[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127568-en":3,"doc-seo-127568-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},127568,687207020761,"Patrick","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predicting Brent Oil Futures Returns with Machine Learning - Dissertation Abstract","This thesis develops a multivariate time-series classification framework combining machine-learning methods with FinBERT sentiment extraction from daily oil news headlines. The approach improves on classical predictors of Brent oil futures returns by capturing market sentiment through NLP and modeling data non-linearity via ML. After model assessment, an ensemble model is used to implement simple trading strategies. An event study evaluates the impact of oil-related news sentiment on Brent futures returns during extreme events, showing outperformance versus buy-and-hold in a bull market under short-sale constraints.","Predicting Brent Oil Futures Returns with Machine Learning  \nand Text Data  \nAugust Nicolai Meland  \nDissertation written under the supervision of Professor Dan Tran  \nDissertation submitted in partial fulfilment of requirements for the MSc in Finance, at the Universidade Católica Portuguesa, 04/01/2023  \nAcknowledgements  \nI want to thank my supervisor, Professor Dan Tran , for fuelling my interest in machine learning and in the general field of artificial intelligence , and for boosting my motivation throughout this dissertation.  \nAbstract  \nIn this thesis we develop a multivariate time series classification model using machine learning techniques and the FinBERT model for extracting sentiment on daily oil news [headlines gathered from oilprice.com. The model](headlines gathered from oilprice.com. The model) improves classical methods for predicting brent oil futures by using natural language processing techniques to capture daily market sentiment on oil and by using machine learning techniques and models to capture non linearity in the data. Using model assessment techniques, we choose an ensemble model to conduct simple trading strategies to show how the developed models can be used in the financial markets. In addition, we explore how news sentiment about oil affects the return of brent oil futures under extreme events by conduction an event study. This thesis finds that by using the developed model in trading brent oil futures with short sale constraints, one can outperform a simple buy and hold trading strategy in a bull market. As the model shows a clear bias towards predicting positive future trading days, limitations have to be set on how the model would perform in a bear market.  \nTitle: Predicting Brent Oil Futures Returns with Machine Learning and Text Data  \nAuthor: August Nicolai Meland  \nKeywords: Machine Learning, Quantitative Finance, NLP, FinBERT, Oil, News, Sentiment  \nResumo  \nNesta tese, desenvolvemos um modelo de classificação de séries temporais multivariadasusando técnicas de aprendizado de máquina e o modelo FinBERT para extrair o sentimento em manchetes de notícias diárias sobre petróleo coletadas do [oilprice.com. O modelo melhora](oilprice.com. O modelo melhora)métodos clássicos para prever os futuros de petróleo Brent usando técnicas de processamento de linguagem natural para capturar o sentimento diário do mercado sobre o petróleo e usandotécnicas e modelos de aprendizado de máquina para capturar a não linearidade dos dados. Usando técnicas de avaliação de modelos, escolhemos um modelo de conjunto para realizarestratégias de negociação simples para mostrar como os modelos desenvolvidos podem ser usados nos mercados financeiros. Além disso, exploramos como o sentimento de notícias sobre o petróleo afeta o retorno dos futuros de petróleo Brent em eventos extremos, realizando umestudo de eventos. Esta tese conclui que, ao usar o modelo desenvolvido na negociação de futuros de petróleo Brent, é possível superar uma estratégia de compra e manutenção simplesem um mercado de alta.  \nTitle: Predicting Brent Oil Futures Returns with Machine Learning and Text Data  \nAuthor: August Nicolai Meland  \nKeywords: Machine Learning, Quantitativo Financeiro, NLP, FinBERT, Óleo, Notícias, Sentimentio  \nTable of Contents  \nList of Tables ............................................................................................................................. v  \n[List of Figures](List of Figures .......................................................................................................................... vi)[ ..........................................................................................................................](List of Figures .......................................................................................................................... vi)[ vi](List of Figures ...............................................................................................................","cbCaiehNuRaDVGfz","https://ap.wps.com/l/cbCaiehNuRaDVGfz","pdf",693891,1,41,"English","en",105,"# 1. Introduction\n# 2. Literature Review\n# 3. Data Description\n# 4. Event Study\n# 5. Machine Learning\n## 5.1 Machine Learning\n## 5.2 Natural Language Processing\n## 5.3 Machine Learning Models\n## 5.4 Dealing with Time-Series Data in ML\n## 5.5 Model Assessment\n# 6. Methodology\n## 6.1 Data Preparation\n## 6.2 Modeling\n## 6.3 Robustness Check","[{\"question\":\"What modeling approach does the thesis propose for Brent oil futures returns?\",\"answer\":\"It builds a multivariate time-series classification model using machine-learning techniques together with FinBERT to extract sentiment from daily oil news headlines.\"},{\"question\":\"How does text sentiment information enter the prediction framework?\",\"answer\":\"FinBERT is used to extract sentiment from daily oil news headlines, and the resulting text-derived sentiment helps improve predictions of Brent oil futures returns.\"},{\"question\":\"How are the models evaluated and used in trading?\",\"answer\":\"The thesis applies model assessment techniques to select an ensemble model, then uses it to run simple trading strategies under short-sale constraints.\"}]","Predicting Brent Oil Futures Returns with Machine Learning - Dissertation Abstract | PDF",1785940027,103,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"predicting-brent-oil-futures-returns-with-machine-learning-dissertation-abstract","",{"@graph":36,"@context":86},[37,54,69],{"@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/predicting-brent-oil-futures-returns-with-machine-learning-dissertation-abstract/127568/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What modeling approach does the thesis propose for Brent oil futures returns?","Question",{"text":76,"@type":77},"It builds a multivariate time-series classification model using machine-learning techniques together with FinBERT to extract sentiment from daily oil news headlines.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does text sentiment information enter the prediction framework?",{"text":81,"@type":77},"FinBERT is used to extract sentiment from daily oil news headlines, and the resulting text-derived sentiment helps improve predictions of Brent oil futures returns.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the models evaluated and used in trading?",{"text":85,"@type":77},"The thesis applies model assessment techniques to select an ensemble model, then uses it to run simple trading strategies under short-sale constraints.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]