{"title":"MambaStock:用于股票预测的选择性状态空间模型","authors":"Zhuangwei Shi","doi":"arxiv-2402.18959","DOIUrl":null,"url":null,"abstract":"The stock market plays a pivotal role in economic development, yet its\nintricate volatility poses challenges for investors. Consequently, research and\naccurate predictions of stock price movements are crucial for mitigating risks.\nTraditional time series models fall short in capturing nonlinearity, leading to\nunsatisfactory stock predictions. This limitation has spurred the widespread\nadoption of neural networks for stock prediction, owing to their robust\nnonlinear generalization capabilities. Recently, Mamba, a structured state\nspace sequence model with a selection mechanism and scan module (S6), has\nemerged as a powerful tool in sequence modeling tasks. Leveraging this\nframework, this paper proposes a novel Mamba-based model for stock price\nprediction, named MambaStock. The proposed MambaStock model effectively mines\nhistorical stock market data to predict future stock prices without handcrafted\nfeatures or extensive preprocessing procedures. Empirical studies on several\nstocks indicate that the MambaStock model outperforms previous methods,\ndelivering highly accurate predictions. This enhanced accuracy can assist\ninvestors and institutions in making informed decisions, aiming to maximize\nreturns while minimizing risks. This work underscores the value of Mamba in\ntime-series forecasting. Source code is available at\nhttps://github.com/zshicode/MambaStock.","PeriodicalId":501139,"journal":{"name":"arXiv - QuantFin - Statistical Finance","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-02-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"MambaStock: Selective state space model for stock prediction\",\"authors\":\"Zhuangwei Shi\",\"doi\":\"arxiv-2402.18959\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The stock market plays a pivotal role in economic development, yet its\\nintricate volatility poses challenges for investors. Consequently, research and\\naccurate predictions of stock price movements are crucial for mitigating risks.\\nTraditional time series models fall short in capturing nonlinearity, leading to\\nunsatisfactory stock predictions. This limitation has spurred the widespread\\nadoption of neural networks for stock prediction, owing to their robust\\nnonlinear generalization capabilities. Recently, Mamba, a structured state\\nspace sequence model with a selection mechanism and scan module (S6), has\\nemerged as a powerful tool in sequence modeling tasks. Leveraging this\\nframework, this paper proposes a novel Mamba-based model for stock price\\nprediction, named MambaStock. The proposed MambaStock model effectively mines\\nhistorical stock market data to predict future stock prices without handcrafted\\nfeatures or extensive preprocessing procedures. Empirical studies on several\\nstocks indicate that the MambaStock model outperforms previous methods,\\ndelivering highly accurate predictions. This enhanced accuracy can assist\\ninvestors and institutions in making informed decisions, aiming to maximize\\nreturns while minimizing risks. This work underscores the value of Mamba in\\ntime-series forecasting. Source code is available at\\nhttps://github.com/zshicode/MambaStock.\",\"PeriodicalId\":501139,\"journal\":{\"name\":\"arXiv - QuantFin - Statistical Finance\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-02-29\",\"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-2402.18959\",\"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-2402.18959","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
MambaStock: Selective state space model for stock prediction
The stock market plays a pivotal role in economic development, yet its
intricate volatility poses challenges for investors. Consequently, research and
accurate predictions of stock price movements are crucial for mitigating risks.
Traditional time series models fall short in capturing nonlinearity, leading to
unsatisfactory stock predictions. This limitation has spurred the widespread
adoption of neural networks for stock prediction, owing to their robust
nonlinear generalization capabilities. Recently, Mamba, a structured state
space sequence model with a selection mechanism and scan module (S6), has
emerged as a powerful tool in sequence modeling tasks. Leveraging this
framework, this paper proposes a novel Mamba-based model for stock price
prediction, named MambaStock. The proposed MambaStock model effectively mines
historical stock market data to predict future stock prices without handcrafted
features or extensive preprocessing procedures. Empirical studies on several
stocks indicate that the MambaStock model outperforms previous methods,
delivering highly accurate predictions. This enhanced accuracy can assist
investors and institutions in making informed decisions, aiming to maximize
returns while minimizing risks. This work underscores the value of Mamba in
time-series forecasting. Source code is available at
https://github.com/zshicode/MambaStock.