[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118826-en":3,"doc-seo-118826-105":30,"detail-sidebar-cat-0-en-105":83},{"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},118826,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Prerequisites for the Use of Machine Learning for Business Valuation","The paper investigates the fundamental theoretical prerequisites for applying machine learning to business valuation. It explains how statistical learning methods address key weaknesses in traditional valuation approaches, particularly the income approach and discounted cash flow. The study supports rejecting conventional econometric linear regression estimated by least squares, favoring more advanced nonparametric models. It further argues that ML broadens economists’ empirical tools, works with small datasets, manages complex asset-valuation problems, reduces false discoveries, and avoids reliance on Gauss-Markov assumptions while addressing the model interpretability “black box” challenge.","CENTRO UNIVERSITÁRIO CURITIBA-UNICURITIBA-VOLUME 6-NÚMERO 39/2023 I e-6267 I JAN-MARÇO I CURITIBA/PARANÁ/BRASIL-PÁGINAS 1 A 12- ISSN: 2316-2880  \nPREREQUISITES FOR THE USE OF MACHINE LEARNING FOR  \nBUSINESS VALUATION  \nPetr Koklev  \nSaint Petersburg State University – Russia  \n[https://orcid.org/0000-0003-2594-7973](https://orcid.org/0000-0003-2594-7973)[ ](https://orcid.org/0000-0003-2594-7973)[koklevp@gmail.com](koklevp@gmail.com)  \nABSTRACT  \nGoal: The paper examines the fundamental theoretical prerequisites for the use of machine learning in business valuation. Methods: The study demonstrates that the use of statistical methods addresses the shortcomings of traditional approaches to valuation, in particular, the income approach and the discounted cash flow method. Results: Substantiation is given for the rejection of traditional econometric methods (linear regression, estimated by the least squares method) in favor of more complex nonparametric statistical models. Conclusion: Machine learning expands the empirical toolkit of the economist, allows for small datasets, solves the problem of asset valuation complexity, protects against false discoveries, and does not require compliance with Gauss-Markov assumptions. The paper also addresses the black box problem – the difficulty of interpreting models derived from statistical learning.  \nKeywords: Valuation; Statistical learning; DCF; Relative valuation; Econometrics; Feature importance.  \nPRÉ-REQUISITOS PARA A UTILIZAÇÃO DA APRENDIZAGEM DEMÁQUINAS PARA A AVALIAÇÃO DE EMPRESAS  \nRESUMO  \nObjectivo: O documento examina os pré-requisitos teóricos fundamentais para autilização da aprendizagem de máquinas na avaliação de empresas. Métodos: O estudo demonstra que a utilização de métodos estatísticos aborda as deficiências dasabordagens tradicionais de avaliação, em particular, a abordagem do rendimento e ométodo do fluxo de caixa descontado. Resultados: É dada uma fundamentação para arejeição dos métodos econométricos tradicionais (regressão linear, estimada pelo método dos mínimos quadrados) em favor de modelos estatísticos não paramétricos mais complexos. Conclusão: A aprendizagem mecânica expande o conjunto de ferramentas empíricas do economista, permite pequenos conjuntos de dados, resolve o problema da complexidade da avaliação de activos, protege contra falsas descobertas, e não exige o cumprimento das suposições de Gauss-Markov. O documento também aborda oproblema da caixa negra - a dificuldade de interpretar modelos derivados daaprendizagem estatística.  \nPalavras-chave: Avaliação; Aprendizagem estatística; DCF; Avaliação relativa; Econometria; Importância das características.  \n1 INTRODUCTION  \nThe consideration of machine learning (ML) is largely motivated by the many fundamental shortcomings of the discounted cash flow (DCF) method. The key problem is considered to be subjectivity in the identification of the main input parameters of DCF models: the size of expected cash flows, the discount rate, and the growth rate of cash flows. In other words, any value of the parameter can be justified by the relevant literature. As a result, the use of DCF cannot deliver unbiased business valuations (Kovalev & Koklev, 2022) .  \nUnderstanding the key characteristics of ML allows us to realize the purpose of this paper – to argue for the use of ML for the problem of valuation.  \nFirst, it is necessary to clarify the terminology. The concept of ML (hereinafter, to avoid monotony, the following will be used as synonyms for ML: \"statistical learning\",\"nonparametric statistical methods\", and \"data mining\") denotes the following (Gu et al. , 2020):  \n1. The use of a variety of usually nonparametric statistical forecasting methods , capable of incorporating nonlinear relationships between independent variables and interaction effects and approximating process functions of any type and complexity.  \n2. Application of regularization – a technique that allows punishing complex models to preven","cbCaiujUOTUquaej","https://ap.wps.com/l/cbCaiujUOTUquaej","pdf",529427,1,12,"English","en",105,"# Introduction\n## Motivation: limitations of discounted cash flow\n## Terminology and definition of machine learning\n## Modeling aspects: regularization, hyperparameter tuning, and cross-validation","[{\"question\":\"What is the “black box” problem in the context of this paper, and how is it addressed?\",\"answer\":\"The black box problem refers to the difficulty of interpreting statistical learning models. The paper notes this issue as part of the prerequisites discussion when using ML for valuation.\"}]","Prerequisites for the Use of Machine Learning for Business Valuation | PDF",1785720485,30,{"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":78,"head_meta":80,"extra_data":82,"updated_unix":28},"prerequisites-for-the-use-of-machine-learning-for-business-valuation","",{"@graph":36,"@context":77},[37,54,68],{"@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/prerequisites-for-the-use-of-machine-learning-for-business-valuation/118826/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71],{"name":72,"@type":73,"acceptedAnswer":74},"What is the “black box” problem in the context of this paper, and how is it addressed?","Question",{"text":75,"@type":76},"The black box problem refers to the difficulty of interpreting statistical learning models. 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