{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/stock-trend-prediction-using-news-sentiment","title":"Stock trend prediction using news sentiment analysis","arxiv_id":"1607.01958","date":"2016-07-07","proceeding":null,"authors":["Joshi Kalyani","Prof. H. N. Bharathi","Prof. Rao Jyothi"],"abstract":"Efficient Market Hypothesis is the popular theory about stock prediction.\nWith its failure much research has been carried in the area of prediction of\nstocks. This project is about taking non quantifiable data such as financial\nnews articles about a company and predicting its future stock trend with news\nsentiment classification. Assuming that news articles have impact on stock\nmarket, this is an attempt to study relationship between news and stock trend.\nTo show this, we created three different classification models which depict\npolarity of news articles being positive or negative. Observations show that RF\nand SVM perform well in all types of testing. Na\\\"ive Bayes gives good result\nbut not compared to the other two. Experiments are conducted to evaluate\nvarious aspects of the proposed model and encouraging results are obtained in\nall of the experiments. The accuracy of the prediction model is more than 80%\nand in comparison with news random labeling with 50% of accuracy; the model has\nincreased the accuracy by 30%.","url_abs":"http://arxiv.org/abs/1607.01958v1","url_pdf":"http://arxiv.org/pdf/1607.01958v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"stock-trend-prediction-using-news-sentiment","repo_url":"https://github.com/diablordking/TheWolfofWallStreet","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"none","reach":null},{"paper_slug":"stock-trend-prediction-using-news-sentiment","repo_url":"https://github.com/nicklamonica/stock-sentiment-analysis","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"articles","task_name":"Articles"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"sentiment-analysis","task_name":"Sentiment Analysis"},{"task_slug":"sentiment-classification","task_name":"Sentiment Classification"},{"task_slug":"stock-prediction","task_name":"Stock Prediction"},{"task_slug":"stock-trend-prediction","task_name":"Stock Trend Prediction"}],"methods":[{"method_slug":"svm","method_name":"SVM"}],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"syntology_url":"https://syntology.ai/paper/1607.01958","atlas_url":"https://app.syntology.ai/?focus=1607.01958","mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}