Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach
In recent times there is a consensus that the stock market is a dynamic and complex system, with some factors difficult to assess and are highly unpredictable that can cause disruptions. While the influence of crises and uncertainties on individual stock markets has been well-studied, a systematic u...
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| Format: | Article |
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2025-06-01
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| Series: | ECONOMICS |
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| Online Access: | https://doi.org/10.2478/eoik-2025-0041 |
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| author | Suresh Vidya Kolluru Mythili Ubaidullah Vaheed |
| author_facet | Suresh Vidya Kolluru Mythili Ubaidullah Vaheed |
| author_sort | Suresh Vidya |
| collection | DOAJ |
| description | In recent times there is a consensus that the stock market is a dynamic and complex system, with some factors difficult to assess and are highly unpredictable that can cause disruptions. While the influence of crises and uncertainties on individual stock markets has been well-studied, a systematic understanding of their impact on global market relationships remains limited. This paper explores Machine Learning techniques over traditional econometric techniques to analyze stock market behavior of select countries over a period of time of twenty-one years. Specifically, we utilize a novel end-to-end hierarchical clustering method and proximity analysis to uncover changes in global stock market behavior across various crisis periods (2001, 2002, 2007-2009, 2016, and 2020). Daily time series data for global stock indices from 2002, to 2023, is analyzed. The proposed clustering method effectively identifies groups of countries with distinct risk profiles. These clusters, combined with an inference strategy, have the potential to inform investment decisions by aiding in the selection of outperforming or underperforming stocks. The results led to four clusters out of 26 countries depicting some countries consistently showing similarity in the behavior of stock market dynamics. Countries like India, Japan, Australia, New Zealand, Sweden, Israel, the US, and South Korea have been considered as balancing out volatility and hedging risks. The study paves the way for further exploration by incorporating macroeconomic variables to investigate their influence on the stock indices within each identified cluster. Additionally, the analysis of common characteristics within each cluster can be further explored. |
| format | Article |
| id | doaj-art-44eb02eba7b347b3a4d92ab00a68bea7 |
| institution | DOAJ |
| issn | 2303-5013 |
| language | English |
| publishDate | 2025-06-01 |
| publisher | Sciendo |
| record_format | Article |
| series | ECONOMICS |
| spelling | doaj-art-44eb02eba7b347b3a4d92ab00a68bea72025-08-20T03:09:52ZengSciendoECONOMICS2303-50132025-06-0113228330310.2478/eoik-2025-0041Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning ApproachSuresh Vidya0Kolluru Mythili1Ubaidullah Vaheed2Department of Finance and Accounting, College of Banking and Financial Studies, Bausher Heights, Muscat, OmanDepartment of Finance and Accounting, College of Banking and Financial Studies, Bausher Heights, Muscat, OmanDepartment of Finance and Accounting, College of Banking and Financial Studies, Bausher Heights, Muscat, OmanIn recent times there is a consensus that the stock market is a dynamic and complex system, with some factors difficult to assess and are highly unpredictable that can cause disruptions. While the influence of crises and uncertainties on individual stock markets has been well-studied, a systematic understanding of their impact on global market relationships remains limited. This paper explores Machine Learning techniques over traditional econometric techniques to analyze stock market behavior of select countries over a period of time of twenty-one years. Specifically, we utilize a novel end-to-end hierarchical clustering method and proximity analysis to uncover changes in global stock market behavior across various crisis periods (2001, 2002, 2007-2009, 2016, and 2020). Daily time series data for global stock indices from 2002, to 2023, is analyzed. The proposed clustering method effectively identifies groups of countries with distinct risk profiles. These clusters, combined with an inference strategy, have the potential to inform investment decisions by aiding in the selection of outperforming or underperforming stocks. The results led to four clusters out of 26 countries depicting some countries consistently showing similarity in the behavior of stock market dynamics. Countries like India, Japan, Australia, New Zealand, Sweden, Israel, the US, and South Korea have been considered as balancing out volatility and hedging risks. The study paves the way for further exploration by incorporating macroeconomic variables to investigate their influence on the stock indices within each identified cluster. Additionally, the analysis of common characteristics within each cluster can be further explored.https://doi.org/10.2478/eoik-2025-0041cluster analysishierarchy clusterstock markete44g100f390,g00g14y10 |
| spellingShingle | Suresh Vidya Kolluru Mythili Ubaidullah Vaheed Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach ECONOMICS cluster analysis hierarchy cluster stock market e44 g100 f390,g00 g14 y10 |
| title | Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach |
| title_full | Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach |
| title_fullStr | Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach |
| title_full_unstemmed | Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach |
| title_short | Encoding Behavior Commonalities In Global Stock Market Indexes: Unsupervised Machine Learning Approach |
| title_sort | encoding behavior commonalities in global stock market indexes unsupervised machine learning approach |
| topic | cluster analysis hierarchy cluster stock market e44 g100 f390,g00 g14 y10 |
| url | https://doi.org/10.2478/eoik-2025-0041 |
| work_keys_str_mv | AT sureshvidya encodingbehaviorcommonalitiesinglobalstockmarketindexesunsupervisedmachinelearningapproach AT kollurumythili encodingbehaviorcommonalitiesinglobalstockmarketindexesunsupervisedmachinelearningapproach AT ubaidullahvaheed encodingbehaviorcommonalitiesinglobalstockmarketindexesunsupervisedmachinelearningapproach |