{"title":"基于深度学习的量化投资策略分析与应用研究","authors":"","doi":"10.25236/ajcis.2023.061004","DOIUrl":null,"url":null,"abstract":"Due to the dynamics and complexity of the stock market, stock prediction models may encounter some challenges in predicting future stock movements, resulting in their poor generalisation ability. This paper discusses the application and effectiveness of deep learning technology in the financial field by studying the quantitative investment strategy based on deep learning. First, theoretical foundations of deep learning are introduced. Then, the methods for constructing quantitative investment strategies based on Long Short-Term Memory Network (LSTM) are elaborated, including data preprocessing, model selection and training, and strategy execution. Next, the performance and stability of the strategy are evaluated through backtesting and empirical analysis of historical data. Finally, the research results are summarized, and the direction of further research and application is prospected.","PeriodicalId":387664,"journal":{"name":"Academic Journal of Computing & Information Science","volume":"161 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2023-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Research on analysis and application of quantitative investment strategies based on deep learning\",\"authors\":\"\",\"doi\":\"10.25236/ajcis.2023.061004\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Due to the dynamics and complexity of the stock market, stock prediction models may encounter some challenges in predicting future stock movements, resulting in their poor generalisation ability. This paper discusses the application and effectiveness of deep learning technology in the financial field by studying the quantitative investment strategy based on deep learning. First, theoretical foundations of deep learning are introduced. Then, the methods for constructing quantitative investment strategies based on Long Short-Term Memory Network (LSTM) are elaborated, including data preprocessing, model selection and training, and strategy execution. Next, the performance and stability of the strategy are evaluated through backtesting and empirical analysis of historical data. Finally, the research results are summarized, and the direction of further research and application is prospected.\",\"PeriodicalId\":387664,\"journal\":{\"name\":\"Academic Journal of Computing & Information Science\",\"volume\":\"161 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2023-01-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Academic Journal of Computing & Information Science\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.25236/ajcis.2023.061004\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Academic Journal of Computing & Information Science","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.25236/ajcis.2023.061004","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Research on analysis and application of quantitative investment strategies based on deep learning
Due to the dynamics and complexity of the stock market, stock prediction models may encounter some challenges in predicting future stock movements, resulting in their poor generalisation ability. This paper discusses the application and effectiveness of deep learning technology in the financial field by studying the quantitative investment strategy based on deep learning. First, theoretical foundations of deep learning are introduced. Then, the methods for constructing quantitative investment strategies based on Long Short-Term Memory Network (LSTM) are elaborated, including data preprocessing, model selection and training, and strategy execution. Next, the performance and stability of the strategy are evaluated through backtesting and empirical analysis of historical data. Finally, the research results are summarized, and the direction of further research and application is prospected.