[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117321-en":3,"doc-seo-117321-105":29,"detail-sidebar-cat-0-en-105":90},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117321,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","The Impact of Machine Learning on Stock Momentum Strategies - Work Project Analysis","This thesis explores integrating four distinct investment strategies within a unified fund, using the Markowitz framework for portfolio optimization and a weighting-based allocation scheme to improve performance and risk management. It focuses on how machine learning affects momentum-based stock selection, specifically applying a Random Forest model to evaluate predictive accuracy and compare outcomes from actual versus predicted returns. Results indicate higher prediction accuracy and, when combined with momentum strategies, lower volatility and drawdowns alongside improved risk-adjusted returns, with robustness in volatile markets.","A Work Project, presented as part ofthe requirements for the Award of a Master’s degree in Finance from the Nova School of Business and Economics.  \nAnalysis of Quantitative Investment Strategies  \nThe Impact of Machine Learning on Stock Momentum Strategies  \nSamuel Schandl  \nWork project carried out under the supervision of:  \nNicholas Hirschey  \n20/12/2023  \nAbstract  \nThis thesis explores the integration of four distinct investment strategies within a unified fund, employing the Markowitz framework for portfolio optimization. The study further incorporates an allocation scheme based on popular weighting principles. By synergizing diverse investment approaches and leveraging the Markowitz model, this research aims to enhance portfolio performance and risk management. The proposed allocation scheme seeks to capitalize on market trends and investor sentiment, offering a comprehensive strategy for constructing a well-balanced and resilient investment fund.  \nThe individual project investigates the impact of machine learning, specifically the Random Forest model, on momentum-based stock selection strategies. It evaluates the model's predictive accuracy and compares the performance of strategies based on actual versus predicted returns. Findings reveal that machine learning enhances prediction accuracy and, when applied to momentum strategies, demonstrates reduced volatility, lower drawdowns, and improved risk-adjusted returns. The study highlights the model's resilience, particularly in volatile market conditions, while acknowledging limitations related to data and computational resources. This research offers insights into the integration of machine learning in financial strategies to navigate complex market dynamics.  \nThis work used infrastructure and resources funded by Fundação para a Ciência e a Tecnologia (UID/ECO/00124/2013, UID/ECO/00124/2019 and Social Sciences DataLab, Project 22209), POR Lisboa (LISBOA-01-0145-FEDER-007722 and Social Sciences DataLab, Project 22209) and POR Norte (Social Sciences DataLab, Project 22209) .  \n1. Introduction  \nIn this thesis, we conduct an in-depth analysis of four investment strategies, each characterized by its distinct approach and unique features. The initial sections are devoted to a thorough introduction of these strategies, outlining their key principles and the theoretical framework that underpins each of them. This is followed by a detailed comparative analysis where we examine and contrast the performance indicators associated with each strategy.  \nThe core objective of this group part is to provide a comprehensive evaluation of these strategies both as standalone approaches and combined. We aim to investigate how strategic optimization and thoughtful allocation can potentially enhance their collective performance. By doing so, this study strives to not only assess the individual effectiveness of each strategy but also to explore the synergies that may arise when they are integrated. Through this approach, we seek to answer critical questions regarding the efficacy of these diverse investment strategies and to understand the potential advantages of their combination.  \n2. Individual Strategies  \n2.1 Multifactor Earnings Surprise Strategy (SUE)  \n2.1.1 Economic Motivation  \nThe Efficient Market Hypothesis (EMH), introduced by Fama (1970), anchors modern investment theory with its assertion that stock prices in efficient markets fully reflect all available information. This hypothesis, however, has been challenged by various anomalies, indicating more intricate forces at work in financial markets. Notable among these are the post-earnings announcement drift (PEAD), originally identified by Ball and Brown (1968), and the concept of price momentum, as elaborated by Jegadeesh and Titman (1993), both later acknowledged by Fama (1998) as significant exceptions. PEAD, often labeled as the ‘granddaddy of underreaction events’, reveals that stock prices persist in responding to earning","cbCaiuICXvhVwBlZ","https://ap.wps.com/l/cbCaiuICXvhVwBlZ","pdf",1334121,1,56,"English","en",105,"# Introduction\n# Individual Strategies\n## Multifactor Earnings Surprise Strategy (SUE)","[{\"question\":\"What is the main research focus of the thesis?\",\"answer\":\"The thesis studies how machine learning, using a Random Forest model, impacts momentum-based stock selection and evaluates the resulting performance and risk effects.\"},{\"question\":\"How are investment strategies combined in the unified fund?\",\"answer\":\"The study integrates four investment strategies and applies the Markowitz framework for portfolio optimization, together with an allocation scheme based on common weighting principles.\"},{\"question\":\"What improvements does machine learning bring to momentum strategies?\",\"answer\":\"Machine learning increases prediction accuracy and, when applied to momentum strategies, is associated with reduced volatility, lower drawdowns, and higher risk-adjusted returns, especially in volatile market 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is the main research focus of the thesis?","Question",{"text":74,"@type":75},"The thesis studies how machine learning, using a Random Forest model, impacts momentum-based stock selection and evaluates the resulting performance and risk effects.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How are investment strategies combined in the unified fund?",{"text":79,"@type":75},"The study integrates four investment strategies and applies the Markowitz framework for portfolio optimization, together with an allocation scheme based on common weighting principles.",{"name":81,"@type":72,"acceptedAnswer":82},"What improvements does machine learning bring to momentum strategies?",{"text":83,"@type":75},"Machine learning increases prediction accuracy and, when applied to momentum strategies, is associated with reduced volatility, lower drawdowns, and higher risk-adjusted returns, especially in volatile market 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