[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117101-en":3,"doc-seo-117101-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},117101,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Predictive Modelling of Financial Crises - Machine Learning Algorithms in a Recursive Real-Time Framework","The thesis analyzes predictive modelling approaches for banking and financial crises by focusing on the identification of early warning signals using machine learning. It reviews the state of the art in forecasting models and discusses the practical challenges faced by early warning systems, including scarce crisis observations, delayed indicator signals, missing data, and the need for transparent modelling for macroprudential authorities. The author implements a baseline recursive framework trained on pre-crisis windows and tested year-by-year, comparing variants and evaluating variable significance under strict train-test separation.","UNIVERSITA’ DEGLI STUDI DI PADOVA  \nDIPARTIMENTO DI SCIENZE ECONOMICHE ED AZIENDALI  \n“M. FANNO”  \nCORSO DI LAUREA MAGISTRALE IN ECONOMICS AND FINANCE  \nTESI DI LAUREA  \n“PREDICTIVE MODELLING OF FINANCIAL CRISES: MACHINE LEARNING ALGORITHMS IN A RECURSIVE REAL-TIME FRAMEWORK”  \nRELATORE:  \nCH.MO PROF. FORNI LORENZO  \nLAUREANDO: MORO FILIPPO  \nMATRICOLA N. 2023368  \nANNO ACCADEMICO 2023 – 2024  \nINDEX  \nINTRODUCTION ...................................................................................................................... 1  \nFINANCIAL CRISES ................................................................................................................ 3  \nIMPLEMENTED MODELS ...................................................................................................... 8  \nPERFORMANCE EVALUATION METRICS ........................................................................26  \nLITERATURE SURVEY ......................................................................................................... 33  \nPREDICTORS ..........................................................................................................................46  \nBASELINE EXERCISE........................................................................................................... 55  \nVARIATIONS........................................................................................................................... 70  \nLAGS ANALYSIS.................................................................................................................... 79  \n2023-2025 PREDICTIONS .................................................................................................... 88  \nCONCLUSIONS ...................................................................................................................... 91  \nBIBLIOGRAPHY .................................................................................................................... 93  \nAPPENDIX .............................................................................................................................. 97  \nINTRODUCTION  \nIn recent decades, the global financial landscape has witnessed a surge in banking crises, causing substantial economic and social damage across developed and developing economies alike. The 1990s and early 2000s marked a period of heightened financial instability, with crises ranging from the collapse of banking systems in emerging markets to the profound disruptions experienced by developed western economies. The recurrence of banking crises is not a new phenomenon, with historical precedents such as the Great Depression serving as reminders of the potential catastrophic consequences. The post-World War II era saw a period of tight regulations on banks in response to past crises, temporarily mitigating the issue. However, a general trend toward financial liberalisation began in the 1970s, leading to a resurgence of banking problems. From the Mexican and Argentine crises in the 1990s and the Asian financial turmoil in 1997-1998, to the Great Financial Crisis (GFC) of 2008, these events have emerged across different geographical boundaries, posing significant challenges to policymakers.  \nThe costs associated with resolving these crises have been enormous, often reaching doubledigit percentages of GDP in affected countries (Laeven and Valencia, 2020) . The global financial crisis of 2008 demonstrated the rapid and far-reaching consequences of a banking calamity. The interconnectedness of financial systems across continents led to prolonged economic and financial losses, with adverse effects persisting for years. The financial institutions, once considered guardians of depositors' savings, found themselves at the centre of a crisis that shook the foundations of economies worldwide. The aftermath of such crises includes severe unemployment, increased poverty, weakened exports, and pro-cyclical spending by governments, exacerbating the","cbCainVFNxfI2E8S","https://ap.wps.com/l/cbCainVFNxfI2E8S","pdf",5444961,1,116,"English","en",105,"# Introduction\n## Financial Crises\n## Implemented Models\n## Performance Evaluation Metrics\n## Literature Survey\n## Predictors\n## Baseline Exercise\n## Variations\n## Lags Analysis\n## 2023-2025 Predictions\n## Conclusions\n## Bibliography\n## Appendix","[{\"question\":\"What problem does the thesis address in financial crisis forecasting?\",\"answer\":\"It addresses the need for effective early warning systems that can identify conditions leading to financial turmoil, despite limited observed crises and practical data and timing constraints.\"},{\"question\":\"How is the modelling framework set up for training and testing?\",\"answer\":\"Models are trained on pre-crisis periods defined as the three years before a crisis event and then tested recursively year-by-year on a separate testing subset, with strict separation between training and testing to avoid biased evaluation.\"},{\"question\":\"What is the objective of implementing baseline and variant models?\",\"answer\":\"The baseline analysis and multiple variants are used to compare results with prior literature, identify the best algorithms, and assess the significance and potential impact of individual predictors and lags on predicted outcomes.\"}]","Predictive Modelling of Financial Crises - 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