Forecasting Emerging Stock Market Crashes via Machine Learning
A Master of Science thesis in Engineering Systems Management by Mohammad Osama Khan entitled, “Forecasting Emerging Stock Market Crashes via Machine Learning”, submitted in November 2023. Thesis advisor is Dr. Hussam Alshraideh and thesis co-advisors are Dr. Zied Bahroun and Dr. Anis Samet. Soft cop...
محفوظ في:
| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2023
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| الموضوعات: | |
| الوصول للمادة أونلاين: | http://hdl.handle.net/11073/25478 |
| الوسوم: |
إضافة وسم
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| _version_ | 1864513437115088896 |
|---|---|
| author | Khan, Mohammad Osama |
| author_facet | Khan, Mohammad Osama |
| author_role | author |
| dc.contributor.none.fl_str_mv | Alshraideh, Hussam Bahroun, Zied Samet, Anis |
| dc.creator.none.fl_str_mv | Khan, Mohammad Osama |
| dc.date.none.fl_str_mv | 2023-11 2024-02-29T07:24:45Z 2024-02-29T07:24:45Z |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2023.68 http://hdl.handle.net/11073/25478 |
| dc.language.none.fl_str_mv | en_US |
| dc.subject.none.fl_str_mv | Machine Learning Liquidity Proxies Stock Market Crashes Emerging Markets Predictive Modelling Financial Forecasting SHAP Shapley Additive Explanations (SHAP) |
| dc.title.none.fl_str_mv | Forecasting Emerging Stock Market Crashes via Machine Learning |
| dc.type.none.fl_str_mv | info:eu-repo/semantics/publishedVersion info:eu-repo/semantics/doctoralThesis |
| description | A Master of Science thesis in Engineering Systems Management by Mohammad Osama Khan entitled, “Forecasting Emerging Stock Market Crashes via Machine Learning”, submitted in November 2023. Thesis advisor is Dr. Hussam Alshraideh and thesis co-advisors are Dr. Zied Bahroun and Dr. Anis Samet. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_bbf5320ccd3855109b04acdfb33e3983 |
| identifier_str_mv | 35.232-2023.68 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/25478 |
| publishDate | 2023 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Forecasting Emerging Stock Market Crashes via Machine LearningKhan, Mohammad OsamaMachine LearningLiquidity ProxiesStock Market CrashesEmerging MarketsPredictive ModellingFinancial ForecastingSHAPShapley Additive Explanations (SHAP)A Master of Science thesis in Engineering Systems Management by Mohammad Osama Khan entitled, “Forecasting Emerging Stock Market Crashes via Machine Learning”, submitted in November 2023. Thesis advisor is Dr. Hussam Alshraideh and thesis co-advisors are Dr. Zied Bahroun and Dr. Anis Samet. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).Stock markets indicate the overall health of an economy as they play a vital role in providing a way for companies to raise capital, create new opportunities and stimulate economic growth. However, stock markets are prone to crashes and the aftermath of such an event can cause far-reaching and long-lasting effects on the economy depending on the severity which induces a need to study stock market crashes. This work explores the idea of crashes in emerging stock markets leveraging a diverse array of machine learning models, while utilizing a comprehensive dataset comprising stock market data from 32 emerging market countries, with features derived from market data, along with several engineered liquidity features. A variation of the Artificial Neural Network model is identified as the top performer displaying high accuracy, about 96.66%, with high true positive rate and low false positive rate, outperforming existing models in the literature. In industry-specific analysis, the model consistently achieved strong true positive and false positive rates, indicating acceptable outcomes for the specific industries under consideration. Furthermore, it is found, using the SHapley Additive exPlanations framework, that return along with the attributes reflecting lag, mean, and standard deviation of liquidity indicators over the past week and month significantly contribute to the prediction of crashes suggesting that stock market crashes are typically gradual processes rather than abrupt occurrences. These findings hold profound implications for risk management and investment decision-making in emerging markets, offering valuable insights for both academia and industry practitioners.College of EngineeringMultidisciplinary ProgramsMaster of Science in Engineering Systems Management (MSESM)Alshraideh, HussamBahroun, ZiedSamet, Anis2024-02-29T07:24:45Z2024-02-29T07:24:45Z2023-11info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2023.68http://hdl.handle.net/11073/25478en_USoai:repository.aus.edu:11073/254782025-06-26T12:11:27Z |
| spellingShingle | Forecasting Emerging Stock Market Crashes via Machine Learning Khan, Mohammad Osama Machine Learning Liquidity Proxies Stock Market Crashes Emerging Markets Predictive Modelling Financial Forecasting SHAP Shapley Additive Explanations (SHAP) |
| status_str | publishedVersion |
| title | Forecasting Emerging Stock Market Crashes via Machine Learning |
| title_full | Forecasting Emerging Stock Market Crashes via Machine Learning |
| title_fullStr | Forecasting Emerging Stock Market Crashes via Machine Learning |
| title_full_unstemmed | Forecasting Emerging Stock Market Crashes via Machine Learning |
| title_short | Forecasting Emerging Stock Market Crashes via Machine Learning |
| title_sort | Forecasting Emerging Stock Market Crashes via Machine Learning |
| topic | Machine Learning Liquidity Proxies Stock Market Crashes Emerging Markets Predictive Modelling Financial Forecasting SHAP Shapley Additive Explanations (SHAP) |
| url | http://hdl.handle.net/11073/25478 |