[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125200-en":3,"doc-seo-125200-105":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},125200,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",8,"Research & Report","Political Connections and Investment Inefficiency - A Machine Learning Approach - Research study","This study examines how political connections affect corporate investment inefficiency, emphasizing the governance function of former politicians serving on the board of commissioners within a two-tier board structure. Advanced machine learning methods are applied to data from publicly listed Indonesian firms, treated as an empirical laboratory. Results show that former politicians on the board significantly reduce real investment inefficiency. Random Forest provides the strongest estimation performance, closely followed by Bagging, strengthening evidence on governance and oversight effects.","Political Connections and Investment Inefficiency: A Machine Learning Approach  \nHarianto, S., Guney, Y., Khalil, M. & Andrikopoulos, P.  \nPublished PDF deposited in Coventry University’s Repository  \nOriginal citation:  \nHarianto, S, Guney, Y, Khalil, M & Andrikopoulos, P 2025, 'Political Connections and Investment Inefficiency: A Machine Learning Approach', The European Journal of Finance, vol. (In-Press), pp. (In-Press) .  \n[https://doi.org/10.1080/1351847X.2025.2513505](https://doi.org/10.1080/1351847X.2025.2513505)  \n[DOI 10.1080/1351847X.2025.2513505](DOI 10.1080/1351847X.2025.2513505)[ ](DOI 10.1080/1351847X.2025.2513505)[ISSN 1351-847X](ISSN 1351-847X)  \n[ESSN 1466-4364](ESSN 1466-4364)  \nPublisher: Taylor and Francis Group Routledge  \n© 2025 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.  \nThis is an Open Access article distributed under the terms of the Creative Commons Attribution License ([http://creativecommons.org/licenses/by/4.0/](http://creativecommons.org/licenses/by/4.0/)), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s) or with their consent.  \nTHE EUROPEAN JOURNAL OF FINANCE  \n[https://doi.org/10.1080/1351847X.2025.2513505](https://doi.org/10.1080/1351847X.2025.2513505)  \nPolitical connections and investment inefficiency: a machine learning approach  \nSandy Harianto a, Yilmaz Guney b, Mohamed Khalil c, d and Panagiotis Andrikopoulos baPrasetiya Mulya Business School, Universitas Prasetiya Mulya, Jakarta, Indonesia;b Centre for Financial and Corporate Integrity, Coventry University, Coventry, UK; c Hull University Business School, University of Hull, Hull, UK;d Faculty of Commerce, Tanta  \nUniversity, Tanta, Egypt  \nABSTRACT  \nThis study examined the impact of political connections on corporate investment inefficiency, focusing on the governance role of former politicians serving on the board of commissioners within a two-tier board structure. Various advanced machine learning algorithms were employed to analyse data from publicly listed Indonesian firms (a laboratory in this context), the results of which indicate that the presence of former politicians on a company’s board of commissioners significantly mitigates real investment inefficiency concerns. The Random Forest algorithm emerged as the best estimator, demonstrating the lowest root relative squared error level, closely followed by the Bootstrap aggregation (Bagging) technique. These findings significantly advance empirical aspects of the literature on political connections, highlighting the potential of former politicians on a two-tier board to effectively mitigate investment inefficiency. This study provides robust evidence of the advantages that former politicians bring to supervisory boards in Indonesia. The results provide valuable insights for regulators and capital market authorities and, moreover, have the potential to reshape the academic discourse on political connections and investment inefficiency.  \nARTICLE HISTORY  \nReceived 26 December 2024 Accepted 25 May 2025  \nKEYWORDS  \nPolitical connections; investment inefficiency; Indonesia; machine learning algorithms; random forest; bagging; neural networks  \nJEL CLASSIFICATIONS  \nG30; C45; C60; C61  \n1. Introduction  \nPolitical figures often gain access to records and intelligence that are not always available to the public, including data on potential business opportunities, market trends, and government regulations. For firms with politicians serving on their boards, such benefits can provide them with advantages over their competitors (Agrawal and Knoeber 2001; Schoenherr 2019), better access to capital markets (Bussolo et al. 2022; Liu, Uchida, and Gao 2012; Shen and Lin 2016), natural resources (Hou, Hu, and Yuan 2017), and a higher likelihood of receiv","cbCaiojOkNdv3aCl","https://ap.wps.com/l/cbCaiojOkNdv3aCl","pdf",3201694,1,32,"English","en",105,"# Abstract\n# Introduction\n## Political ties and firm advantages\n## Risks: corruption and regulatory capture\n# Article methods and evidence (machine learning approach)","[{\"question\":\"What governance role do former politicians on the board of commissioners play in this study?\",\"answer\":\"The study focuses on how former politicians serving on the board of commissioners within a two-tier board structure mitigate corporate investment inefficiency concerns.\"},{\"question\":\"How is machine learning used to analyze investment inefficiency?\",\"answer\":\"Multiple advanced machine learning algorithms are applied to data from publicly listed Indonesian firms, and the models are evaluated to identify the best estimator.\"},{\"question\":\"Which algorithm performs best for predicting or estimating investment inefficiency, and what are the results?\",\"answer\":\"Random Forest emerges as the best estimator with the lowest root relative squared error level, closely followed by Bagging; overall, political connections significantly reduce investment inefficiency.\"}]","Political Connections and Investment Inefficiency - A Machine Learning Approach - Research study | PDF",1785897366,81,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"political-connections-and-investment-inefficiency-a-machine-learning-approach-research-study","",{"@graph":36,"@context":85},[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/political-connections-and-investment-inefficiency-a-machine-learning-approach-research-study/125200/",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","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What governance role do former politicians on the board of commissioners play in this study?","Question",{"text":75,"@type":76},"The study focuses on how former politicians serving on the board of commissioners within a two-tier board structure mitigate corporate investment inefficiency concerns.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is machine learning used to analyze investment inefficiency?",{"text":80,"@type":76},"Multiple advanced machine learning algorithms are applied to data from publicly listed Indonesian firms, and the models are evaluated to identify the best estimator.",{"name":82,"@type":73,"acceptedAnswer":83},"Which algorithm performs best for predicting or estimating investment inefficiency, and what are the results?",{"text":84,"@type":76},"Random Forest emerges as the best estimator with the lowest root relative squared error level, closely followed by Bagging; 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