[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122283-en":3,"doc-seo-122283-105":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":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},122283,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Predicting Financial Data with Macroeconomic Factors using Machine Learning - Thesis","This thesis predicts stock index movements by analyzing macroeconomic factors with machine learning to generate market sentiment. It examines major U.S. stock index funds such as the S&P 500 alongside key indicators including GDP, unemployment rate, consumer price index, money supply, and retail sales. Economic data are collected from open sources including the Federal Reserve and NASDAQ, with news data used to derive sentiment. Tree-based models such as XGBoost and Random Forest are evaluated across market cycles and geopolitical events.","West Chester University  \nDigital Commons @ West Chester University  \n\n| West Chester University Master’s Theses | Masters Theses and Doctoral Projects |\n| --- | --- |\n| Spring 2024\u003Cbr>Predicting Financial Data with Macroeconomic Factors using Machine Learning\u003Cbr>Sai Sravya Anem\u003Cbr>[sa1013360@wcupa.edu](sa1013360@wcupa.edu)\u003Cbr>Follow this and additional works at: [https://digitalcommons.wcupa.edu/all_theses](https://digitalcommons.wcupa.edu/all_theses) |  |\n\nRecommended Citation  \nAnem, Sai Sravya, \"Predicting Financial Data with Macroeconomic Factors using Machine Learning\"(2024) . West Chester University Master’s Theses. 340.  \n[https://digitalcommons.wcupa.edu/all_theses/340](https://digitalcommons.wcupa.edu/all_theses/340)  \nThis Thesis is brought to you for free and open access by the Masters Theses and Doctoral Projects at Digital Commons @ West Chester University. It has been accepted for inclusion in West Chester University Master’s Theses by an authorized administrator of Digital Commons @ West Chester University. For more information, [please contact wcressler@wcupa.edu](please contact wcressler@wcupa.edu).  \nPredicting Financial Data with Macroeconomic Factors using Machine Learning  \nA Thesis Presented to the Faculty of the  \nDepartment of Computer Science  \nWest Chester University  \nWest Chester, PA  \nIn Partial Fulfillment of the Requirements for the Degree of Master of Science  \nBy  \nSai Sravya Anem  \n©Copyright 2024 Sai Sravya  \nAll Rights Reserved  \nAcknowledgements  \nI would like to thank my parents, family, and my husband, Harsha for supporting me, standing by me, and providing unwavering support throughout this project. I would also like to thank my advisor, Dr. Md Amiruzzaman, for believing in me and for providing me with his invaluable advice.  \nAbstract  \nThis thesis presents a predicting stock index by analyzing the macroeconomic Factors using Machine Learning to generate market sentiment. The study explores popular US stock index funds like S&P 500 to critical economic indicators like GDP, Unemployment rate, Consumer Price Index, Money Supply, Retail Sales, etc. The economic data is collected from open-sourced datasets from the Federal Reserve, NASDAQ, and news websites. Data is cleaned and transformed to build a narrative to estimate quarterly fund returns. Tree-based algorithms like XGBoost and Random Forest are used for the prediction. Market sentiment is generated using traditional natural language processing methods and attempts to leverage large language models to summarize market sentiment. Overall, the index forecast performance is evaluated by different market cycles and  \ngeopolitical events using XGBoost Algorithm.  \nTable of Contents  \nList of Figures vii  \nList of Tables viii  \n1 Introduction 1  \n1.1 Stock Market and Macro-Economic Factors ..................... 1  \n1.2 Overview ....................................... 2  \n1.3 Problem Statement .................................. 4  \n1.3.1 Can Macroeconomic Factors signal the Stock Market Direction? ...... 5  \n1.3.2 Can we Forecast the stock market with the macroeconomic factors? .... 5  \n1.4 Thesis Outline ..................................... 6  \n2 Related Work 8  \n2.1 Relation between the stock market and Macroeconomic Indicators ......... 8  \n2.2 Stock Market Prediction using different Machine Learning Models ......... 10  \n2.3 Stock Market Prediction using sentiment analysis .................. 13  \n2.4 Stock Market Prediction using Macro Indicators and Sentiment Analysis ...... 14  \n3 Method 16  \n3.1 Stock and Macroeconomic Factors Data ....................... 16  \n3.2 Market News Data .................................. 19  \n3.3 Machine Learning Design .............................. 21  \n3.3.1 Correlation .................................. 23  \n3.3.2 Forecast Method ............................... 24  \n3.3.3 Random Forest Model ............................ 24  \n3.3.4 XGBoost Model ............................... 28  \n3.4 Sentiment Analys","cbCair9xsfUTnPh1","https://ap.wps.com/l/cbCair9xsfUTnPh1","pdf",4249040,1,82,"English","en",105,"# List of Figures\n# List of Tables\n# 1 Introduction\n## 1.1 Stock Market and Macro-Economic Factors\n## 1.2 Overview\n## 1.3 Problem Statement\n## 1.4 Thesis Outline\n# 2 Related Work\n## 2.1 Relation between the stock market and Macroeconomic Indicators\n## 2.2 Stock Market Prediction using different Machine Learning Models\n## 2.3 Stock Market Prediction using sentiment analysis\n## 2.4 Stock Market Prediction using Macro Indicators and Sentiment Analysis\n# 3 Method\n## 3.1 Stock and Macroeconomic Factors Data\n## 3.2 Market News Data\n## 3.3 Machine Learning Design\n## 3.4 Sentiment Analysis\n## 3.5 Test of Normality\n# 4 Results and Evaluation\n## 4.1 Feature Importance of Macroeconomic Factors\n## 4.2 Prediction Performance Evaluation\n## 4.3 Market Sentiment Analysis using Weekly news\n# 5 Conclusion and Future Work\n## 5.1 Conclusion\n## 5.2 Future Work\n# 6 Appendices","[{\"question\":\"Which macroeconomic indicators are used in the study?\",\"answer\":\"The thesis uses indicators such as GDP, unemployment rate, consumer price index, money supply, and retail sales, among others.\"},{\"question\":\"How is market sentiment generated for the predictions?\",\"answer\":\"Market sentiment is produced from market news using traditional natural language processing methods and experiments with large language models for summarization.\"},{\"question\":\"Which machine learning models are applied and how is performance evaluated?\",\"answer\":\"The prediction models include tree-based methods such as XGBoost and Random Forest, with evaluation across different market cycles and geopolitical events.\"}]","Predicting Financial Data with Macroeconomic Factors using Machine Learning - 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