Shubham Agrawal, Nitin Kumar, Geetanjali Rathee, Chaker Abdelaziz Kerrache, Carlos T. Calafate, Muhammad Bilal
{"title":"利用情感和技术分析提高股市预测准确性","authors":"Shubham Agrawal, Nitin Kumar, Geetanjali Rathee, Chaker Abdelaziz Kerrache, Carlos T. Calafate, Muhammad Bilal","doi":"10.1007/s10660-024-09874-x","DOIUrl":null,"url":null,"abstract":"<p>The utilization of sentiment analysis as a method for predicting stock market trends has gained significant attention recently, especially during economic crises. This research aims to assess the predictive accuracy of sentiment analysis in the stock market by constructing a reinforced model that integrates both sentiment and technical analysis. While prior studies have concentrated on social media sentiment for stock price prediction, this research introduces an enhanced model that combines sentiment analysis with technical indicators to improve the precision of stock market prediction. The study creates and evaluates predictive models for stock prices and trends using a substantial dataset of tweets from twenty prominent companies. Finally the re-enforced model has been developed and tested on the stock prices of: Apple, General Electric, Ford Motors and Amazon. The deliberate selection of these companies, each representing distinct industry sectors, serves a dual purpose. It not only facilitates a practical evaluation of our model across diverse market conditions but also ensures computational feasibility, allowing for a focused and detailed analysis of the model’s predictive accuracy and reliability in various economic landscapes. The study’s outcomes offer valuable insights into the effectiveness of the reinforced model, which combines sentiment and technical analysis to predict stock market movements, providing a more comprehensive approach to understanding market sentiment’s influence on stock prices. Furthermore, these findings contribute to the existing knowledge on stock market prediction techniques and emphasize the importance of considering multiple factors in decision-making.\n</p>","PeriodicalId":47264,"journal":{"name":"Electronic Commerce Research","volume":"21 1","pages":""},"PeriodicalIF":3.7000,"publicationDate":"2024-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improving stock market prediction accuracy using sentiment and technical analysis\",\"authors\":\"Shubham Agrawal, Nitin Kumar, Geetanjali Rathee, Chaker Abdelaziz Kerrache, Carlos T. Calafate, Muhammad Bilal\",\"doi\":\"10.1007/s10660-024-09874-x\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>The utilization of sentiment analysis as a method for predicting stock market trends has gained significant attention recently, especially during economic crises. This research aims to assess the predictive accuracy of sentiment analysis in the stock market by constructing a reinforced model that integrates both sentiment and technical analysis. While prior studies have concentrated on social media sentiment for stock price prediction, this research introduces an enhanced model that combines sentiment analysis with technical indicators to improve the precision of stock market prediction. The study creates and evaluates predictive models for stock prices and trends using a substantial dataset of tweets from twenty prominent companies. Finally the re-enforced model has been developed and tested on the stock prices of: Apple, General Electric, Ford Motors and Amazon. The deliberate selection of these companies, each representing distinct industry sectors, serves a dual purpose. It not only facilitates a practical evaluation of our model across diverse market conditions but also ensures computational feasibility, allowing for a focused and detailed analysis of the model’s predictive accuracy and reliability in various economic landscapes. The study’s outcomes offer valuable insights into the effectiveness of the reinforced model, which combines sentiment and technical analysis to predict stock market movements, providing a more comprehensive approach to understanding market sentiment’s influence on stock prices. Furthermore, these findings contribute to the existing knowledge on stock market prediction techniques and emphasize the importance of considering multiple factors in decision-making.\\n</p>\",\"PeriodicalId\":47264,\"journal\":{\"name\":\"Electronic Commerce Research\",\"volume\":\"21 1\",\"pages\":\"\"},\"PeriodicalIF\":3.7000,\"publicationDate\":\"2024-06-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Electronic Commerce Research\",\"FirstCategoryId\":\"91\",\"ListUrlMain\":\"https://doi.org/10.1007/s10660-024-09874-x\",\"RegionNum\":4,\"RegionCategory\":\"管理学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q2\",\"JCRName\":\"BUSINESS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electronic Commerce Research","FirstCategoryId":"91","ListUrlMain":"https://doi.org/10.1007/s10660-024-09874-x","RegionNum":4,"RegionCategory":"管理学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"BUSINESS","Score":null,"Total":0}
Improving stock market prediction accuracy using sentiment and technical analysis
The utilization of sentiment analysis as a method for predicting stock market trends has gained significant attention recently, especially during economic crises. This research aims to assess the predictive accuracy of sentiment analysis in the stock market by constructing a reinforced model that integrates both sentiment and technical analysis. While prior studies have concentrated on social media sentiment for stock price prediction, this research introduces an enhanced model that combines sentiment analysis with technical indicators to improve the precision of stock market prediction. The study creates and evaluates predictive models for stock prices and trends using a substantial dataset of tweets from twenty prominent companies. Finally the re-enforced model has been developed and tested on the stock prices of: Apple, General Electric, Ford Motors and Amazon. The deliberate selection of these companies, each representing distinct industry sectors, serves a dual purpose. It not only facilitates a practical evaluation of our model across diverse market conditions but also ensures computational feasibility, allowing for a focused and detailed analysis of the model’s predictive accuracy and reliability in various economic landscapes. The study’s outcomes offer valuable insights into the effectiveness of the reinforced model, which combines sentiment and technical analysis to predict stock market movements, providing a more comprehensive approach to understanding market sentiment’s influence on stock prices. Furthermore, these findings contribute to the existing knowledge on stock market prediction techniques and emphasize the importance of considering multiple factors in decision-making.
期刊介绍:
The Internet and the World Wide Web have brought a fundamental change in the way that individuals access data, information and services. Individuals have access to vast amounts of data, to experts and services that are not limited in time or space. This has forced business to change the way in which they conduct their commercial transactions with their end customers and with other businesses, resulting in the development of a global market through the Internet. The emergence of the Internet and electronic commerce raises many new research issues. The Electronic Commerce Research journal will serve as a forum for stimulating and disseminating research into all facets of electronic commerce - from research into core enabling technologies to work on assessing and understanding the implications of these technologies on societies, economies, businesses and individuals. The journal concentrates on theoretical as well as empirical research that leads to better understanding of electronic commerce and its implications. Topics covered by the journal include, but are not restricted to the following subjects as they relate to the Internet and electronic commerce: Dissemination of services through the Internet;Intelligent agents technologies and their impact;The global impact of electronic commerce;The economics of electronic commerce;Fraud reduction on the Internet;Mobile electronic commerce;Virtual electronic commerce systems;Application of computer and communication technologies to electronic commerce;Electronic market mechanisms and their impact;Auctioning over the Internet;Business models of Internet based companies;Service creation and provisioning;The job market created by the Internet and electronic commerce;Security, privacy, authorization and authentication of users and transactions on the Internet;Electronic data interc hange over the Internet;Electronic payment systems and electronic funds transfer;The impact of electronic commerce on organizational structures and processes;Supply chain management through the Internet;Marketing on the Internet;User adaptive advertisement;Standards in electronic commerce and their analysis;Metrics, measurement and prediction of user activity;On-line stock markets and financial trading;User devices for accessing the Internet and conducting electronic transactions;Efficient search techniques and engines on the WWW;Web based languages (e.g., HTML, XML, VRML, Java);Multimedia storage and distribution;Internet;Collaborative learning, gaming and work;Presentation page design techniques and tools;Virtual reality on the net and 3D visualization;Browsers and user interfaces;Web site management techniques and tools;Managing middleware to support electronic commerce;Web based education, and training;Electronic journals and publishing on the Internet;Legal issues, taxation and property rights;Modeling and design of networks to support Internet applications;Modeling, design and sizing of web site servers;Reliability of intensive on-line applications;Pervasive devices and pervasive computing in electronic commerce;Workflow for electronic commerce applications;Coordination technologies for electronic commerce;Personalization and mass customization technologies;Marketing and customer relationship management in electronic commerce;Service creation and provisioning. Audience: Academics and professionals involved in electronic commerce research and the application and use of the Internet. Managers, consultants, decision-makers and developers who value the use of electronic com merce research results. Special Issues: Electronic Commerce Research publishes from time to time a special issue of the devoted to a single subject area. If interested in serving as a guest editor for a special issue, please contact the Editor-in-Chief J. Christopher Westland at westland@uic.edu with a proposal for the special issue. Officially cited as: Electron Commer Res