{"title":"Exploring Candlesticks and Multi-Time Windows for Forecasting Stock-Index Movements","authors":"Kanghyeon Seo, Jihoon Yang","doi":"10.1145/3555776.3577604","DOIUrl":null,"url":null,"abstract":"Stock-index movement prediction is an important research topic in FinTech because the index indicates the economic status of a whole country. With a set of daily candlesticks of the stock-index, investors could gain a meaningful basis for the prediction of the next day's movement. This paper proposes a stock-index price-movement prediction model, Combined Time-View TabNet (CTV-TabNet), a novel approach that utilizes attributes of the candlesticks data with multi-time windows. Our model comprises three modules: TabNet encoder, gated recurrent unit with a sequence control, and multi-time combiner. They work together to forecast the movements based on the sequential attributes of the candlesticks. CTV-TabNet not only outperforms baseline models in prediction performance on 20 stock-indices of 14 different countries but also yields higher returns of index-futures trading simulations when compared to the baselines. Additionally, our model provides comprehensive interpretations of the stock-index related to its inherent properties in predictive performance.","PeriodicalId":42971,"journal":{"name":"Applied Computing Review","volume":null,"pages":null},"PeriodicalIF":0.4000,"publicationDate":"2023-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Applied Computing Review","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1145/3555776.3577604","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
Stock-index movement prediction is an important research topic in FinTech because the index indicates the economic status of a whole country. With a set of daily candlesticks of the stock-index, investors could gain a meaningful basis for the prediction of the next day's movement. This paper proposes a stock-index price-movement prediction model, Combined Time-View TabNet (CTV-TabNet), a novel approach that utilizes attributes of the candlesticks data with multi-time windows. Our model comprises three modules: TabNet encoder, gated recurrent unit with a sequence control, and multi-time combiner. They work together to forecast the movements based on the sequential attributes of the candlesticks. CTV-TabNet not only outperforms baseline models in prediction performance on 20 stock-indices of 14 different countries but also yields higher returns of index-futures trading simulations when compared to the baselines. Additionally, our model provides comprehensive interpretations of the stock-index related to its inherent properties in predictive performance.