[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127197-en":3,"doc-seo-127197-105":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},127197,549768702563,"Sage","https://ap-avatar.wpscdn.com/avatar/8000c4aa63b76e948b?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786536092046926083",8,"Research & Report","Financial Distress Prediction in the Nordics - Early Warnings from Machine Learning Models - explicable model summary","This paper presents an explicable early warning machine learning approach for predicting financial distress across listed Nordic corporations. A new dataset spans Q1 2001 to Q2 2022 and merges idiosyncratic quarterly financial statement data with financial-market information and macroeconomic trend indicators. A LightGBM model with features selected via explainable AI outperforms benchmark methods across evaluation metrics. Liquidity, solvency, and firm size emerge as highly important predictors, and incorporating seasonality alongside entity, market, and macro inputs further improves performance.","Article  \nFinancial Distress Prediction in the Nordics: Early Warnings from Machine Learning Models  \nNils-Gunnar Birkeland Abrahamsen 1, Emil Nylén-Forthun 1, Mats Møller 1, Petter Eilif de Lange 2 and Morten Risstad 1, *  \nCitation: Abrahamsen, Nils-Gunnar Birkeland, Emil Nylén-Forthun, Mats Møller, Petter Eilif de Lange, and Morten Risstad. 2024. Financial Distress Prediction in the Nordics:  \nEarly Warnings from Machine Learning Models. Journal of Risk and Financial Management 17: 432 .  \n[https://doi.org/10.3390/jrfm17100432](https://doi.org/10.3390/jrfm17100432)  \nAcademic Editor: Jong-Min Kim  \nReceived: 13 August 2024  \nRevised: 25 September 2024  \nAccepted: 26 September 2024  \nPublished: 27 September 2024  \nCopyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ([https://](https://)[ ](https://)[creativecommons.org/licenses/by/](creativecommons.org/licenses/by/)[ ](creativecommons.org/licenses/by/)[4.0/](4.0/)) .  \n1 Department of Industrial Economics and Technology Management, Norwegian University of Science and Technology, 7491 Trondheim, Norway  \n2 Department of International Business, Norwegian University of Science and Technology, 6001 Ålesund, Norway; [petter.e.delange@ntnu.no](petter.e.delange@ntnu.no)  \n* [Correspondence: morten.risstad@ntnu.no](Correspondence: morten.risstad@ntnu.no); Tel.: +47-97166263  \nAbstract: This paper proposes an explicable early warning machine learning model for predicting financial distress, which generalizes across listed Nordic corporations. We develop a novel dataset, covering the period from Q1 2001 to Q2 2022, in which we combine idiosyncratic quarterly financial statement data, information from financial markets, and indicators of macroeconomic trends. The preferred LightGBM model, whose features are selected by applying explainable artificial intelligence, outperforms the benchmark models by a notable margin across evaluation metrics. We find that features related to liquidity, solvency, and size are highly important indicators of financial health and thus crucial variables for forecasting financial distress. Furthermore, we show that explicitly accounting for seasonality, in combination with entity, market, and macro information, improves model performance.  \nKeywords: financial distress prediction; credit risk; machine learning; explainable AI; Nordics  \n1. Introduction  \nFor decades financial distress prediction has been on the agenda of both practitioners and researchers. On the backdrop of globalization and increased economic complexity, researchers have developed quantitative models which can infer the true state of companies beyond what can be captured by simple and rigid methodologies, with machine learning (ML) methods now representing the state of the art.  \nRegardless of its definition, financial distress prediction relies on data reflecting the company’s true situation. Data derived from financial statements play a critical role in this regard. However, as pointed out by Jan (2021), relying solely on information from financial statements for distress prediction is risky due to information asymmetry. Businesses may use different accounting practices, implying that the probability of financial distress can differ for companies with otherwise similar financial statements. This encourages expanding the feature space of prediction models to include macro variables and forward-looking market variables that are helpful in identifying the true state of a company beyond what is available from periodically disclosed financial statements. Furthermore, financial distress prediction is often analyzed on a micro-or lower macroeconomic scale, evaluating either a set of companies within a single country or a specific industry. Arguably, this may be too narrow a scope given the increasingly globalized economy and the scarcity of distr","cbCaiePitWo6yt7M","https://ap.wps.com/l/cbCaiePitWo6yt7M","pdf",2296126,1,23,"English","en",105,"# Introduction\n## Data and feature construction\n## Modeling approach and explainability\n## Evaluation and key findings\n# Conclusion","[{\"question\":\"What machine learning model is emphasized for early warning performance?\",\"answer\":\"The study highlights a preferred LightGBM model whose feature selection is guided by explainable AI, achieving superior results versus benchmark models.\"},{\"question\":\"What data sources are combined to predict financial distress?\",\"answer\":\"The model integrates idiosyncratic quarterly financial statement data, information from financial markets, and indicators reflecting macroeconomic trends.\"},{\"question\":\"Which factors are identified as especially important for financial health?\",\"answer\":\"Features related to liquidity, solvency, and firm size are reported as highly important indicators for forecasting financial distress.\"}]","Financial Distress Prediction in the Nordics - Early Warnings from Machine Learning Models - explicable model summary | PDF",1785937450,58,{"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},"financial-distress-prediction-in-the-nordics-early-warnings-from-machine-learning-models-explicable-model-summary","",{"@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/financial-distress-prediction-in-the-nordics-early-warnings-from-machine-learning-models-explicable-model-summary/127197/",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 machine learning model is emphasized for early warning performance?","Question",{"text":75,"@type":76},"The study highlights a preferred LightGBM model whose feature selection is guided by explainable AI, achieving superior results versus benchmark models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources are combined to predict financial distress?",{"text":80,"@type":76},"The model integrates idiosyncratic quarterly financial statement data, information from financial markets, and indicators reflecting macroeconomic trends.",{"name":82,"@type":73,"acceptedAnswer":83},"Which factors are identified as especially important for financial health?",{"text":84,"@type":76},"Features related to liquidity, solvency, and firm size are reported as highly important indicators for forecasting financial distress.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]