[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125084-en":3,"doc-seo-125084-105":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},125084,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Can central bankers’ talk predict bank stock returns? A machine learning approach","The paper combines machine learning with textual analysis to forecast bank stock returns using language derived from Federal Open Market Committee (FOMC) press materials, with textual features extracted from the Chair’s Q&A responses. The study compares machine learning models against OLS regressions and evaluates out-of-sample predictive performance. Results show machine learning yields more accurate predictions, while the best performance comes from jointly training on financial variables and textual data. Trading portfolios built from the top model’s predictions consistently outperform benchmarks, extending limited evidence on bank return predictability and informing investors’ strategy design.","Munich Personal RePEc Archive  \nCan central bankers’ talk predict bank stock returns? A machine learning approach  \nKatsafados, Apostolos G. and Leledakis, George N. and Panagiotou, Nikolaos P. and Pyrgiotakis, Emmanouil G.  \nDepartment of Accounting and Finance, School of Business, Athens University of Economics and Business, Greece, Department of Accounting and Finance, School of Business, Athens University of Economics and Business, Greece, Department of Accounting and Finance, School of Business, Athens University of Economics and Business, Greece, Essex Business School, University of Essex, U.K.  \nOctober 2024  \nOnline at [https://mpra. ub. uni-muenchen. de/122899/](https://mpra. ub. uni-muenchen. de/122899/)  \n[MPRA Paper No. 122899](MPRA Paper No. 122899) , [posted 10 Dec 2024 14:26 UTC](posted 10 Dec 2024 14:26 UTC)  \nCan central bankers’ talk predict bank stock returns? A machine learning approach  \nby  \nApostolos G. Katsafados1, George N. Leledakis1*, Nikolaos P. Panagiotou1,  \nEmmanouil G. Pyrgiotakis2  \n1 Department of Accounting and Finance, School of Business, Athens University of Economics and Business, Greece  \n2 Essex Business School, University of Essex, U.K.  \nAbstract  \nWe combine machine learning algorithms (ML) with textual analysis techniques to forecast bank stock returns. Our textual features are derived from press releases of the Federal Open Market Committee (FOMC) . We show that ML models produce more accurate out-of-sample predictions than OLS regressions, and that textual features can be more informative inputs than traditional financial variables. However, we achieve the highest predictive accuracy by training ML models on a combination of both financial variables and textual data. Importantly, portfolios constructed using the predictions of our best performing ML model consistently outperform their benchmarks. Our findings add to the scarce literature on bank return predictability and have important implications for investors.  \nJEL classification: C63, E58, G17, G21, G40  \nKeywords: Bank stock prediction; Trading strategies; Machine learning; Press conferences; Natural language processing; Banks  \nThis version: November, 2024  \n*Corresponding author: Department of Accounting and Finance, School of Business, Athens University of Economics and Business, 76 Patission Str., 104 34, Athens, Greece; Tel.: +30 210 8203459. E-mail addresses:  \n[katsafados@aueb.gr](katsafados@aueb.gr) (A. Katsafados), [gleledak@aueb.gr](gleledak@aueb.gr) (G. Leledakis), [panagiotou@aueb.gr](panagiotou@aueb.gr) (N. Panagiotou),  \n[e.pyrgiotakis@essex.ac.uk](e.pyrgiotakis@essex.ac.uk) (E. Pyrgiotakis). George Leledakis greatly acknowledges financial support received from the Research Center of the Athens University of Economics and Business (EP-3744-01) . All remaining errors and omissions are our own.  \n1. Introduction  \nForecasting equity returns is a thoroughly examined topic in the finance literature with studies focusing either on market returns (Brock et al., 1992; Campbell and Thompson, 2008; Neely et al., 2014) or individual stock returns (Lee and Swaminathan, 2000; Jegadeesh and Titman, 2002; Boudoukh et al., 2007) . A key takeaway from this literature is that forecasting stock returns is particularly challenging, albeit not impossible (Rapach and Zhou, 2013) . Unsurprisingly, the focus of equity forecasting literature has predominantly been on nonfinancial sectors, as banks are often excluded due to their unique characteristics (e.g. high leverage and heavy regulation) . At the same time, there is a plethora of studies that examine the determinants of bank-stock returns (Baek and Bilson, 2015; Carmichael and Coën 2018; Venmans, 2021) . This literature, however, typically focuses on in-sample statistics which are not widely regarded as reliable indicators of a model’s forecasting ability (Bossaerts and Hillion, 1999) . Consequently, to date, evidence of out-of-sample predictability of bank stock returns is rather","cbCaieFOScoK3Y3j","https://ap.wps.com/l/cbCaieFOScoK3Y3j","pdf",755182,1,44,"English","en",105,"# Introduction\n## Machine learning and textual analysis for return forecasting\n## Data and feature construction","[{\"question\":\"What data source is used to create the textual features for the predictions?\",\"answer\":\"Textual features are derived from Federal Open Market Committee (FOMC) conference calls, specifically from the Chair’s answers throughout the Q\\u0026A section.\"},{\"question\":\"How do machine learning models compare with OLS regressions in forecasting performance?\",\"answer\":\"Machine learning models provide more accurate out-of-sample predictions than OLS regressions.\"},{\"question\":\"What portfolio impact do the best-performing models achieve?\",\"answer\":\"Portfolios constructed using predictions from the best machine learning model consistently outperform their benchmarks.\"}]","Can central bankers’ talk predict bank stock returns? 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