Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm
Abstract The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-01-01
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| Series: | Financial Innovation |
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| Online Access: | https://doi.org/10.1186/s40854-024-00729-w |
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| author | Montasser Ghachem Oguz Ersan |
| author_facet | Montasser Ghachem Oguz Ersan |
| author_sort | Montasser Ghachem |
| collection | DOAJ |
| description | Abstract The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can serve as an alternative to the existing methods for estimating PIN models. Our method provides optimal estimates for the original PIN model as well as two of its extensions: the multilayer PIN model and the adjusted PIN model, along with its restricted versions. Our results indicate that estimations using the ECM algorithm are generally faster, more accurate, and more memory-efficient than the standard methods used in the literature, making it a robust alternative. More importantly, the ECM algorithm is not limited to the models discussed and can be easily adapted to estimate future extensions of the PIN model. |
| format | Article |
| id | doaj-art-b638e486f24e4ed59fb0c12c7c4fb433 |
| institution | Kabale University |
| issn | 2199-4730 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj-art-b638e486f24e4ed59fb0c12c7c4fb4332025-01-26T12:48:41ZengSpringerOpenFinancial Innovation2199-47302025-01-0111113710.1186/s40854-024-00729-wEstimation of the probability of informed trading models via an expectation-conditional maximization algorithmMontasser Ghachem0Oguz Ersan1Department of Economics, Stockholm UniversityInternational Trade and Finance Department, Faculty of Economics, Administrative and Social Sciences, Kadir Has UniversityAbstract The estimation of the probability of informed trading (PIN) model and its extensions poses significant challenges owing to various computational problems. To address these issues, we propose a novel estimation method called the expectation-conditional-maximization (ECM) algorithm, which can serve as an alternative to the existing methods for estimating PIN models. Our method provides optimal estimates for the original PIN model as well as two of its extensions: the multilayer PIN model and the adjusted PIN model, along with its restricted versions. Our results indicate that estimations using the ECM algorithm are generally faster, more accurate, and more memory-efficient than the standard methods used in the literature, making it a robust alternative. More importantly, the ECM algorithm is not limited to the models discussed and can be easily adapted to estimate future extensions of the PIN model.https://doi.org/10.1186/s40854-024-00729-wExpectation conditional-maximization algorithmECMPIN modelMPINMultilayer probability of informed tradingAdjusted PIN model |
| spellingShingle | Montasser Ghachem Oguz Ersan Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm Financial Innovation Expectation conditional-maximization algorithm ECM PIN model MPIN Multilayer probability of informed trading Adjusted PIN model |
| title | Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm |
| title_full | Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm |
| title_fullStr | Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm |
| title_full_unstemmed | Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm |
| title_short | Estimation of the probability of informed trading models via an expectation-conditional maximization algorithm |
| title_sort | estimation of the probability of informed trading models via an expectation conditional maximization algorithm |
| topic | Expectation conditional-maximization algorithm ECM PIN model MPIN Multilayer probability of informed trading Adjusted PIN model |
| url | https://doi.org/10.1186/s40854-024-00729-w |
| work_keys_str_mv | AT montasserghachem estimationoftheprobabilityofinformedtradingmodelsviaanexpectationconditionalmaximizationalgorithm AT oguzersan estimationoftheprobabilityofinformedtradingmodelsviaanexpectationconditionalmaximizationalgorithm |