[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119470-en":3,"doc-seo-119470-105":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},119470,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Essays on Machine Learning on Financial Economics, and Human Capital Development - Dissertation","This dissertation investigates predictive modeling of economic and financial risks using advanced econometric and machine learning methods, alongside human capital development. Three connected essays examine risk assessment across education, banking stability, and targeted lending programs. The first models systemic bank risk using machine learning with Value at Risk and other risk metrics, improving traditional assessment. The second improves loan risk prediction for banks in the Main Street Lending Program by combining borrower traits, macro conditions, and financial indicators. The third analyzes how birth order and gender affect children’s test scores to illuminate education and long-run mobility.","UC Riverside  \nUC Riverside Electronic Theses and Dissertations  \nTitle  \nEssays on Machine Learning on Financial Economics, and Human Capital Development  \nPermalink  \n[https://escholarship.org/uc/item/327749cq](https://escholarship.org/uc/item/327749cq)  \nISBN  \n9798288852862  \nAuthor  \nzhou, qichen  \nPublication Date  \n2025-05-25  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nUNIVERSITY OF CALIFORNIA  \nRIVERSIDE  \nEssays on Machine Learning on Financial Economics, and Human Capital  \nDevelopment  \nA Dissertation submitted in partial satisfaction  \nof the requirements for the degree of  \nDoctor of Philosophy  \nin  \nEconomics  \nby  \nQichen Zhou  \nJune 2025  \nDissertation Committee:  \nDr. Marcelle Chauvet, Chairperson  \nDr. Joseph Cummins  \nDr. Jana Grittersova  \nCopyright by Qichen Zhou 2025  \nThe Dissertation of Qichen Zhou is approved:  \nCommittee Chairperson  \nUniversity of California, Riverside  \nAcknowledgments  \nI am sincerely grateful to my advisor, Professor Marcelle Chauvet, for her academic guidance, support, and patience during my PhD studies. Her constructive feedback helped me improve my research, and her mentorship has shaped how I approach academic work. I also appreciate her support throughout the job market process.  \nI would like to thank my dissertation committee members, Professor Joseph Cummins and Professor Jana Grittersova, for their helpful comments and suggestions, especially on my job market paper. Their input was important to the development of my work. At the same time, I would also like to thank Professor Dongwon Lee, Ruoyao Shi, Donggyu Kim, Steven Helfand, Sarojini Hirshleifer, and Anil Deolalikar for their feedback on my research and for their participation in my oral exam and practice job talk.  \nI must also express my appreciation to my PhD classmates, colleagues, UCR alumnand Gary Kuzas for their support in various aspects of my study and life.  \nLast but not the least, I am deeply indebted to my family, for their love, support  \nand understanding throughout this journey.  \nTo my family for all the support.  \nv  \nABSTRACT OF THE DISSERTATION  \nEssays on Machine Learning on Financial Economics, and Human Capital Development  \nby  \nQichen Zhou  \nDoctor of Philosophy, Graduate Program in Economics  \nUniversity of California, Riverside, June 2025  \nDr. Marcelle Chauvet, Chairperson  \nThe overarching theme of this dissertation revolves around the predictive modeling of economic and financial risks using advanced econometric and machine learning techniques, and human capital development. The three essays presented in this work explore diverse but interconnected aspects of risk assessment: human capital development through education, systemic financial risk forecasting, and loan risk prediction in government intervention programs. Each study contributes to understanding risk dynamics in different domains—education, banking stability, and targeted lending programs—providing valuable insights for policymakers and financial institutions. The first two essays use Machine learning techniques and the third essay use fixed effect OLS methodology.  \nThe first paper, \"Forecasting Systemic Risk of Banks with Machine Learning,\"shifts the focus to financial stability. This study applies machine learning techniques to predict systemic risk in the banking sector, particularly through the lens of Value at Risk (VaR) and other risk metrics. By leveraging institutional and macroeconomic variables, the research enhances traditional risk assessment frameworks, demonstrating the predictive  \npower of machine learning in financial stability analysis.  \nThe second paper, \"Forecasting Loan Risk of Banks with Machine Learning in the Main Street Lending Program,\" extends the discussion to targeted government interventionsin financial markets. This study examines how machine learning can improve loan risk prediction for banks participating in the","cbCaikMt8TfKkhCQ","https://ap.wps.com/l/cbCaikMt8TfKkhCQ","pdf",2214416,1,101,"English","en",105,"# 1 Introduction\n# 2 Forecasting Loan Risk of Banks with Machine Learning in Main Street Lending Program\n## 2.1 Introduction\n## 2.2 Model & Methods\n## 2.2.1 Linear Regression\n## 2.2.2 Support Vector Machine\n## 2.2.3 Factorization Machine\n## 2.2.4 XGBoost\n## 2.2.5 Transformer\n## 2.3 Data\n## 2.3.1 Model estimation and hyperparameter optimization\n## 2.4 Result","[{\"question\":\"What is the central theme of the dissertation?\",\"answer\":\"The dissertation centers on predicting economic and financial risks using econometric and machine learning techniques, and studying how human capital develops through education.\"},{\"question\":\"How does the first essay forecast systemic risk in banks?\",\"answer\":\"It applies machine learning to predict systemic bank risk, using Value at Risk (VaR) and other risk metrics and combining institutional and macroeconomic variables.\"},{\"question\":\"What factors does the third essay analyze regarding children’s test scores?\",\"answer\":\"It examines demographic influences on human capital accumulation, focusing on how birth order and gender relate to children’s academic performance.\"}]","Essays on Machine Learning on Financial Economics, and Human Capital Development - 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