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Machine Reading Comprehension of News on Stock Price Changes

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Book cover Advances in Artificial Intelligence (JSAI 2021)

Part of the book series: Advances in Intelligent Systems and Computing ((AISC,volume 1423))

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Abstract

One of the reasons why stock prices fluctuate greatly is because of IR announcements, which are information for investors, and news reports on events that are closely related to the companies. When a stock price change occurs, news sites for investors may report the stock price change and the reason for the change. However, such articles report only a certain portion of overall events that are closely related to reasons for stock price changes. Thus, in order to provide investors with information on those reasons for stock price changes, it is necessary to develop a system to collect information on events that could be closely related to the stock price changes of certain companies from the Internet. As the first step towards developing such a system, this paper takes an approach of employing a BERT-based machine reading comprehension model, which extracts causes of stock price changes from news reports on stock price changes. Those extracted reasons are intended to be further used to train a system to collect information on events that could be closely related to the stock price changes of certain companies from the Internet.

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Notes

  1. 1.

    When evaluated with the SQuAD [14] (https://rajpurkar.github.io/SQuAD-explorer/).

  2. 2.

    https://minkabu.jp/.

  3. 3.

    Delivered from October 30th to December 3rd, 2020.

  4. 4.

    As an alternative, we also examine the approach of using a single general question sentence such as below: However, this approach does not perform well in the evaluation compared with the case (a) in this section.

  5. 5.

    https://github.com/google-research/bert.

  6. 6.

    Trained with 104 languages, available from https://github.com/google-research/bert/blob/master/multilingual.md.

  7. 7.

    http://taku910.github.io/mecab/ (in Japanese).

  8. 8.

    https://github.com/neologd/mecab-ipadic-neologd.

  9. 9.

    run_squad.py, with the number of epochs as 2, batch size as 8, and learning rate as 0.00003.

  10. 10.

    http://www.cl.ecei.tohoku.ac.jp/rcqa/ (in Japanese).

  11. 11.

    https://sites.google.com/view/fiqa/home.

References

  1. Becquin, G.: GBe at FinCausal 2020, task 2: span-based causality extraction for financial documents. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 40–44 (2020)

    Google Scholar 

  2. Chakravarthy, S., Kanakagiri, T., Radhakrishnan, K., Umapathy, A.: Domino at FinCausal 2020, task 1 and 2: causal extraction system. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 90–94 (2020)

    Google Scholar 

  3. Chen, D., Fisch, A., Weston, J., Bordes, A.: Reading Wikipedia to answer open-domain questions. In: Proceedings of 55th ACL, pp. 1870–1879 (2017)

    Google Scholar 

  4. Chen, T., Li, H., Kasamatsu, M., Utsuro, T., Kawada, Y.: Developing a how-to tip machine comprehension dataset and its evaluation in machine comprehension by BERT. In: Proceedings of 3rd FEVER, pp. 26–35 (2020)

    Google Scholar 

  5. Devlin, J., Chang, M.W., Lee, K., Toutanova, K.: BERT: pre-training of deep bidirectional Transformers for language understanding. In: Proceedings of NAACL-HLT, pp. 4171–4186 (2019)

    Google Scholar 

  6. Imoto, T., Ito, T.: JDD @ FinCausal 2020, task 2: financial document causality detection. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 50–54 (2020)

    Google Scholar 

  7. Ionescu, M., Avram, A.M., Dima, G.A., Cercel, D.C., Dascalu, M.: UPB at FinCausal-2020, tasks 1 & 2: causality analysis in financial documents using pretrained language models. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 55–59 (2020)

    Google Scholar 

  8. Kao, P.W., Chen, C.C., Huang, H.H., Chen, H.H.: NTUNLPL at FinCausal 2020, task 2: improving causality detection using Viterbi decoder. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 69–73 (2020)

    Google Scholar 

  9. Lee, K., Chang, M.W., Toutanova, K.: Latent retrieval for weakly supervised open domain question answering. In: Proceedings of 57th ACL, pp. 6086–6096 (2019)

    Google Scholar 

  10. Liu, Z., Huang, D., Huang, K., Li, Z., Zhao, J.: FinBERT: a pre-trained financial language representation model for financial text mining. In: Proceedings of 29th IJCAI, pp. 4513–4519 (2020)

    Google Scholar 

  11. Mariko, D., Abi-Akl, H., Labidurie, E., Durfort, S., De Mazancourt, H., El-Haj, M.: The financial document causality detection shared task (FinCausal 2020). In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 23–32 (2020)

    Google Scholar 

  12. Özenir, G., Karadeniz İ.: ISIKUN at the FinCausal 2020: linguistically informed machine-learning approach for causality identification in financial documents. In: Proceedings of the 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 85-89 (2020)

    Google Scholar 

  13. Pielka, M., et al.: Fraunhofer IAIS at FinCausal 2020, tasks 1 & 2: using ensemble methods and sequence tagging to detect causality in financial documents. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 64–68 (2020)

    Google Scholar 

  14. Pranav, R., Jian, Z., Konstantin, L., Percy, L.: SQuAD: 100,000+ questions for machine comprehension of text. In: Proceedings of EMNLP, pp. 2383–2392 (2016)

    Google Scholar 

  15. Sakai, H., Sakaji, H., Izumi, K., Matsui, H., Irie, K.: Extraction of causal and complementary information for generating market analysis comments by automatic generation of training data. In: Proceedings of 34th Annual Conference JSAI (2020). (in Japanese)

    Google Scholar 

  16. Szántó, Z., Berend, G.: ProsperAMnet at FinCausal 2020, task 1 & 2: modeling causality in financial texts using multi-headed transformers. In: Proceedings of 1st Joint Workshop on Financial Narrative Processing and MultiLing Financial Summarisation, pp. 80–84 (2020)

    Google Scholar 

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Author information

Authors and Affiliations

  1. Graduate School of Science and Technology, University of Tsukuba, 1-1-1, Tennodai, Tsukuba, Ibaraki, 305-8573, Japan

    Seiji Suzuki, Gakuto Tsutsumi & Takehito Utsuro

Authors
  1. Seiji Suzuki
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  2. Gakuto Tsutsumi
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  3. Takehito Utsuro
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Corresponding author

Correspondence to Takehito Utsuro .

Editor information

Editors and Affiliations

  1. Graduate School of System Design, Tokyo Metropolitan University, Hino, Tokyo, Japan

    Yasufumi Takama

  2. Graduate School of Economics, Osaka University, Toyonaka, Osaka, Japan

    Naohiro Matsumura

  3. Faculty of Business and Commerce, Kansai University, Osaka, Japan

    Katsutoshi Yada

  4. Faculty of Informatics, Kansai University, Takatsuki, Osaka, Japan

    Mitsunori Matsushita

  5. Department of Applied Computer Science, Tokyo Polytechnic University, Atsugi, Kanagawa, Japan

    Daisuke Katagami

  6. Division of Behavioral Science, Chiba University, Chiba, Japan

    Akinori Abe

  7. Department of Intelligence Science and Technology, Kyoto University, Kyoto, Japan

    Hisashi Kashima

  8. Institute of Industrial Science, The University of Tokyo, Meguro-ku, Tokyo, Japan

    Toshihiro Hiraoka

  9. Department of Computer Science and Engineering, Nagoya Institute of Technology, Nagoya, Japan

    Takahiro Uchiya

  10. Graduate School of Information Science and Technology Sapporo Hokkaido Japan, Hokkaido University, Sapporo, Hokkaido, Japan

    Rafal Rzepka

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Suzuki, S., Tsutsumi, G., Utsuro, T. (2022). Machine Reading Comprehension of News on Stock Price Changes. In: Takama, Y., et al. Advances in Artificial Intelligence. JSAI 2021. Advances in Intelligent Systems and Computing, vol 1423. Springer, Cham. https://doi.org/10.1007/978-3-030-96451-1_1

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