[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127531-en":3,"doc-seo-127531-105":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},127531,687207017582,"Himbo","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Essays in Corporate Finance with Machine Learning Techniques - PhD dissertation","This PhD dissertation presents three corporate finance (entrepreneurial finance) studies using machine learning techniques to evaluate venture capital and investment outcomes in the U.K. It estimates the causal impact of the Venture Capital Trust (VCT) scheme on investee investment, using matrix completion to construct counterfactuals and report an ATT of 41%. It then examines whether VCT skills and deal structure, or luck, drive the success of VCT-backed firms via deep neural network models and attribution methods. Finally, it extends the analysis to the broader U.K. VC industry and identifies key skill determinants for success, including VC specialization.","Essays in Corporate Finance with Machine Learning Techniques  \nDennis Iweze  \nSchool of Economics and Finance  \nQueen Mary University of London  \nThis dissertation is submitted in partial fulfilment of the requirements for the Degree of Doctor of Philosophy (PhD) in Finance  \nMarch 2023  \nStatement of Originality  \nI, Dennis Ezimechine Iweze, confirm that the research included within this thesis is my own work or that where it has been carried out in collaboration with, or supported by others, that this is duly acknowledged below and my contribution indicated. Previously published material is also acknowledged below.  \nI attest that I have exercised reasonable care to ensure that the work is original, and does not to the best of my knowledge break any UK law, infringe any third party’s copyright or other Intellectual Property Right, or contain any confidential material.  \nI accept that the College has the right to use plagiarism detection software to check the electronic version of the thesis.  \nI confirm that this thesis has not been previously submitted for the award of a degree by this or any other university.  \nThe copyright of this thesis rests with the author and no quotation from it or information derived from it may be published without the prior written consent of the author.  \nDennis Iweze March 2023  \nAcknowledgements  \nThis PhD thesis represents another stage in my intellectual journey. In the course of producing it, I leaned on the generosity and support of several people. First, I want to acknowledge my PhD supervisors: Professor Jason Sturgess and Professor Haroon Mumtaz. In particular, Professor Jason Sturgess, thank you for inspiring the focus of my research and reading/refining drafts of the evolving thesis, from the very start of my PhD to its completion. You trained me into a financial economist and also looked out for my well-being in more ways than you ever let on. I will be forever grateful.  \nThis thesis is about my intellectual journey, so it is also for my family. My gratitude must start with my parents, iya Bukky and Papa chichi, you both showed me the way. I am you. Eyin Omo Iya mi, we are together forever. I appreciate you all: Sis Bukky, Brother Ope, Chichi, Adesua and Yvonne for your encouragement. I am grateful to my children Adanna and Chidiebele, you unwittingly sacrificed so much and are the motivation behind everything I do. Titi, it is no exaggeration to say that this thesis would have been impossible without you. I will spend my life thanking you.  \nAbstract  \nThis thesis comprises three corporate finance (entrepreneurial finance) studies with machine learning techniques.  \nIn chapter one, I estimate the causal effect of the Venture Capital Trust (VCT) scheme on investment (change in total-asset formation) for investees in the U.K. To that end, I handcollect data on all firms that received VCT funding (investees) from inception of the scheme till 2018 . Thereafter, I adapt an unsupervised machine learning algorithm called matrix completion to estimate causal effects in settings where some investee-years are exposed toa binary treatment (VCT funding) and the goal is to estimate counterfactual outcomes for the investee-years combinations. In tandem with the hand-collected data, I use the matrix completion algorithm to estimate the causal effect of the Venture Capital Trust (VCT) scheme on the investment of investees. The estimand is the Average Treatment Effect on the Treated (ATT) . I find that the VCT scheme caused a 41% increase in the investment of investees; the ATT is 41% . I also document novel insights regarding the relationship between changes to the U.K. government VCT policy, VCT fundraising and the aggregate investment of VCTs. Finally, I show that the matrix completion estimator outperforms an unconfoundedness-based estimator and alleviates the potential selection bias issue inherent in a causal study like this study.  \nIn chapter two, I begin by highlighting the importance of V","cbCaifTY2HN5Tb5M","https://ap.wps.com/l/cbCaifTY2HN5Tb5M","pdf",2204456,1,190,"English","en",105,"# List of Figures\n# List of Tables\n# Introduction\n# 1 A Matrix Completion Approach to Policy Evaluation: Evaluating the Impact of the VCT Scheme on Investment in the U.K.\n## 1.1 Introduction","[{\"question\":\"What is the main research focus of the dissertation?\",\"answer\":\"The dissertation studies corporate finance questions, especially how venture capital and the U.K. Venture Capital Trust (VCT) scheme affect investment and the success of VCT-backed firms, using machine learning techniques.\"},{\"question\":\"How does the thesis estimate the causal effect of the VCT scheme in Chapter 1?\",\"answer\":\"It constructs counterfactual outcomes for investee-years using an unsupervised matrix completion algorithm, targeting the Average Treatment Effect on the Treated (ATT).\"},{\"question\":\"Which factors are found to determine the success of VCT-backed firms in later chapters?\",\"answer\":\"The thesis finds that VCT skills and the funding deal structure are significant determinants of success, with prior high financial performance highlighted as an important skill driver. It further shows that VC specialization in the FTSE-industry of invested firms is the most important skill determinant in the extended industry analysis.\"}]","Essays in Corporate Finance with Machine Learning Techniques - PhD dissertation | PDF",1785939791,479,{"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},"essays-in-corporate-finance-with-machine-learning-techniques-phd-dissertation","",{"@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/essays-in-corporate-finance-with-machine-learning-techniques-phd-dissertation/127531/",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","2026-08-22","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 is the main research focus of the dissertation?","Question",{"text":76,"@type":77},"The dissertation studies corporate finance questions, especially how venture capital and the U.K. Venture Capital Trust (VCT) scheme affect investment and the success of VCT-backed firms, using machine learning techniques.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How does the thesis estimate the causal effect of the VCT scheme in Chapter 1?",{"text":81,"@type":77},"It constructs counterfactual outcomes for investee-years using an unsupervised matrix completion algorithm, targeting the Average Treatment Effect on the Treated (ATT).",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors are found to determine the success of VCT-backed firms in later chapters?",{"text":85,"@type":77},"The thesis finds that VCT skills and the funding deal structure are significant determinants of success, with prior high financial performance highlighted as an important skill driver. 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