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Supervised by Prof. Dr. Tom Zimmermann with Prof. Dr. Dieter Hess as co-supervisor, it reports methods, estimation, interpretation, and evaluation results relevant for forecasting, portfolio decision-making, and return predictability.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/three-essays-on-machine-learning-in-empirical-finance-inauguraldissertation/290420/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/three-essays-on-machine-learning-in-empirical-finance-inauguraldissertation/290420.png","ImageObject",300,407,{"name":42,"@type":43},"Eliana","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-10-29","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":55},"InteractionCounter",{"@type":54},"ViewAction",9,{"@type":57,"mainEntity":58},"FAQPage",[59,65,69],{"name":60,"@type":61,"acceptedAnswer":62},"What is the document’s purpose and academic context?","Question",{"text":63,"@type":64},"It is an inaugural dissertation submitted for the PhD degree in Economics and Social Sciences at the University of Cologne. 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Sebastian Weibels  \naus  \nOberhausen  \nReferent: Prof. Dr. Tom Zimmermann  \nKorreferent: Prof. Dr. Dieter Hess  \nTag der Promotion: 27.04.2026  \nAcknowledgements  \nThis thesis marks the completion of my doctoral studies in the Department of Econometrics and Statistics and the Department of Finance at the University of Cologne.  \nFirst and foremost, I would like to express my deepest gratitude to my supervisor, Prof. Dr. Tom Zimmermann, for his continuous support, guidance, and advice throughout my doctoral journey. His expertise has been instrumental not only in shaping my research but also in influencing my personal development. I am also indebted to my co-supervisor, Prof. Dr. Dieter Hess, for his mentorship and encouragement. In addition, I am sincerely grateful to Prof. Dr. Hartmann-Wendels for his supervision during the early stages of my doctoral studies.  \nI am also deeply grateful to my co-authors, Dr. Frederik Simon, Prof. Dr. Dieter Hess, and Prof. Dr. Tom Zimmermann, for their collaboration, dedication, and invaluable contributions to our research. I have greatly benefited from their input and expertise.  \nMoreover, I am thankful to my colleagues in the Department of Econometrics and Statistics, the Department of Finance, and the Centre for Financial Research at the University of Cologne for their support and for the many stimulating discussions we shared. It was a wonderful time that will always remain a special memory for me.  \nI would particularly like to thank my friends, who supported me throughout this journey and whose friendship and sense of humor brought me joy and balance beyond academia, providing welcome distractions from the demands of my research.  \nLastly, I would like to thank my family and loved ones for their unwavering encouragement and the opportunities they have given me. Your support throughout all stages of my life has been a constant source of strength. Without a doubt, I would not have achieved this without you. For this, and for so much more, I am endlessly grateful.  \nSebastian Weibels  \nContents  \nAcknowledgements I  \nList of Tables VIII  \nList of Figures X  \n1 Introduction 1  \n2 Deep Parametric Portfolio Policies 7  \n2.1 Introduction .................................... 7  \n2.2 Theory ....................................... 12  \n2.2.1 Expected utility framework and parametric portfolio policies ... 12  \n2.2.2 Risk aversion as economic regularization ............... 14  \n2.3 Estimation and results .............................. 19  \n2.3.1 Network architecture ........................... 19  \n2.3.2 Data .................................... 23  \n2.3.3 Performance results for CRRA investors ............... 24  \n2.3.4 Supporting results ............................ 28  \n2.3.5 Robustness ................................ 31  \n2.3.6 Market frictions .............................. 33  \n2.4 Alternative investor utility functions ...................... 36  \n2.4.1 Mean-variance and loss aversion .................... 36  \n2.4.2 Portfolio moments across utility functions and models ....... 39  \n2.5 Conclusion ..................................... 41  \n3 Interpretable Machine Learning for Earnings Forecasts: Leveraging HighDimensional Financial Statement Data 43  \n3.1 Introduction .................................... 43  \n3.2 Empirical approach ................................ 51  \n3.2.1 General setup ............................... 51  \n3.2.2 Data .................................... 52  \n3.2.3 Models ................................... 53  \n3.2.4 Out-of-sample approach ......................... 54  \n3.2.5 Evaluation ................................. 55  \n3.2.6 Interpretation ............................... 56  \n3.3 Evaluation ..................................... 58  \n3.3.1 Accur","cbCaia7m6GBmds2C","https://ap.wps.com/l/cbCaia7m6GBmds2C","pdf",4814979,249,"English","# Acknowledgements\n# List of Tables\n# List of Figures\n# 1 Introduction\n# 2 Deep Parametric Portfolio Policies\n## 2.1 Introduction\n## 2.2 Theory\n## 2.3 Estimation and results\n## 2.4 Alternative investor utility functions\n## 2.5 Conclusion\n# 3 Interpretable Machine Learning for Earnings Forecasts: Leveraging HighDimensional Financial Statement Data\n## 3.1 Introduction\n## 3.2 Empirical approach\n## 3.3 Evaluation\n## 3.4 Interpretation\n## 3.5 Conclusion\n# 4 Hard to Process: Atypical Firms and the Cross-Section of Expected Stock Returns\n## 4.1 Introduction\n## 4.2 Constructing a measure of firm atypicality\n## 4.3 Data and model\n## 4.4 ATYP as a measure of information-processing difficulty\n## 4.5 Atypical firms and the cross-section of expected returns\n## 4.6 Channels of return predictability\n## 4.7 Robustness checks","[{\"question\":\"What is the document’s purpose and academic context?\",\"answer\":\"It is an inaugural dissertation submitted for the PhD degree in Economics and Social Sciences at the University of Cologne. It completes the author’s doctoral studies and is dedicated to a research contribution in econometrics, statistics, and finance.\"},{\"question\":\"What are the three main research essays covered in the thesis?\",\"answer\":\"The thesis includes: deep parametric portfolio policies, interpretable machine learning for earnings forecasts using high-dimensional financial statement data, and the study of atypical firms in relation to expected stock returns.\"},{\"question\":\"How does the thesis evaluate and interpret its machine-learning approaches?\",\"answer\":\"It describes empirical approaches and evaluation procedures such as accuracy and bias, out-of-sample performance measures, implied cost of capital, and methods for interpretation including variable importance, group importance, and nonlinearity.\"}]","Three Essays on Machine Learning in Empirical Finance - Inauguraldissertation | PDF",627]