{"title":"风向标交易:对预测市场未来价格走势有影响的交易特征","authors":"Tejas Ramdas, Martin T. Wells","doi":"arxiv-2409.05192","DOIUrl":null,"url":null,"abstract":"In this study, we leverage powerful non-linear machine learning methods to\nidentify the characteristics of trades that contain valuable information.\nFirst, we demonstrate the effectiveness of our optimized neural network\npredictor in accurately predicting future market movements. Then, we utilize\nthe information from this successful neural network predictor to pinpoint the\nindividual trades within each data point (trading window) that had the most\nimpact on the optimized neural network's prediction of future price movements.\nThis approach helps us uncover important insights about the heterogeneity in\ninformation content provided by trades of different sizes, venues, trading\ncontexts, and over time.","PeriodicalId":501293,"journal":{"name":"arXiv - ECON - Econometrics","volume":"17 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2024-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets\",\"authors\":\"Tejas Ramdas, Martin T. Wells\",\"doi\":\"arxiv-2409.05192\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this study, we leverage powerful non-linear machine learning methods to\\nidentify the characteristics of trades that contain valuable information.\\nFirst, we demonstrate the effectiveness of our optimized neural network\\npredictor in accurately predicting future market movements. Then, we utilize\\nthe information from this successful neural network predictor to pinpoint the\\nindividual trades within each data point (trading window) that had the most\\nimpact on the optimized neural network's prediction of future price movements.\\nThis approach helps us uncover important insights about the heterogeneity in\\ninformation content provided by trades of different sizes, venues, trading\\ncontexts, and over time.\",\"PeriodicalId\":501293,\"journal\":{\"name\":\"arXiv - ECON - Econometrics\",\"volume\":\"17 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2024-09-08\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"arXiv - ECON - Econometrics\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/arxiv-2409.05192\",\"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 - ECON - Econometrics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/arxiv-2409.05192","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Bellwether Trades: Characteristics of Trades influential in Predicting Future Price Movements in Markets
In this study, we leverage powerful non-linear machine learning methods to
identify the characteristics of trades that contain valuable information.
First, we demonstrate the effectiveness of our optimized neural network
predictor in accurately predicting future market movements. Then, we utilize
the information from this successful neural network predictor to pinpoint the
individual trades within each data point (trading window) that had the most
impact on the optimized neural network's prediction of future price movements.
This approach helps us uncover important insights about the heterogeneity in
information content provided by trades of different sizes, venues, trading
contexts, and over time.