Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study
A Master of Science thesis in Engineering Systems Management by Khaled Essam Hosny Ahmed entitled, “Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study”, submitted in February 2026. Thesis advisor is Dr. Hussam AlShraideh and thesis co-advisor is Dr. Anis Samet. Soft...
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| المؤلف الرئيسي: | |
|---|---|
| التنسيق: | doctoralThesis |
| منشور في: |
2026
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| الموضوعات: | |
| الوصول للمادة أونلاين: | https://hdl.handle.net/11073/33523 |
| الوسوم: |
إضافة وسم
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| _version_ | 1870676404235927552 |
|---|---|
| author | Ahmed, Khaled Essam Hosny |
| author_facet | Ahmed, Khaled Essam Hosny |
| author_role | author |
| dc.contributor.none.fl_str_mv | Alshraideh, Hussam Samet, Anis |
| dc.creator.none.fl_str_mv | Ahmed, Khaled Essam Hosny |
| dc.date.none.fl_str_mv | 2026-06-22T07:52:56Z 2026-06-22T07:52:56Z 2026-02 |
| dc.format.none.fl_str_mv | application/pdf |
| dc.identifier.none.fl_str_mv | 35.232-2026.04 https://hdl.handle.net/11073/33523 |
| dc.language.none.fl_str_mv | en_US |
| dc.relation.none.fl_str_mv | Master of Science in Engineering Systems Management (MSESM) |
| dc.subject.none.fl_str_mv | Negative Coefficient of Skewness Down-to-Up Volatility Expected Shortfall Stock-Price measures Stock-Liquidity measures Machine Learning |
| dc.title.none.fl_str_mv | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| 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 Khaled Essam Hosny Ahmed entitled, “Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study”, submitted in February 2026. Thesis advisor is Dr. Hussam AlShraideh and thesis co-advisor is Dr. Anis Samet. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form). |
| format | doctoralThesis |
| id | aus_bb985271a0cee1c2d251c9954434977d |
| identifier_str_mv | 35.232-2026.04 |
| language_invalid_str_mv | en_US |
| network_acronym_str | aus |
| network_name_str | aus |
| oai_identifier_str | oai:repository.aus.edu:11073/33523 |
| publishDate | 2026 |
| repository.mail.fl_str_mv | |
| repository.name.fl_str_mv | |
| repository_id_str | |
| spelling | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning StudyAhmed, Khaled Essam HosnyNegative Coefficient of SkewnessDown-to-Up VolatilityExpected ShortfallStock-Price measuresStock-Liquidity measuresMachine LearningA Master of Science thesis in Engineering Systems Management by Khaled Essam Hosny Ahmed entitled, “Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study”, submitted in February 2026. Thesis advisor is Dr. Hussam AlShraideh and thesis co-advisor is Dr. Anis Samet. Soft copy is available (Thesis, Completion Certificate, Approval Signatures, and AUS Archives Consent Form).The stability and predictability of stock markets are crucial for shaping global economic dynamics, particularly in emerging markets where volatility and risk are elevated. This research conducts a rigorous study to predict stock crashes caused by price volatility and periods of insufficient liquidity using machine learning techniques. Using novel firm-specific stock-price and stock-liquidity measures, specifically Negative Coefficient of Skewness (NCSKEW), Down-to-Up Volatility (DUVOL), and Expected Shortfall (ES), among others, we engineered 261 predictive features across multiple time windows. To define an output crash event, we employ a consensus-based ensemble approach, flagging crashes when at least five of the twenty complementary risk indicators simultaneously fall below their rolling 5th percentile thresholds. Our analysis leverages an exceptionally comprehensive dataset: 23 million observations spanning approximately 37 years (1985 – 2022) across 24,197 stocks from 57 emerging markets. This geographic and temporal breadth significantly exceeds that of the existing literature. Evaluating four machine learning algorithms: Classification and Regression Trees (CART), Logistic Regression (GLM), XGBoost, and Random Forest, we find that Random Forest achieves superior performance: 98.81% accuracy, Cohen’s Kappa of 0.815, and a Receiver Operating Characteristic – Area Under the Curve (ROC AUC) of 0.995, demonstrating exceptional prediction performance despite severe class imbalance ~3.96% (25:1 ratio). Notably, the model maintains 99.9% sensitivity while keeping the false alarm rate at 0.11%. Through SHAP analysis of Random Forest, we identify Expected Shortfall (ES) as the dominant crash predictor, with ES measures across multiple confidence levels (90%, 95%, 97.5%, 99%) and time windows dominating feature importance rankings. The SHAP beeswarm analysis reveals a clear relationship: higher tail risk values directly increase stock crash probability, with 504-day ES windows proving most predictive. Our results demonstrate that ensemble machine learning methods effectively capture complex, non-linear relationships between stock crashes and various price and liquidity measures, providing actionable insights for investment decision-makers.College of EngineeringDepartment of Industrial EngineeringMaster of Science in Engineering Systems Management (MSESM)Alshraideh, HussamSamet, Anis2026-06-22T07:52:56Z2026-06-22T07:52:56Z2026-02info:eu-repo/semantics/publishedVersioninfo:eu-repo/semantics/doctoralThesisapplication/pdf35.232-2026.04https://hdl.handle.net/11073/33523en_USMaster of Science in Engineering Systems Management (MSESM)oai:repository.aus.edu:11073/335232026-06-23T06:52:41Z |
| spellingShingle | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study Ahmed, Khaled Essam Hosny Negative Coefficient of Skewness Down-to-Up Volatility Expected Shortfall Stock-Price measures Stock-Liquidity measures Machine Learning |
| status_str | publishedVersion |
| title | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| title_full | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| title_fullStr | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| title_full_unstemmed | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| title_short | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| title_sort | Forecasting Stock Crashes in Emerging Markets: A Comparative Machine Learning Study |
| topic | Negative Coefficient of Skewness Down-to-Up Volatility Expected Shortfall Stock-Price measures Stock-Liquidity measures Machine Learning |
| url | https://hdl.handle.net/11073/33523 |