{"title":"预测金融文本情绪:一个实证检验","authors":"Ruchi Kejriwal, M. Garg, Gaurav Sarin","doi":"10.1108/xjm-06-2022-0148","DOIUrl":null,"url":null,"abstract":"\nPurpose\nStock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.\n\n\nDesign/methodology/approach\nThe research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.\n\n\nFindings\nOut of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.\n\n\nOriginality/value\nThis study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.\n","PeriodicalId":34603,"journal":{"name":"Vilakshan XIMB Journal of Management","volume":"8 1","pages":""},"PeriodicalIF":0.0000,"publicationDate":"2022-11-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Predict financial text sentiment: an empirical examination\",\"authors\":\"Ruchi Kejriwal, M. Garg, Gaurav Sarin\",\"doi\":\"10.1108/xjm-06-2022-0148\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\nPurpose\\nStock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.\\n\\n\\nDesign/methodology/approach\\nThe research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.\\n\\n\\nFindings\\nOut of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.\\n\\n\\nOriginality/value\\nThis study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.\\n\",\"PeriodicalId\":34603,\"journal\":{\"name\":\"Vilakshan XIMB Journal of Management\",\"volume\":\"8 1\",\"pages\":\"\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-11-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Vilakshan XIMB Journal of Management\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1108/xjm-06-2022-0148\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Vilakshan XIMB Journal of Management","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1108/xjm-06-2022-0148","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Predict financial text sentiment: an empirical examination
Purpose
Stock market has always been lucrative for various investors. But, because of its speculative nature, it is difficult to predict the price movement. Investors have been using both fundamental and technical analysis to predict the prices. Fundamental analysis helps to study structured data of the company. Technical analysis helps to study price trends, and with the increasing and easy availability of unstructured data have made it important to study the market sentiment. Market sentiment has a major impact on the prices in short run. Hence, the purpose is to understand the market sentiment timely and effectively.
Design/methodology/approach
The research includes text mining and then creating various models for classification. The accuracy of these models is checked using confusion matrix.
Findings
Out of the six machine learning techniques used to create the classification model, kernel support vector machine gave the highest accuracy of 68%. This model can be now used to analyse the tweets, news and various other unstructured data to predict the price movement.
Originality/value
This study will help investors classify a news or a tweet into “positive”, “negative” or “neutral” quickly and determine the stock price trends.