{"url":"/dataset/stocknet","name":"stocknet","full_name":"stocknet-dataset","description_markdown":"# stocknet-dataset\r\n\r\nThis repository releases a comprehensive dataset for stock movement prediction from tweets and historical stock prices. Please cite the following paper [[bib](https://aclanthology.info/papers/P18-1183/p18-1183.bib)] if you use this dataset,  \r\n\r\nYumo Xu and Shay B. Cohen. 2018. [Stock Movement Prediction from Tweets and Historical Prices](http://aclweb.org/anthology/P18-1183). In Proceedings of the 56st Annual Meeting of the Association for Computational Linguistics. Melbourne, Australia, volume 1.\r\n> 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.\r\n\r\nYou might also be interested in [our code](https://github.com/yumoxu/stocknet-code) for stock movement prediction. \r\n\r\nShould you have any query please contact me at [yumo.xu@ed.ac.uk](mailto:yumo.xu@ed.ac.uk).\r\n\r\n## Dataset Overview\r\nTwo-year price movements from 01/01/2014 to 01/01/2016 of 88 stocks are selected to target, coming from all the 8 stocks in the Conglomerates sector and the top 10 stocks in capital size in each of the other 8 sectors. The full list of 88 stocks and their companies selected from 9 sectors is available in **StockTable**, a facsimile of the paper appendix **appendix\\_table\\_of\\_target\\_stocks.pdf**.\r\n\r\n## Data Component\r\nThis dataset comprises two main components,\r\n\r\n* **./tweet**: tweet data from [Twitter](https://twitter.com/)\r\n* **./price**: price data from [Yahoo Finance](http://\u0002nance.yahoo.com/) \r\n\r\nEach component contains their raw data and preprocessed data organized by stocks,  \r\n\r\n* **./tweet/raw**\r\n* **./tweet/preprocessed**\r\n\r\nand  \r\n\r\n* **./price/raw**  \r\n* **./price/preprocessed**\r\n\r\n## Data Format\r\n\r\n### Raw Tweet Data\r\nFormat: JSON  \r\nKeys: see [Introduction to Tweet JSON](https://developer.twitter.com/en/docs/tweets/data-dictionary/overview/intro-to-tweet-json\r\n)\r\n\r\n### Preprocessed Tweet Data\r\nFormat: JSON  \r\nKeys: 'text', 'user\\_id\\_str', 'created\\_at'\r\n\r\n### Raw Price Data\r\nFormat: CSV  \r\nEntries: date, open price, high price, low price, close price, adjust close price, volume  \r\n\r\n### Preprocessed Price Data\r\nFormat: TXT  \r\nEntries: date, movement percent, open price, high price, low price, close price, volume  \r\nNote: *open, high, low, close prices are normalized values.*","description_withheld":null,"homepage":"https://github.com/yumoxu/stocknet-dataset","introduced_date":"2018-07-01","introduced_date_note":null,"introduced_by":{"paper":"/paper/stock-movement-prediction-from-tweets-and","title":"Stock Movement Prediction from Tweets and Historical Prices","first_author":"Yumo Xu","url":null},"license":{"name":"MIT","url":"https://github.com/yumoxu/stocknet-dataset/blob/master/LICENSE"},"modalities":[{"name":"Texts","url":"/datasets/modality/texts"},{"name":"Time series","url":"/datasets/modality/time-series"}],"tasks":[{"name":"Time Series","url":"/task/time-series-1","datasets_with_task":"/datasets/task/time-series-1"},{"name":"Stock Market Prediction","url":"/task/stock-market-prediction","datasets_with_task":"/datasets/task/stock-market-prediction"},{"name":"Stock Prediction","url":"/task/stock-prediction","datasets_with_task":"/datasets/task/stock-prediction"},{"name":"Text-Based Stock Prediction","url":"/task/text-based-stock-prediction","datasets_with_task":"/datasets/task/text-based-stock-prediction"}],"languages":[],"variants":["stocknet"],"data_loaders":[],"num_papers_in_archive":36,"source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28"},"benchmarks":[{"leaderboard":"/sota/stock-market-prediction-on-stocknet","task":"Stock Market Prediction","dataset_variant":"stocknet","rows":5,"metrics":["F1"],"first_row_in_archive_order":{"model":"HOT","paper":"/paper/higher-order-transformers-enhancing-stock","metrics":{"F1":"0.72"},"code_links":[{"title":"s-omranpour/Multimodal-Stock-Prediction","url":"https://github.com/s-omranpour/Multimodal-Stock-Prediction"}]},"note":"rows are the archive's own order at snapshot; nothing here re-ranks them"}],"papers_with_a_benchmark_row":[{"paper":"/paper/higher-order-transformers-enhancing-stock","title":"Higher Order Transformers: Enhancing Stock Movement Prediction On Multimodal Time-Series Data","date":"2024-12-13","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/deep-attentive-learning-for-stock-movement","title":"Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations","date":"2020-11-01","rows_on_this_dataset":1,"code_links":1,"syntology":null},{"paper":"/paper/stock-movement-prediction-from-tweets-and","title":"Stock Movement Prediction from Tweets and Historical Prices","date":"2018-07-01","rows_on_this_dataset":1,"code_links":1,"syntology":null}],"syntology_totals":{"read_at":"2026-09-24T18:15:14+00:00","papers_with_samples":0,"samples_harvested":0,"samples_ran":0,"samples_unverified":0,"pointer_only_for_licence":0,"papers_with_no_sample_that_ran":0,"note":"the per-paper counts above, summed; not a rate"},"papers_note":"The archive never published its papers-using-dataset list; these are papers with a leaderboard row on this dataset's benchmarks."}