[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122102-en":3,"doc-seo-122102-105":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},122102,8796095461610,"Oliver","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Forecasting Emerging Stock Market Crashes via Machine Learning - Thesis","Stock markets reflect overall economic health by enabling firms to raise capital and drive growth, yet they remain vulnerable to crashes whose consequences can persist long after the event. This thesis studies stock market crashes in emerging markets using multiple machine learning models and a comprehensive dataset covering 32 emerging economies. Features are derived from market data and supplemented with engineered liquidity indicators. A tuned Artificial Neural Network variation achieves the best performance (~96.66%) with a high true positive rate and low false positives. SHAP analysis shows lagged, mean, and standard deviation liquidity attributes over the last week and month are key contributors, indicating gradual crash dynamics.","AUS Repository  \nForecasting Emerging Stock Market Crashes via Machine Learning  \n\n| Item Type | Thesis |\n| --- | --- |\n| Authors | Khan, Mohammad Osama |\n| Download date | 2026-05-15 17:06:19 |\n| Link to Item | [http://hdl.handle. net/11073/25478](http://hdl.handle. net/11073/25478) |\n\nFORECASTING EMERGING STOCK MARKET  \nCRASHES VIA MACHINE LEARNING  \nby  \nMohammad Osama Khan  \nA Thesis presented to the Faculty of the American University of Sharjah College of Engineering In Partial Fulfilment of the Requirements for the Degree of  \nMaster of Science in Engineering Systems Management  \nSharjah, United Arab Emirates  \nNovember 2023  \nDeclaration of Authorship  \nI declare that this thesis is my own work and, to the best of my knowledge and belief, it does not contain material published or written by a third party, except where permission has been obtained and/or appropriately cited through full and accurate referencing.  \nSigned MOHAMMAD OSAMA KHAN .  \nDate 27th November 2023 .  \nThe Author controls copyright for this report.  \nMaterial should not be reused without the consent of the author. Due acknowledgement should be made where appropriate.  \n© Year 2023  \nMohammad Osama Khan  \nALL RIGHTS RESERVED  \nApprovals  \nWe, the undersigned, approve the Masters Thesis written by:  \nMohammad Osama Khan  \nThesis Title: Forecasting Emerging Stock Market Crashes via  \nMachine Learning  \nDate of Defense: 28/11/2023  \nName, Title and Affiliation Signature  \n\n| Dr. Hussam Alshraideh\u003Cbr>Associate Professor\u003Cbr>Department of Industrial Engineering\u003Cbr>Thesis Advisor |\n| --- |\n| Dr. Zied Bahroun\u003Cbr>Associate Professor\u003Cbr>Department of Industrial Engineering\u003Cbr>Thesis Co-advisor |\n| Dr. Anis Samet\u003Cbr>Professor\u003Cbr>Department of Finance\u003Cbr>Thesis Co-Advisor |\n| Dr. Abdulrahim Shamayleh\u003Cbr>Associate Professor\u003Cbr>Department of Industrial Engineering\u003Cbr>Thesis Examiner (Internal) |\n\nDr. Ra'afat Abu-Rukba Assistant Professor  \nDepartment of Computer Science and Engineering University Name: American University of Sharjah Thesis Examiner (External)  \nAccepted by:  \nDr. Fadi Aloul Dean  \nCollege/School of Engineering  \nDr. Mohamed El-Tarhuni  \nVice Provost for Research and Graduate Studies Office of Research and Graduate Studies  \nAcknowledgements  \nI would like to express my sincere gratitude to my advisors, Dr. Hussam Alsharaideh, Dr. Anis Samet, and Dr. Zied Bahroun, for their invaluable guidance, support, and mentorship throughout the course of my research. Their expertise and unwavering commitment to my academic and professional growth have been instrumental in the successful completion of this thesis.  \nI want to express my gratitude to the teachers in the department of industrial engineering who used excellent teaching strategies and techniques to guide me through the master's level courses. I greatly value their respectful counsel and inspiration.  \nI also appreciate the American University of Sharjah for the assistantship, which has been crucial to my academic success. I appreciate AUS's commitment to fostering students' intellectual and professional development, and I am proud to be associated with the university.  \nDedication  \nI dedicate this thesis to my cherished family, whose enduring support and boundless affection have served as my steadfast pillars of strength throughout my academic journey.  \nTo my parents, your sacrifices, encouragement, and unwavering belief in my abilities have propelled me on my quest for knowledge. Your enduring faith in me has consistently inspired me, and I am profoundly grateful for the values, guidance, and wisdom you have bestowed upon me.  \nTo my siblings, you have been my confidants, my enthusiastic supporters, and my companions on numerous adventures. Your faith in my aspirations and your patience during my late-night study sessions have not gone unnoticed, and I deeply treasure the strong bond we share.  \nTo my extended family, friends, and loved ones, I extend my heartfelt thanks for your understanding, su","cbCaioDqj9SShb3i","https://ap.wps.com/l/cbCaioDqj9SShb3i","pdf",1464551,1,68,"English","en",105,"# Abstract\n# Motivation and Background\n# Data and Feature Engineering\n# Machine Learning Models and Evaluation\n# Model Interpretability via SHAP\n# Findings and Implications","[{\"question\":\"What is the main goal of the thesis?\",\"answer\":\"To forecast crashes in emerging stock markets and understand which factors most influence the predictions.\"},{\"question\":\"What data and features are used for prediction?\",\"answer\":\"The study uses stock market data from 32 emerging market countries, with features derived from market data and engineered liquidity indicators.\"},{\"question\":\"Which model performs best and how is it evaluated?\",\"answer\":\"A variation of an Artificial Neural Network delivers the top performance, achieving about 96.66% accuracy with a high true positive rate and low false positive rate.\"}]","Forecasting Emerging Stock Market Crashes via Machine Learning - 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