[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124368-en":3,"doc-seo-124368-105":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},124368,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Defining the Determinants of Corporate Financial Performance - A Machine Learning Approach","This study investigates the determinants of corporate financial performance (CFP) among Russian enterprises from 2012–2023 under geopolitical disruptions, using ensemble machine learning to fill gaps in modeling non-linear institutional interactions. Based on data from 25 large non-financial firms, it applies 75%/25% train-test splits and 10-fold cross-validation to reduce overfitting. Results show industry effects weakening after sanctions, while organizational size relates nonlinearly to CFP. CSR and R&D effects also decline post-2022, and SHAP clarifies threshold effects. The findings highlight fragility of intangible assets and support adaptive resource allocation for resilience.","Defining the Determinants of Corporate Financial Performance:  \nA Machine Learning Approach  \nIdelia R. Badykova 1* , Aliya A. Dinmukhametova 1  \n1 Department of Business Statistics and Economics, Kazan National Research Technological University, Kazan, Russia.  \nAbstract  \nThis study investigates the determinants of corporate financial performance (CFP) among Russian enterprises (2012–2023) through the lens of geopolitical disruptions, employing ensemble machine learning (ML) to address methodological gaps in modeling non-linear institutional interactions. Using data from 25 large non-financial firms, we analyze sectoral, organizational, and strategic drivers, integrating train-test splits (75%/25%) and 10-fold cross-validation to mitigate overfitting. Results reveal that industry affiliation, initially dominant (28% explanatory power pre-2022), declined sharply post-sanctions (15%), reflecting vulnerabilities in globally integrated sectors like manufacturing and extractives. Organizational size exhibited a nonlinear relationship with CFP, favoring comparatively smaller firms’ agility over larger enterprises’ rigidity, consistent with transaction cost economics. Strategic investments in corporate social responsibility (CSR) and research and development (R&D) diminished post-2022 as firms prioritized liquidity and operational stability, aligning with resource-based view principles. Methodologically, Shapley Additive Explanations (SHAP) clarified threshold effects in CSR returns and innovation’s reduced role under sanctions. The study innovates by applying ensemble machine learning to sanctionaffected emerging markets, challenging linear econometric assumptions and advancing institutional theory through a crisis-contextualized framework of resource dependence and stakeholder salience. Findings underscore the fragility of intangible assets under systemic shocks and advocate adaptive resource allocation frameworks to balance short-term survival with long-term resilience. This work provides policymakers and managers actionable insights for fostering operational agility and strategic foresight in volatile institutional environments.  \nKeywords:  \nCorporate Financial Effectiveness;  \nEnsemble Machine Learning;  \nGeopolitical Risk;  \nSanctions; Resource Dependence Theory; Resource-Based View (RBV);  \nShapley Additive Explanations (SHAP); Russian Enterprises;  \nEmerging Markets.  \nArticle History:  \nReceived: 25  \nRevised: 10  \nAccepted: 17  \nPublished: 01  \nApril July  \nJuly August  \n2025  \n2025  \n2025  \n2025  \n1-Introduction  \nCorporate Financial Performance (CFP) remains a pivotal indicator of organizational resilience, synthesizing operational efficiency, governance quality, and macroeconomic adaptability [1 , 2] . Grounded in foundational principles of financial analysis [3] and capital structure theory [4], CFP evaluation has evolved to incorporate dynamic capabilities [5] and institutional transitions [6] . While existing studies have mapped key CFP determinants using traditional econometric methods [7 , 8], including capital structure dynamics [9], the application of advanced computational techniques – particularly ensemble machine learning (ML) – to dissect complex, non-linear relationships in crisis contexts remains nascent [10 , 11] . Recent sector-specific applications, such as Yıldırım et al.(2024) [11], demonstrate ML’s efficacy in ranking financial performance determinants within energy sectors, highlighting the method’s potential for domain-specific insights. Theoretical frameworks such as agency theory [12] and stakeholder theory [13 , 14] underscore the multifaceted nature of CFP determinants, with evolutionary perspectives highlighting adaptive processes [15] .  \n* [CONTACT](CONTACT: badykovair@corp.knrtu.ru)[: badykovair@corp.knrtu.ru](CONTACT: badykovair@corp.knrtu.ru)  \nDOI: [http://dx.doi.org/10.28991/ESJ-2025-09-04-01](http://dx.doi.org/10.28991/ESJ-2025-09-04-01)  \n© 2025 by the authors. Licensee ESJ, Italy. 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