the results indicate about performance across time periods?",{"text":84,"@type":76},"Monthly portfolios perform relatively well before 2020, especially random forests, but they lose predictive power after this period and start generating negative returns beginning in 2021, while daily portfolios are heavily affected by transaction costs.","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"]