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dc.contributor.author | Mao, Bolin | en |
dc.contributor.author | Chu, Chenhui | en |
dc.contributor.author | Nakashima, Yuta | en |
dc.contributor.author | Nagahara, Hajime | en |
dc.date.accessioned | 2022-10-07T04:04:12Z | - |
dc.date.available | 2022-10-07T04:04:12Z | - |
dc.date.issued | 2022-09 | - |
dc.identifier.uri | http://hdl.handle.net/2433/276616 | - |
dc.description.abstract | The efficient market hypothesis (EMH) plays a fundamental role in modern financial theory. Previous empirical studies have tested the weak and semi-strong forms of EMH with typical financial data, such as historical stock prices and annual earnings. However, few tests have been extended to include alternative data such as tweets. In this study, we use 1) two stock tweet datasets that have different features and 2) nine natural language processing (NLP)-based deep learning models to test the semi-strong form EMH in the United States stock market. None of our experimental results show that stock tweets with NLP-based models can prominently improve the daily stock price prediction accuracy compared with random guesses. Our experiment provides evidence that the semi-strong form of EMH holds in the United States stock market on a daily basis when considering stock tweet information with the NLP-based models. | en |
dc.language.iso | eng | - |
dc.publisher | Institute of Economic Research, Kyoto University | en |
dc.publisher.alternative | 京都大学経済研究所 | ja |
dc.subject | Efficient Market Hypothesis Test | en |
dc.subject | Daily Stock Price Prediction | en |
dc.subject | Stock Tweet | en |
dc.subject | Natural Language Processing | en |
dc.subject | C4 | en |
dc.subject | C5 | en |
dc.subject | G1 | en |
dc.subject.ndc | 330 | - |
dc.title | Efficient Market Hypothesis Test with Stock Tweets and Natural Language Processing Models | en |
dc.type | research report | - |
dc.type.niitype | Research Paper | - |
dc.identifier.jtitle | KIER Discussion Paper | en |
dc.identifier.volume | 1082 | - |
dc.identifier.spage | 1 | - |
dc.identifier.epage | 30 | - |
dc.textversion | author | - |
dc.sortkey | 01082 | - |
dc.address | Kyoto Institute of Economic Research, Kyoto University | en |
dc.address | Graduate School of Informatics, Kyoto University | en |
dc.address | Institute for Datability Science, Osaka University | en |
dc.address | Institute for Datability Science, Osaka University | en |
dc.relation.url | https://www.kier.kyoto-u.ac.jp/publication/?cat=en | - |
dcterms.accessRights | open access | - |
出現コレクション: | KIER Discussion Paper (英文版) |
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