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Efficient Market Hypothesis Test with Stock Tweets and Natural Language Processing Models

Author

Listed:
  • Bolin Mao

    (Kyoto Institute of Economic Research, Kyoto University)

  • Chenhui Chu

    (Graduate School of Informatics, Kyoto University)

  • Yuta Nakashima

    (Institute for Datability Science, Osaka University)

  • Hajime Nagahara

    (Institute for Datability Science, Osaka University)

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.

Suggested Citation

  • Bolin Mao & Chenhui Chu & Yuta Nakashima & Hajime Nagahara, 2022. "Efficient Market Hypothesis Test with Stock Tweets and Natural Language Processing Models," KIER Working Papers 1082, Kyoto University, Institute of Economic Research.
    • Handle: RePEc:kyo:wpaper:1082
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    Efficient Market Hypothesis Test; Daily Stock Price Prediction; Stock Tweet; Natural Language Processing;
    All these keywords.

    JEL classification:

    • C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • G1 - Financial Economics - - General Financial Markets

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