{"title":"利用 RNN 和 LSTM 对印度股市进行同步分析:基于阈值的分类方法","authors":"Sanjay Sathish, Charu C Sharma","doi":"arxiv-2409.06728","DOIUrl":null,"url":null,"abstract":"Our research presents a new approach for forecasting the synchronization of\nstock prices using machine learning and non-linear time-series analysis. To\ncapture the complex non-linear relationships between stock prices, we utilize\nrecurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By\ntransforming Cross Recurrence Plot (CRP) data into a time-series format, we\nenable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory\n(LSTM) networks for predicting stock price synchronization through both\nregression and classification. We apply this methodology to a dataset of 20\nhighly capitalized stocks from the Indian market over a 21-year period. The\nfindings reveal that our approach can predict stock price synchronization, with\nan accuracy of 0.98 and F1 score of 0.83 offering valuable insights for\ndeveloping effective trading strategies and risk management tools.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"2 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-08-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach\",\"authors\":\"Sanjay Sathish, Charu C Sharma\",\"doi\":\"arxiv-2409.06728\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Our research presents a new approach for forecasting the synchronization of\\nstock prices using machine learning and non-linear time-series analysis. To\\ncapture the complex non-linear relationships between stock prices, we utilize\\nrecurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By\\ntransforming Cross Recurrence Plot (CRP) data into a time-series format, we\\nenable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory\\n(LSTM) networks for predicting stock price synchronization through both\\nregression and classification. We apply this methodology to a dataset of 20\\nhighly capitalized stocks from the Indian market over a 21-year period. The\\nfindings reveal that our approach can predict stock price synchronization, with\\nan accuracy of 0.98 and F1 score of 0.83 offering valuable insights for\\ndeveloping effective trading strategies and risk management tools.\",\"PeriodicalId\":501139,\"journal\":{\"name\":\"arXiv - QuantFin - Statistical Finance\",\"volume\":\"2 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-08-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - QuantFin - Statistical Finance\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.06728\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"arXiv - QuantFin - Statistical Finance","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.06728","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Leveraging RNNs and LSTMs for Synchronization Analysis in the Indian Stock Market: A Threshold-Based Classification Approach
Our research presents a new approach for forecasting the synchronization of
stock prices using machine learning and non-linear time-series analysis. To
capture the complex non-linear relationships between stock prices, we utilize
recurrence plots (RP) and cross-recurrence quantification analysis (CRQA). By
transforming Cross Recurrence Plot (CRP) data into a time-series format, we
enable the use of Recurrent Neural Networks (RNN) and Long Short-Term Memory
(LSTM) networks for predicting stock price synchronization through both
regression and classification. We apply this methodology to a dataset of 20
highly capitalized stocks from the Indian market over a 21-year period. The
findings reveal that our approach can predict stock price synchronization, with
an accuracy of 0.98 and F1 score of 0.83 offering valuable insights for
developing effective trading strategies and risk management tools.