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SHAP values are used for interpretability, highlighting key variables and explaining model behavior, while sector analyses reveal distinct feature impacts and performance differences across industries.",{"@graph":14,"@context":72},[15,34,55],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & Report",3,{"item":32,"name":10,"@type":21,"position":33},"https://docshare.wps.com/document/sector-specific-financial-forecasting-with-machine-learning-algorithm-and-shap-interaction-values-research-paper/290646/",4,{"url":32,"name":10,"@type":35,"image":36,"author":41,"headline":10,"publisher":44,"fileFormat":47,"inLanguage":8,"description":12,"dateModified":48,"datePublished":49,"encodingFormat":47,"isAccessibleForFree":50,"interactionStatistic":51},"DigitalDocument",{"url":37,"@type":38,"width":39,"height":40},"https://docshare.wps.com/thumbnails/sector-specific-financial-forecasting-with-machine-learning-algorithm-and-shap-interaction-values-research-paper/290646.png","ImageObject",300,407,{"name":42,"@type":43},"Levi","Person",{"url":19,"name":45,"@type":46},"DocShare","Organization","application/pdf","2026-09-18","2026-09-17",true,{"@type":52,"interactionType":53,"userInteractionCount":26},"InteractionCounter",{"@type":54},"ViewAction",{"@type":56,"mainEntity":57},"FAQPage",[58,64,68],{"name":59,"@type":60,"acceptedAnswer":61},"Which machine learning models are evaluated in the study?","Question",{"text":62,"@type":63},"The study evaluates Linear Regression, Ridge, Lasso, Decision Tree, Bagging, Random Forest, AdaBoost, Gradient Boosting (GBM), LightGBM, and XGBoost.","Answer",{"name":65,"@type":60,"acceptedAnswer":66},"Why is SHAP used in the financial forecasting approach?",{"text":67,"@type":63},"SHAP (SHapley Additive exPlanations) values are used to identify the most influential variables affecting predictions and to explain model behavior.",{"name":69,"@type":60,"acceptedAnswer":70},"What main conclusion does the research draw about model performance?",{"text":71,"@type":63},"Gradient Boosting performs best, and ensemble methods generally deliver better accuracy and stability than linear models.","https://schema.org",{"og:url":32,"og:type":74,"og:title":10,"og:site_name":45,"og:description":12},"article",{"robots":76,"canonical":32},"index,follow",{"doc_id":78,"site_id":7},290646,1789642265,{"code":4,"msg":81,"data":82},"success",[83,87,91,95,100,105,110,114,119,122,126],{"id":22,"doc_module":4,"doc_module_name":25,"category_name":84,"show_sort_weight":85,"slug":86},"Story & Novel",90,"story-novel",{"id":26,"doc_module":4,"doc_module_name":25,"category_name":88,"show_sort_weight":89,"slug":90},"Literature",80,"literature",{"id":33,"doc_module":4,"doc_module_name":25,"category_name":92,"show_sort_weight":93,"slug":94},"Exam",70,"exam",{"id":96,"doc_module":4,"doc_module_name":25,"category_name":97,"show_sort_weight":98,"slug":99},5,"Comic",60,"comic",{"id":101,"doc_module":4,"doc_module_name":25,"category_name":102,"show_sort_weight":103,"slug":104},6,"Technology",50,"technology",{"id":106,"doc_module":4,"doc_module_name":25,"category_name":107,"show_sort_weight":108,"slug":109},7,"Healthcare",40,"healthcare",{"id":111,"doc_module":4,"doc_module_name":25,"category_name":29,"show_sort_weight":112,"slug":113},8,30,"research-report",{"id":115,"doc_module":4,"doc_module_name":25,"category_name":116,"show_sort_weight":117,"slug":118},9,"Religion & Spirituality",20,"religion-spirituality",{"id":117,"doc_module":4,"doc_module_name":25,"category_name":120,"show_sort_weight":117,"slug":121},"World Cup","world-cup",{"id":123,"doc_module":4,"doc_module_name":25,"category_name":124,"show_sort_weight":123,"slug":125},10,"Lifestyle","lifestyle",{"id":127,"doc_module":4,"doc_module_name":25,"category_name":128,"show_sort_weight":96,"slug":129},19,"General","general",{"code":4,"msg":81,"data":131},{"doc_id":78,"user_id":132,"nickname":42,"user_avatar":133,"doc_module":4,"category_id":111,"category_name":29,"doc_title":10,"doc_description":12,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":26,"is_deleted":4,"is_public":22,"is_downloadable":22,"audit_status":22,"page_count":139,"language":140,"language_code":8,"site_id":7,"html_lang":8,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":12,"update_tm":79,"read_time":144},7971461740909,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","Abstract This study examines the application of machine learning models to predict financial performance  \nin various sectors, using data from 21 companies listed in the BIST100 index (2013-2023) . The primary objective is to assess the potential of these models in improving financial forecast accuracy and to emphasize the need for transparent, explainable approaches in finance. A range of machine learning models, including Linear Regression, Ridge, Lasso, Decision Tree, Bagging, Random Forest, AdaBoost, Gradient Boosting (GBM), LightGBM, and XGBoost, were evaluated. Gradient Boosting emerged as the best-performing model, with ensemble methods generally demonstrating superior accuracy and stability compared to linear models. To enhance interpretability, SHAP (SHapley Additive exPlanations) values were utilized, identifying the most influential variables affecting predictions and providing insights into model behavior. Sector-based analyses further revealed differences in model performance and feature impacts, offering a granular understanding of financial dynamics across industries. The findings highlight the effectiveness of machine learning, particularly ensemble methods, in forecasting financial performance. The study underscores the importance of using explainable models in finance to build trust and support decision-making. By integrating advanced techniques with interpretability tools, this research contributes to financial technology, advancing the adoption of machine learning in datadriven investment strategies.  \nJEL classification: C51, C52, C53  \nKeywords: Machine Learning Models, SHAP, Financial Forecasting  \nReceived: 31.07.2024 Accepted: 15.11.2024  \nCite this:  \nErgenç, C. & Aktaş, R. (2025) . Sector-specific financial forecasting with machine learning algorithm and SHAP interaction values. Financial Internet Quarterly 21(1), pp. 42-66.  \n© 2025 Cansu Ergenç and Rafet Aktaş, published by Sciendo. This work is licensed under the Creative Commons Attribution-NonCommercialNoDerivatives 3.0 License.  \n1 Ankara Yildirim Beyazit University, Ankara, Turkey, [e-mail: cansuergenc@aybu.edu.tr](e-mail: cansuergenc@aybu.edu.tr), ORCID: [https://orcid.org/0000-0002-4722-0911](https://orcid.org/0000-0002-4722-0911) .  \n2 Ankara Yildirim Beyazit University, Ankara, Turkey, [e-mail: raktas@aybu.edu.tr](e-mail: raktas@aybu.edu.tr), ORCID: [https://orcid.org/0009-0008-8033-4604](https://orcid.org/0009-0008-8033-4604) .  \nCansu Ergenç, RafetAktaş  \nSector-specific financial forecasting with machine learning algorithm and SHAP Financial Internet Quarterly 2025, vol. 21 / no. 1  \ninteraction values  \nFinancial performance has always been crucial for companies, impacting nations globally. It is crucial for all countries and companies (Perrini et al., 2011; Barauskaite & Streimikiene, 2020) . In recent years, the combination of finance and artificial intelligence has not just led to progress, but a transformation in financial forecasting (Lin, 2019; Nguyen et al., 2022; Avelar & Jordão, 2024) . Machine learning algorithms also playa major role in this transformation. Because machine learning algorithms have provided advanced techniques that can process large amounts of data, identify patterns, and make predictions with unprecedented accuracy (Zhou et al., 2017; Mahalakshmi et al., 2022; Bouchefry & De Souza, 2020) . Learning from historical data and adapting to new information, which is a feature of machine learning models, and the performance of models that improve over time are very important developments for finance (Pandey & Sergeeva, 2022; Ionescu & Diaconita, 2023; George, 2024) .  \nThe place of accurate financial forecasting for financial markets is undeniable (Penman, 2002; Samonas, 2015; Kumar, 2017; Barnhizer & Barnhizer, 2019; Sastry, 2020; Massei, 2023) . Investors reduce their financial risks and make informed investments by making the right investment decisions for accurate financial forecasts. Financial analysts, on the","cbCaieD3CfITT5QD","https://ap.wps.com/l/cbCaieD3CfITT5QD","pdf",4458580,26,"English","# Abstract\n# Introduction\n## Financial forecasting and decision-making\n## Machine learning methods in finance\n# Data and evaluation scope\n## BIST100 index and sectoral performance","[{\"question\":\"Which machine learning models are evaluated in the study?\",\"answer\":\"The study evaluates Linear Regression, Ridge, Lasso, Decision Tree, Bagging, Random Forest, AdaBoost, Gradient Boosting (GBM), LightGBM, and XGBoost.\"},{\"question\":\"Why is SHAP used in the financial forecasting approach?\",\"answer\":\"SHAP (SHapley Additive exPlanations) values are used to identify the most influential variables affecting predictions and to explain model behavior.\"},{\"question\":\"What main conclusion does the research draw about model performance?\",\"answer\":\"Gradient Boosting performs best, and ensemble methods generally deliver better accuracy and stability than linear models.\"}]","Sector-specific financial forecasting with machine learning algorithm and SHAP interaction values - research paper | PDF",66]