[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117263-en":3,"doc-seo-117263-105":29,"detail-sidebar-cat-0-en-105":94},{"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":13,"seo_description":14,"update_tm":27,"read_time":28},117263,8796095462418,"Noah","https://ap-avatar.wpscdn.com/avatar/80000253c1241d02b47?x-image-process=image/resize,m_fixed,w_180,h_180&k=1778826106357471780",8,"Research & Report","The Profit Rate-Interest Rate Nexus: Evidence from Machine Learning Algorithms","This study examines potential predictors of profit rates and deposit rates and tests whether these rates are driven by identical factors in participation banking and deposit banking contexts. Empirical results based on tree-based machine learning methods, including boosting, bagging, and random forest, indicate that participation banks’ profit rates are more strongly linked to industrial production and real economic activity. In contrast, domestic and global interest rates show substantial influence on deposit banks’ rate setting. The paper adds methodological and predictive insights for data-rich analysis.","TUJISE  \nTurkish Journal of Islamic Economics  \nThe Profit Rate-Interest Rate Nexus Evidence from Machine Learning Algorithms  \nMehmet Yeşilyaprak Ali Polat  \nÖnder Özgür Süleyman Şahal  \nAbstract: The main purpose of this study is to examine potential predictors of profit rates and deposit rates and to examine whether these rates are affected by identical factors. This paper empirically addresses tree-based machine learning algorithms (e.g., boosting, bagging, random forest) . The empirical findings of the study demonstrate participation banks’ profit rates to be more influenced by industrial production due to these banks being in contact more with real economic activity. As expected, however, domestic and global interest rates appear to have great significance in how deposit banks set their rates. This study contributes to the literature in two ways. First, it determines the potential predictors of profit rates and deposit rates in a data-rich environment. Second, the study uses random forest, bagging, and boosting algorithms as methodological tools and benefits from the apparent advantages these algorithms have empirically.  \nKeywords: Profit, interest, participation banking, Islamic finance, bank, usury.  \nJEL Classification: E43, G21  \nAssoc. Prof., Beykoz University, [mehmetyesilyaprak@beykoz.edu.tr](mehmetyesilyaprak@beykoz.edu.tr),  0000-0001-8334-5191 Assoc. Prof., Ankara Yildirim Beyazit University, [apolat@hotmail.com](apolat@hotmail.com),  0000-0001-6041-5003 Assist. Prof., Ankara Yildirim Beyazit University, [oozgur@ybu.edu.tr](oozgur@ybu.edu.tr),  0000-0001-5221-4842 Manager of Insurance Analytics, Turk Eximbank, [suleymansahal@gmail.com](suleymansahal@gmail.com),  0000-0003-4179-8998  \nYeşiltaprak, M., Polat, A., Özgür, Ö . & Şahal, S. (2023) . The profit rate-interest rate nexus: Evidence from machine learning algorithms. Turkish Journal of Islamic Economics, 10(1), 135-161.  \n© Research Center for Islamic Economics DOI: 10.26414/A3704  \nTUJISE, 10( 1), 2023, 135-161  \n[tujise.org](tujise.org)  \nSubmitted: 24.12.2021  \nRevised: 11.03.2022  \nAccepted: 28.03.2022  \nOnline First: 18.04.2022  \nTurkish Journal of Islamic Economics (TUJISE)  \nIntroduction  \nThe global pace of Islamic finance (IF) in the second half of the 21st century has not only brought a new framework for financing but also opened a theoretical scope for discussing unconventional financing methods. These theoretical discussions have shown themself in different strands of the literature, one developing strand being related to the similarities or causality between Islamic and conventional financial institutions regarding profit shares and interest rates on deposits in financial markets containing both conventional and Islamic financial institutions (IFIs) .  \nThe total global IF assets reached $2.88 trillion USD in 2019, with70% being held in Islamic Banks and 19% being held in Sukuks (Islamic Corporation for the Development of the Private Sector [ICD], 2020) . Therefore, when talking about IF, one should actually bear in mind that Islamic Banking (IB) and Sukuk-related developments are being talked about rather than other IFIs, Islamic funds, or takafuls. ICD’s report also forecasted that global Islamic financial assets to be expected to reach $3.69 trillion USD.  \nEid andAsutay (2019) indicated IF tobe the fastest growing sector in the global financial industry with a broad and sophisticated product and service range being put forward for the financial and theological needs of more than 1.5 billion Muslims. However, much research has indicated customer satisfaction to be higher among non-Muslim customers (Amin et al., 2011) who use IB services and service quality in IBs tobe fundamental (Awan & Bukhari, 2011) for IB customers. Therefore, IF is attractive to everyone irrespective of religion, although the underlying reasons for providing and choosing IB services vary.  \nThe basic business model for IBs is to collect funds from real and legal person","cbCaid9sgetX5zba","https://ap.wps.com/l/cbCaid9sgetX5zba","pdf",2368519,1,27,"English","en",105,"# Introduction\n## Islamic finance growth and market scope\n## Business model and Sharia-related distinctions\n## Research focus: profit rate and deposit rate drivers","[{\"question\":\"What is the main purpose of the study?\",\"answer\":\"To examine predictors of profit rates and deposit rates and to assess whether these rates are affected by the same factors.\"},{\"question\":\"Which machine learning algorithms are used?\",\"answer\":\"The study applies tree-based methods, including boosting, bagging, and random forest.\"},{\"question\":\"What factors most influence participation banks’ profit rates?\",\"answer\":\"Industrial production and links to real economic activity show stronger influence on participation banks’ profit rates.\"},{\"question\":\"How do interest rates relate to deposit banks’ rate setting?\",\"answer\":\"Domestic and global interest rates appear to be highly significant in how deposit banks set their deposit rates.\"}]",1785674856,68,{"code":4,"msg":30,"data":31},"ok",{"site_id":24,"language":23,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":89,"head_meta":91,"extra_data":93,"updated_unix":27},"the-profit-rate-interest-rate-nexus-evidence-from-machine-learning-algorithms","",{"@graph":35,"@context":88},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/the-profit-rate-interest-rate-nexus-evidence-from-machine-learning-algorithms/117263/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":23,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":64,"interactionType":65,"userInteractionCount":4},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80,84],{"name":71,"@type":72,"acceptedAnswer":73},"What 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