A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach
Abstract This paper addresses the problem of designing an efficient platform for pairs-trading implementation in real time. Capturing the stylised features of a spread process, i.e., the evolution of the differential between the returns from a pair of stocks, exhibiting a heavy-tailed mean-reverting...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2017-12-01
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| Series: | Journal of Big Data |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s40537-017-0106-3 |
| _version_ | 1798044863962284032 |
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| author | Anton Tenyakov Rogemar Mamon |
| author_facet | Anton Tenyakov Rogemar Mamon |
| author_sort | Anton Tenyakov |
| collection | DOAJ |
| description | Abstract This paper addresses the problem of designing an efficient platform for pairs-trading implementation in real time. Capturing the stylised features of a spread process, i.e., the evolution of the differential between the returns from a pair of stocks, exhibiting a heavy-tailed mean-reverting process is also dealt with. Likewise, the optimal recovery of time-varying parameters in a return-spread model is tackled. It is important to solve such issues in an integrated manner to carry out the execution of trading strategies in a dynamic market environment. The Kalman and hidden Markov model (HMM) multi-regime dynamic filtering approaches are fused together to provide a powerful method for pairs-trading actualisation. Practitioners’ considerations are taken into account in the way the new filtering method is automated. The synthesis of the HMM’s expectation–maximisation algorithm and Kalman filtering procedure gives rise to a set of self-updating optimal parameter estimates. The method put forward in this paper is a hybridisation of signal-processing algorithms. It highlights the critical role and beneficial utility of data fusion methods. Its appropriateness and novelty support the advancements of accurate predictive analytics involving big financial data sets. The algorithm’s performance is tested on historical return spread between Coca-Cola and Pepsi Inc.’s equities. Through a back-testing trade, a hypothetical trader might earn a non-zero profit under the assumption of no transaction costs and bid-ask spreads. The method’s success is illustrated by a trading simulation. The findings from this work show that there is high potential to gain when the transaction fees are low, and an investor is able to benefit from the proposed interplay of the two filtering methods. |
| first_indexed | 2024-04-11T23:11:06Z |
| format | Article |
| id | doaj.art-980677f9a46e4dc0926dd5072f93b0e8 |
| institution | Directory Open Access Journal |
| issn | 2196-1115 |
| language | English |
| last_indexed | 2024-04-11T23:11:06Z |
| publishDate | 2017-12-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Journal of Big Data |
| spelling | doaj.art-980677f9a46e4dc0926dd5072f93b0e82022-12-22T03:57:51ZengSpringerOpenJournal of Big Data2196-11152017-12-014112010.1186/s40537-017-0106-3A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approachAnton Tenyakov0Rogemar Mamon1Treasury Department, TD Bank GroupDepartment of Statistical and Actuarial Sciences, University of Western OntarioAbstract This paper addresses the problem of designing an efficient platform for pairs-trading implementation in real time. Capturing the stylised features of a spread process, i.e., the evolution of the differential between the returns from a pair of stocks, exhibiting a heavy-tailed mean-reverting process is also dealt with. Likewise, the optimal recovery of time-varying parameters in a return-spread model is tackled. It is important to solve such issues in an integrated manner to carry out the execution of trading strategies in a dynamic market environment. The Kalman and hidden Markov model (HMM) multi-regime dynamic filtering approaches are fused together to provide a powerful method for pairs-trading actualisation. Practitioners’ considerations are taken into account in the way the new filtering method is automated. The synthesis of the HMM’s expectation–maximisation algorithm and Kalman filtering procedure gives rise to a set of self-updating optimal parameter estimates. The method put forward in this paper is a hybridisation of signal-processing algorithms. It highlights the critical role and beneficial utility of data fusion methods. Its appropriateness and novelty support the advancements of accurate predictive analytics involving big financial data sets. The algorithm’s performance is tested on historical return spread between Coca-Cola and Pepsi Inc.’s equities. Through a back-testing trade, a hypothetical trader might earn a non-zero profit under the assumption of no transaction costs and bid-ask spreads. The method’s success is illustrated by a trading simulation. The findings from this work show that there is high potential to gain when the transaction fees are low, and an investor is able to benefit from the proposed interplay of the two filtering methods.http://link.springer.com/article/10.1186/s40537-017-0106-3algorithm fusioninvestmentfinancial signal processingchange of measureOrnstein–Uhlenbeck process |
| spellingShingle | Anton Tenyakov Rogemar Mamon A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach Journal of Big Data algorithm fusion investment financial signal processing change of measure Ornstein–Uhlenbeck process |
| title | A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach |
| title_full | A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach |
| title_fullStr | A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach |
| title_full_unstemmed | A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach |
| title_short | A computing platform for pairs-trading online implementation via a blended Kalman-HMM filtering approach |
| title_sort | computing platform for pairs trading online implementation via a blended kalman hmm filtering approach |
| topic | algorithm fusion investment financial signal processing change of measure Ornstein–Uhlenbeck process |
| url | http://link.springer.com/article/10.1186/s40537-017-0106-3 |
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