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Applying BERT to analyze investor sentiment in stock market

  • S.I. : Higher Level Artificial Neural Network Based Intelligent Systems
  • Published:
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Abstract

This paper is an analysis of investor sentiment in the stock market based on the bidirectional encoder representations from transformers (BERT) model. First, we extracted the sentiment value from online information published by stock investor, using the Bert model. Second, these sentiment values were weighted by attention for computing the investor sentiment indicator. Finally, the relationship between investor sentiment and stock yield was analyzed through a two-step cross-sectional regression validation model. The experiments found that investor sentiment in online reviews had a significant impact on stock yield. The experiments show that the Bert model used in this paper can achieve an accuracy of 97.35% for the analysis of investor sentiment, which is better than both LSTM and SVM methods.

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References

  1. De Gooijer JG, Hyndman RJ (2005) 25 years of IIF time series forecasting: a selective review. Soc Sci Electron Publ 22(3):443–473

    Google Scholar 

  2. Farina L, Rinaldi S (2000) Positive linear systems. Theory and applications. J Vet Med Sci 63(9):945–948

    MATH  Google Scholar 

  3. Fisher KL, Statman M (2000) Investor sentiment and stock returns. Financ Anal J 56(2):16–23

    Article  Google Scholar 

  4. Jin Z, Yang Y, Liu Y (2019) Stock closing price prediction based on sentiment analysis and LSTM. Neural Comput Appl 32:9713–9729. https://doi.org/10.1007/s00521-019-04504-2

  5. Zweig ME (1973) An investor expectations stock price predictive model using closed-end fund premiums. J Finance 28:67–78

    Article  Google Scholar 

  6. Lee CMC, Shleifer A, Thaler RH (1991) Investor sentiment and the closed-end fund puzzle. J Finance 46:75–109

    Article  Google Scholar 

  7. Swaminathan B (1996) Time-varying expected small firm returns and closed-end fund discounts. Rev Financ Stud 9:845–887

    Article  Google Scholar 

  8. Kurov A (2010) Investor sentiment and the stock market's reaction to monetary policy. J Banking Finance 34:139–149. https://doi.org/10.1016/j.jbankfin.2009.07.010

  9. Yueqin L, Yong H, Chao Y (2020) Investor sentiment and stock price: empirical evidence from Chinese SEOs. Econ Model. https://doi.org/10.1016/j.econmod.2020.02.012

  10. Riloff E, Wiebe J, Wilson T (2003) Learning subjective nouns using extraction pattern bootstrapping. In: Proceedings of the seventh conference on natural language learning at HLT-NAACL 2003, Morristown, NJ, USA. Association for Computational Linguistics, pp 25–32

  11. Zhang D (2017) High-speed train control system big data analysis based on the fuzzy rdf model and uncertain reasoning. Int J Comput Commun Control 12(4):577–591

    Article  Google Scholar 

  12. Kim SM, Hovy E (2006) Automatic identification of pro and con reasons in online reviews. In: Proceedings of the COLING/ACL 2006 Main Conference Poster Sessions, pp 483–490

  13. Banea C, Mihalcea R, Wiebe J (2010) Multilingual subjectivity: are more languages better?. In: Proceedings of the 23rd International Conference on Computational Linguistics (Coling 2010), pp 28–36

  14. Wan X (2009) Co-training for cross-lingual sentiment classification. In: Proceedings of the joint conference of the 47th annual meeting of the ACL and the 4th international joint conference on natural language processing of the AFNLP. Association for Computational Linguistics, Suntec, Singapore, pp 235–243

  15. Boyd-Graber J, Resnik P (2010). Holistic sentiment analysis across languages: Multilingual supervised latent Dirichlet allocation. In Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing, pp 45–55

  16. Lu B (2010) Identifying opinion holders and targets with dependency parser in Chinese news texts. In Proceedings of the NAACL HLT 2010 student research workshop pp 46–51

  17. Mihalcea R, Banea C, Wiebe J (2007) Learning multilingual subjective language via cross-lingual projections. In: Proceedings of the 45th annual meeting of the association of computational linguistics, Prague, Czech Republic. Stroudsburg, PA: Association for Computational Linguistics, pp 976–983

  18. Barsky RB, Sims ER (2012) Information, animal spirits, and the meaning of innovations in consumer confidence. Am Econ Rev Am Econ Assoc 102(4):343–177

  19. Schmeling M (2009) Investor sentiment and stock returns: some international evidence. J Empir Finance 16(3):394–408

  20. Chung SL, Hung CH, Yeh CY (2012) When does investor sentiment predict stock returns? J Empir Finance 19(2):217–240

  21. Zhiqing M, Guojie Z, Yunwen Z (2018) Research on the relationship between internet investor sentiment and stock market price——based on analysis of text mining technology. Price Theory Practice 8:127–130. https://doi.org/10.19851/j.cnki.cn11-1010/f.2018.08.031

  22. Brown GW, Cliff MT (2004) Investor sentiment and the near-term stock market. J Empir Finance 11(1):1–27

    Article  Google Scholar 

  23. Bin L, Tan C, Cardie C, Tsou BK (2011) Joint bilingual sentiment classification with unlabeled parallel corpora. In: Proceedings of the 49th annual meeting of the association for computational linguistics: human language technologies. Association for Computational Linguistics, Stroudsburg, pp 320–330

  24. Baker M, Wurgler J (2006) Investor sentiment and the cross-section of stock returns. J Finance 61(4):1645–1680

  25. So WG, Kim HK (2020) The influence of drama viewing on online purchasing intention: an empirical study. J Syst Manag Sci 10(2):69–81

  26. Horvat D, Wydra S, Lerch CM (2018) Modelling and simulating the dynamics of the european demand for bio-based plastics. Int J Simul Model 17(3):419–430

  27. Moon KS, Kim H (2019) Performance of deep learning in prediction of stock market volatility. Econ Comput Econ Cybern Stud Res 53(2):77–92. https://doi.org/10.24818/18423264/53.2.19.05

  28. He Z, He L, Wen F (2019) Risk compensation and market returns: the role of investor sentiment in the stock market. Emerg Mark Finance Trade 55(3):704–718

  29. Fama EF, MacBeth JD (1973) Risk, return, and equilibrium: empirical tests. J Political Econ 113(3):607–636

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Acknowledgments

This research is supported by the R&D Program of Beijing Municipal Education commission (Grant No. KJZD20191000401). This research is also supported by the Program of the Co-Construction with Beijing Municipal Commission of Education of China (Grant Nos. B20H100020, B19H100010) and funded by the Key Project of Beijing Social Science Foundation Research Base (Grant No. 19JDYJA001).

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Authors and Affiliations

  1. National Academy of Economic Security, Beijing Jiaotong University, Beijing, China

    Menggang Li & Xiaojun Jia

  2. School of Economics and Management, Beijing Jiaotong University, Beijing, China

    Wenrui Li, Fang Wang & Guangwei Rui

  3. Beijing Laboratory of National Economic Security Early-warning Engineering, Beijing Jiaotong University, Beijing, China

    Menggang Li, Wenrui Li & Fang Wang

  4. Beijing Center for Industrial Security and Development Research, Beijing Jiaotong University, Beijing, China

    Menggang Li & Xiaojun Jia

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  1. Menggang Li
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  2. Wenrui Li
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  3. Fang Wang
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  4. Xiaojun Jia
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  5. Guangwei Rui
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Correspondence to Wenrui Li or Xiaojun Jia.

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Li, M., Li, W., Wang, F. et al. Applying BERT to analyze investor sentiment in stock market. Neural Comput & Applic 33, 4663–4676 (2021). https://doi.org/10.1007/s00521-020-05411-7

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