Pranjala G. Kolapwar;Uday V. Kulkarni;Jaishri M. Waghmare
{"title":"基于行业的配对交易策略与新配对选择技术","authors":"Pranjala G. Kolapwar;Uday V. Kulkarni;Jaishri M. Waghmare","doi":"10.1109/TAI.2024.3433469","DOIUrl":null,"url":null,"abstract":"A pair trading strategy (PTS) is a balanced approach that involves simultaneous trading of two highly correlated stocks. This article introduces the PTS-return-based pair selection (PTS-R) strategy which is the modification of the traditional PTS. The PTS-R follows a similar framework to the traditional PTS, differing only in the criteria it employs for selecting stock pairs. Moreover, this article proposes a novel trading strategy called sector-based pairs trading strategy (SBPTS) along with its two variants, namely SBPTS-correlation-based pair selection (SBPTS-C) and SBPTS-return-based pair selection (SBPTS-R). The SBPTS focuses on the pairs of stocks within the same sector. It consists of three innovative phases: the classification of input stocks into the respective sectors, the identification of the best-performing sector, and the selection of stock pairs based on their returns. The goal is to identify the pairs with a strong historical correlation and the highest returns within the best-performing sector. These chosen pairs are then used for trading. The strategies are designed to enhance the efficacy of the pairs trading and are validated through experimentation on real-world stock data over a ten-year historical period from 2013 to 2023. The results demonstrate their effectiveness compared to the existing techniques for pair selection and trading strategy.","PeriodicalId":73305,"journal":{"name":"IEEE transactions on artificial intelligence","volume":"6 1","pages":"3-13"},"PeriodicalIF":0.0000,"publicationDate":"2024-07-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Sector-Based Pairs Trading Strategy With Novel Pair Selection Technique\",\"authors\":\"Pranjala G. Kolapwar;Uday V. Kulkarni;Jaishri M. Waghmare\",\"doi\":\"10.1109/TAI.2024.3433469\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"A pair trading strategy (PTS) is a balanced approach that involves simultaneous trading of two highly correlated stocks. This article introduces the PTS-return-based pair selection (PTS-R) strategy which is the modification of the traditional PTS. The PTS-R follows a similar framework to the traditional PTS, differing only in the criteria it employs for selecting stock pairs. Moreover, this article proposes a novel trading strategy called sector-based pairs trading strategy (SBPTS) along with its two variants, namely SBPTS-correlation-based pair selection (SBPTS-C) and SBPTS-return-based pair selection (SBPTS-R). The SBPTS focuses on the pairs of stocks within the same sector. It consists of three innovative phases: the classification of input stocks into the respective sectors, the identification of the best-performing sector, and the selection of stock pairs based on their returns. The goal is to identify the pairs with a strong historical correlation and the highest returns within the best-performing sector. These chosen pairs are then used for trading. The strategies are designed to enhance the efficacy of the pairs trading and are validated through experimentation on real-world stock data over a ten-year historical period from 2013 to 2023. The results demonstrate their effectiveness compared to the existing techniques for pair selection and trading strategy.\",\"PeriodicalId\":73305,\"journal\":{\"name\":\"IEEE transactions on artificial intelligence\",\"volume\":\"6 1\",\"pages\":\"3-13\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-07-25\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"IEEE transactions on artificial intelligence\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://ieeexplore.ieee.org/document/10609738/\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE transactions on artificial intelligence","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10609738/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Sector-Based Pairs Trading Strategy With Novel Pair Selection Technique
A pair trading strategy (PTS) is a balanced approach that involves simultaneous trading of two highly correlated stocks. This article introduces the PTS-return-based pair selection (PTS-R) strategy which is the modification of the traditional PTS. The PTS-R follows a similar framework to the traditional PTS, differing only in the criteria it employs for selecting stock pairs. Moreover, this article proposes a novel trading strategy called sector-based pairs trading strategy (SBPTS) along with its two variants, namely SBPTS-correlation-based pair selection (SBPTS-C) and SBPTS-return-based pair selection (SBPTS-R). The SBPTS focuses on the pairs of stocks within the same sector. It consists of three innovative phases: the classification of input stocks into the respective sectors, the identification of the best-performing sector, and the selection of stock pairs based on their returns. The goal is to identify the pairs with a strong historical correlation and the highest returns within the best-performing sector. These chosen pairs are then used for trading. The strategies are designed to enhance the efficacy of the pairs trading and are validated through experimentation on real-world stock data over a ten-year historical period from 2013 to 2023. The results demonstrate their effectiveness compared to the existing techniques for pair selection and trading strategy.