{"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-movement-prediction-from-tweets-and","title":"Stock Movement Prediction from Tweets and Historical Prices","arxiv_id":null,"date":"2018-07-01","proceeding":"ACL 2018 7","authors":["Yumo Xu","Shay B. Cohen"],"abstract":"Stock movement prediction is a challenging problem: the market is highly stochastic, and we make temporally-dependent predictions from chaotic data. We treat these three complexities and present a novel deep generative model jointly exploiting text and price signals for this task. Unlike the case with discriminative or topic modeling, our model introduces recurrent, continuous latent variables for a better treatment of stochasticity, and uses neural variational inference to address the intractable posterior inference. We also provide a hybrid objective with temporal auxiliary to flexibly capture predictive dependencies. We demonstrate the state-of-the-art performance of our proposed model on a new stock movement prediction dataset which we collected.","url_abs":"https://aclanthology.org/P18-1183","url_pdf":"https://aclanthology.org/P18-1183.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-movement-prediction-from-tweets-and","repo_url":"https://github.com/yumoxu/stocknet-dataset","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"feature-engineering","task_name":"Feature Engineering"},{"task_slug":"prediction","task_name":"Prediction"},{"task_slug":"stock-market-prediction","task_name":"Stock Market Prediction"},{"task_slug":"stock-trend-prediction","task_name":"Stock Trend Prediction"},{"task_slug":"time-series","task_name":"Time Series Analysis"},{"task_slug":"variational-inference","task_name":"Variational Inference"}],"methods":[],"datasets_introduced":[{"slug":"stocknet","name":"stocknet","full_name":"stocknet-dataset"}],"methods_introduced":[],"results":[{"leaderboard":"/sota/stock-market-prediction-on-astock","task":"Stock Market Prediction","dataset":"Astock","model":"StockNet","rank_in_archive_order":17,"of":17,"metrics":{"Accuray":"46.72","F1-score":"44.44","Precision":"47.65","Recall":"46.68"},"uses_additional_data":false},{"leaderboard":"/sota/stock-market-prediction-on-stocknet","task":"Stock Market Prediction","dataset":"stocknet","model":"StockNet","rank_in_archive_order":3,"of":5,"metrics":{"F1":"0.575"},"uses_additional_data":true}],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}