Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques
Selection of the optimal rebalancing frequency (ORF) is crucial for the pair trading algorithm (PTA) that periodically rebalances the allocation of two assets. This study proposes a machine learning (ML) approach to predict ORF ranges. To improve ML accuracy, pairs were categorized into three subgro...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Borsa Istanbul Review |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214845024001583 |
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| _version_ | 1832591806656675840 |
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| author | Mahmut Bağcı Pınar Kaya Soylu |
| author_facet | Mahmut Bağcı Pınar Kaya Soylu |
| author_sort | Mahmut Bağcı |
| collection | DOAJ |
| description | Selection of the optimal rebalancing frequency (ORF) is crucial for the pair trading algorithm (PTA) that periodically rebalances the allocation of two assets. This study proposes a machine learning (ML) approach to predict ORF ranges. To improve ML accuracy, pairs were categorized into three subgroups based on their correlation levels: positively, weakly, and negatively correlated. The statistical distribution of the ORF values is also presented. Accuracy scores show that random forest, logistic regression, and support vector classifiers perform competitively for the ORF range classification in both short- and long-term applications. The negatively correlated pairs showed the best classification performance, whereas the positively correlated pairs showed the lowest accuracy rate. Furthermore, the robustness of the proposed ML procedure is verified using a validation dataset, demonstrating the applicability of ORF range classification in practical exchange markets. |
| format | Article |
| id | doaj-art-ea8fd49abc0e419bb95b28b581605866 |
| institution | Kabale University |
| issn | 2214-8450 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Borsa Istanbul Review |
| spelling | doaj-art-ea8fd49abc0e419bb95b28b5816058662025-01-22T05:42:32ZengElsevierBorsa Istanbul Review2214-84502024-12-01248390Classification of the optimal rebalancing frequency for pairs trading using machine learning techniquesMahmut Bağcı0Pınar Kaya Soylu1Corresponding author.; Department of Management Information Systems, Marmara University, Maltepe, Istanbul, 34854, TürkiyeDepartment of Management Information Systems, Marmara University, Maltepe, Istanbul, 34854, TürkiyeSelection of the optimal rebalancing frequency (ORF) is crucial for the pair trading algorithm (PTA) that periodically rebalances the allocation of two assets. This study proposes a machine learning (ML) approach to predict ORF ranges. To improve ML accuracy, pairs were categorized into three subgroups based on their correlation levels: positively, weakly, and negatively correlated. The statistical distribution of the ORF values is also presented. Accuracy scores show that random forest, logistic regression, and support vector classifiers perform competitively for the ORF range classification in both short- and long-term applications. The negatively correlated pairs showed the best classification performance, whereas the positively correlated pairs showed the lowest accuracy rate. Furthermore, the robustness of the proposed ML procedure is verified using a validation dataset, demonstrating the applicability of ORF range classification in practical exchange markets.http://www.sciencedirect.com/science/article/pii/S2214845024001583Pairs tradingPortfolio rebalancingOptimal rebalancing frequency |
| spellingShingle | Mahmut Bağcı Pınar Kaya Soylu Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques Borsa Istanbul Review Pairs trading Portfolio rebalancing Optimal rebalancing frequency |
| title | Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques |
| title_full | Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques |
| title_fullStr | Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques |
| title_full_unstemmed | Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques |
| title_short | Classification of the optimal rebalancing frequency for pairs trading using machine learning techniques |
| title_sort | classification of the optimal rebalancing frequency for pairs trading using machine learning techniques |
| topic | Pairs trading Portfolio rebalancing Optimal rebalancing frequency |
| url | http://www.sciencedirect.com/science/article/pii/S2214845024001583 |
| work_keys_str_mv | AT mahmutbagcı classificationoftheoptimalrebalancingfrequencyforpairstradingusingmachinelearningtechniques AT pınarkayasoylu classificationoftheoptimalrebalancingfrequencyforpairstradingusingmachinelearningtechniques |