新聞と掲示板データを用いた日経平均VI予測モデルの提案と評価
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| Published in | 人工知能学会論文誌 Vol. 40; no. 4; pp. D-O94_1 - 14 |
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| Main Authors | , , , , , , , , |
| Format | Journal Article |
| Language | Japanese |
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一般社団法人 人工知能学会
01.07.2025
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| ISSN | 1346-0714 1346-8030 |
| DOI | 10.1527/tjsai.40-4_D-O94 |
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| Author | 山下 達雄 細川 蓮 上田 健太郎 梅原 英一 諏訪 博彦 小川 祐樹 服部 宏充 坪内 孝太 安本 慶一 |
|---|---|
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| References | [Feng 19] Feng, F., Chen, H., He, X., Ding, J., Sun, M., and Chua, T.: Enhancing stock movement prediction with adversarial training. in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), pp. 5843-5849 (2019) [Schumaker 09] Schumaker. R. P. and Chen, H.: A quantitative stock prediction system based on financial news, Information Processing & Management, Vol. 45, No. 5, pp. 571-583 (2009) URL= https://doi.org/10.1016/j.ipm.2009.05.001 [Christie 82] Christie, A. A.: The stochastic behavior of common stock variances: Value, leverage and interest rate effects, Journal of Financial Economics, Vol. 10, No. 4, pp. 407-432 (1982) URL= https://doi.org/10.1016/0304-405X(82)90018-6 [Kudo 06] Kudo, T.: MeCab: Yet another part-of-speech and morphological analyzer, http://mecab.sourceforge.jp (2006) [Alanyali 13] Alanyali, M., Moat, H. S., and Preis, T.: Quantifying the relationship between financial news and the stock market Scientific Reports, Vol. 3, No. 1, p. 3578 (2013) URL= https://doi.org/10.1038/srep03578 [Nti 21] Nti, I. K., Adekoya, A. F., and Weyori, B. A.: A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction, Journal of Big Data, Vol. 8, No. 1. p. 17 (2021) URL= https://doi.org/10.1186/s40537-020-00400-y [Ramos-Pérez 19] Ramos-Pérez, E., Alonso-González, P. J., and Núñez-Velázquez, J. J.: Forecasting volatility with a stacked model based on a hybridized artificial neural network, Expert Systems with Applications, Vol. 129, pp. 1-9 (2019) URL= https://doi.org/10.1016/j.eswa.2019.03.046 [Tan 21] Tan, S. D. and Tas, O.: Social media sentiment in international stock returns and trading activity, Journal of Behavioral Finance, Vol. 22, No. 4, pp. 221-234 (2021) URL= https://doi.org/10.1080/15427560.2020.1772261 [Bagla 24] Bagla, K., Kumar, A., Gupta, S., and Gupta, A.: Noisy Text Data: Foible of popular Transformer based NLP models, in Proceedings of the 3rd International Conference on AI-ML Systems, pp. 1-6 (2024) [Sasaki 20] Sasaki, K.. Suwa, H., Ogawa, Y., Umehara, Е., Yamashita, T., and Tsubouchi, K.: Evaluation of VI index forecasting model by machine learning for Yahoo! stock bbs using volatility trading simulation, in Hawaii International Conference on System Sciences (HICSS), pp. 1-9 (2020) [Sato 15] Sato, T.: Neologism dictionary based on the language resources on the web for MeCab, https://github.com/ neologd/mecab-ipadic-neologd (2015) [Katsafados 21] Katsafados, A. G., Androutsopoulos, I., Chalkidis, I., Fergadiotis, E., Leledakis, G. N., and Pyrgiotakis, E. G.: Using textual analysis to identify merger participants: Evidence from the U.S. banking industry. Finance Research Letters, Vol. 42, p. 101949 (2021) URL= https://doi.org/10.1016/j.frl.2021.101949 [Chen 15] Chen, K., Zhou, Y., and Dai, F.: A LSTM-based method for stock returns prediction: A case study of China stock market, in 2015 IEEE International Conference on Big Data (Big Data), pp. 2823-2824 (2015) [Lundberg 17] Lundberg, S. M. and Lee, S.-L.: A unified approach to interpreting model predictions, in Proceedings of the 31st International Conference on Neural Information Processing Systems, Vol. 30, pp. 4765-4774 (2017) [Zhao 22] Zhao, X. and Liu, Y.: False awareness stock market prediction by LightGBM with focal loss, in Proceedings of the Conference on Research in Adaptive and Convergent Systems (RACS), pp. 147--152 (2022) [Takamura 05] Takamura, H., Inui, T., and Okumura, M.: Extracting semantic orientations of words using spin model, in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pp. 133-140 (2005) [Gunnarsson 24] Gunnarsson, E. S., Isern, H. R., Kaloudis, A., Ristad, M., Vigdel, B., and Westgaard, S.: Prediction of realized volatility and implied volatility indices using Al and machine learning: A review, International Review of Financial Analysis, Vol. 93. p. 103221 (2024) URL= https://doi.org/10.1016/j.irfa.2024.103221 [Welch 08] Welch, I. and Goyal, A.: A comprehensive look at the empirical performance of equity premium prediction, The Review of Financial Studies, Vol. 21, No. 4, pp. 1455-1508 (2008) URL= https://doi.org/10.1093/rfs/hhm014 [Ahn 12] Ahn, J. J., Kim, D. H., Oh, K. J., and Kim, T. Y.: Applying option greeks to directional forecasting of implied volatility in the options market: An intelligent approach, Expert Systems with Applications, Vol. 39, No. 10, pp. 9315–9322 (2012) URL= https://doi.org/10.1016/j.eswa.2012.02.070 [Khan 22] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., and Alfakeeh, A. S.: Stock market prediction using machine learning classifiers and social media, news, Journal of Ambient Intelligence and Humanized Computing, Vol. 13, No. 7, pp. 3433–3456 (2022) [Ueda 21]Ueda, K., Sasaki, K., Suwa, H., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., and Yasumoto, K.: Prediction of Nikkei VI increase for reducing investment risk using Yahoo! JAPAN stock BBS, in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence Workshop/Special Session, pp. 126–133 (2021) [Gu 20] Gu, C. and Kurov, A.: Informational Role of Social Media: Evidence from Twitter Sentiment, Journal of Banking & Finance, Vol. 121, (2020) URL= https://doi.org/10.1016/j.jbankfin.2020.105969 [Duan 18] Duan, J., Zhang, Y., Ding, X., Chang, C. Y., and Liu, T.: Learning target-specific representations of financial news documents for cumulative abnormal return prediction, in Proceedings of the 27th International Conference on Computational Linguistics, pp. 2823-2833 (2018) [Song 18] Song, Y., Wang, H., and Zhu, M.: Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR, Finane Innov, Vol. 4 No. 2 (2018) URL= https://doi.org/10.1186/s40854-018-0086-0 [Blei 03] Blei, D. M., Ng, A. Y., and Jordan, M. I.: Latent dirichlet allocation, Journal of Machine Learning Research, Vol. 3, pp. 993– 1022 (2003) [Bollen 11] Bollen, J., Mao, H., and Zeng, X.: Twitter mood predicts the stock market, Journal of Computational Science, Vol. 2, No. 1, pp. 1-8 (2011) URL= https://doi.org/10.1016/j.jocs.2010.12.007 [Long 19] Long, W., Lu, Z., and Cui, L.: Deep learning-based feature engineering for stock price movement prediction, Knowledge-Based Systems, Vol. 164, pp. 163-173 (2019) URL= https://doi.org/10.1016/j.knosys.2018.10.034 [Ding 15] Ding. X., Zhang, Y., Liu, T., and Duan, J.: Deep learning for event-driven stock prediction, in Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI 15), pp. 2327-2333 (2015) [Tetlock 08] Tetlock, P. C., Saar-Tsechansky, M., and Macskassy, S.: More than words: Quantifying language to measure firms fundamentals, The Journal of Finance, Vol. 63, No. 3, pp. 1437-1467 (2008) URL= https://doi.org/10.1111/j.1540-6261.2008.01362.x [Bhandari 22] Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., and Khatri, R. K. C.: Predicting stock market index using LSTM, Machine Learning with Applications, Vol. 9, p. 100320 (2022) URL= https://doi.org/10.1016/j.mlwa.2022.100320 [Ueda 24] Ueda, K., Suwa, H., Yamada, M., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., and Yasumoto, K.: SSCDV: Social media document embedding with sentiment and topics for financial market forecasting, Expert Systems with Applications, Vol. 245, p. 122988 (2024) [Oliveira 17] Oliveira, N., Cortez, P., and Areal, N.: The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices, Expert Systems with Applications, Vol. 73, pp. 125–144 (2017) [Hong 24] Hong, M., Chen, Z., Mahmoud Soliman, W., and Zhang, K.: A comparative study of ISTM, lightGBM, and autoregressive model in narrow-Based ETF market prediction with multi-ticker models, in Proceedings of the 6th International Conference on Machine Learning and Machine Intelligence, MLMI 23, pp. 10-16 (2024) [Huynh 21] Huynh, D., Audet, G., Alabi, N., and Tian, Y.: Stock price prediction leveraging reddit: The role of trust filter and sliding window, in 2021 IEEE International Conference on Big Data (Big Data). pp. 1054-1060 (2021) [Ke 17] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A highly efficient gradient boosting decision tree, in Advances in Neural Information Processing Systems 30, pp. 3146–3154 (2017) [Gangopadhyay 23] Gangopadhyay, S. and Majumder, P.: Text representation for direction prediction of share market, Expert Systems with Applications, Vol. 211, (2023) URL= https://doi.org/10.1016/j.eswa.2022.118472 [Li 17] Li, B., Chan, K. C. C., Ou, C., and Sun, R.: Discovering public sentiment in social media for predicting stock movement of publicly listed companies, Information Systems, Vol. 69, pp. 81–92 (2017) [Shirata 11] Shirata, C. Y., Takeuchi, H., Ogino, S., and Watanabe, H.: Extracting key phrases as predictors of corporate bankruptcy: empirical analysis of annual reports by text mining, Journal of Emerging Technologies in Accounting, Vol. 8, pp. 31-44 (2011) URL= https://doi.org/10.2308/jeta-10182 [Suwa 17] Suwa, H., Ogawa, Y., Umehara, E., Kakigi, K., Yasumoto, K., Yamashita, T., and Tsubouchi, K.: Develop method to predict the increase in the Nikkei VI index, in 2017 IEEE International Conference on Big Data (Big Data), pp. 3133-3138 (2017) [Wu 22] Wu, S., Liu, Y., Zou, Z., and Weng, T.-H.: S I LSTM: Stock price prediction based on multiple data sources and sentiment analysis, Connection Science, Vol. 34, No. 1, pp. 44–62 (2022) |
| References_xml | – reference: [Ueda 21]Ueda, K., Sasaki, K., Suwa, H., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., and Yasumoto, K.: Prediction of Nikkei VI increase for reducing investment risk using Yahoo! JAPAN stock BBS, in Proceedings of the IEEE/WIC/ACM International Conference on Web Intelligence Workshop/Special Session, pp. 126–133 (2021) – reference: [Ding 15] Ding. X., Zhang, Y., Liu, T., and Duan, J.: Deep learning for event-driven stock prediction, in Proceedings of the 24th International Conference on Artificial Intelligence (IJCAI 15), pp. 2327-2333 (2015) – reference: [Feng 19] Feng, F., Chen, H., He, X., Ding, J., Sun, M., and Chua, T.: Enhancing stock movement prediction with adversarial training. in Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence (IJCAI), pp. 5843-5849 (2019) – reference: [Gunnarsson 24] Gunnarsson, E. S., Isern, H. R., Kaloudis, A., Ristad, M., Vigdel, B., and Westgaard, S.: Prediction of realized volatility and implied volatility indices using Al and machine learning: A review, International Review of Financial Analysis, Vol. 93. p. 103221 (2024) URL= https://doi.org/10.1016/j.irfa.2024.103221 – reference: [Bollen 11] Bollen, J., Mao, H., and Zeng, X.: Twitter mood predicts the stock market, Journal of Computational Science, Vol. 2, No. 1, pp. 1-8 (2011) URL= https://doi.org/10.1016/j.jocs.2010.12.007 – reference: [Schumaker 09] Schumaker. R. P. and Chen, H.: A quantitative stock prediction system based on financial news, Information Processing & Management, Vol. 45, No. 5, pp. 571-583 (2009) URL= https://doi.org/10.1016/j.ipm.2009.05.001 – reference: [Bhandari 22] Bhandari, H. N., Rimal, B., Pokhrel, N. R., Rimal, R., Dahal, K. R., and Khatri, R. K. C.: Predicting stock market index using LSTM, Machine Learning with Applications, Vol. 9, p. 100320 (2022) URL= https://doi.org/10.1016/j.mlwa.2022.100320 – reference: [Gangopadhyay 23] Gangopadhyay, S. and Majumder, P.: Text representation for direction prediction of share market, Expert Systems with Applications, Vol. 211, (2023) URL= https://doi.org/10.1016/j.eswa.2022.118472 – reference: [Alanyali 13] Alanyali, M., Moat, H. S., and Preis, T.: Quantifying the relationship between financial news and the stock market Scientific Reports, Vol. 3, No. 1, p. 3578 (2013) URL= https://doi.org/10.1038/srep03578 – reference: [Welch 08] Welch, I. and Goyal, A.: A comprehensive look at the empirical performance of equity premium prediction, The Review of Financial Studies, Vol. 21, No. 4, pp. 1455-1508 (2008) URL= https://doi.org/10.1093/rfs/hhm014 – reference: [Tan 21] Tan, S. D. and Tas, O.: Social media sentiment in international stock returns and trading activity, Journal of Behavioral Finance, Vol. 22, No. 4, pp. 221-234 (2021) URL= https://doi.org/10.1080/15427560.2020.1772261 – reference: [Ahn 12] Ahn, J. J., Kim, D. H., Oh, K. J., and Kim, T. Y.: Applying option greeks to directional forecasting of implied volatility in the options market: An intelligent approach, Expert Systems with Applications, Vol. 39, No. 10, pp. 9315–9322 (2012) URL= https://doi.org/10.1016/j.eswa.2012.02.070 – reference: [Song 18] Song, Y., Wang, H., and Zhu, M.: Sustainable strategy for corporate governance based on the sentiment analysis of financial reports with CSR, Finane Innov, Vol. 4 No. 2 (2018) URL= https://doi.org/10.1186/s40854-018-0086-0 – reference: [Ueda 24] Ueda, K., Suwa, H., Yamada, M., Ogawa, Y., Umehara, E., Yamashita, T., Tsubouchi, K., and Yasumoto, K.: SSCDV: Social media document embedding with sentiment and topics for financial market forecasting, Expert Systems with Applications, Vol. 245, p. 122988 (2024) – reference: [Gu 20] Gu, C. and Kurov, A.: Informational Role of Social Media: Evidence from Twitter Sentiment, Journal of Banking & Finance, Vol. 121, (2020) URL= https://doi.org/10.1016/j.jbankfin.2020.105969 – reference: [Hong 24] Hong, M., Chen, Z., Mahmoud Soliman, W., and Zhang, K.: A comparative study of ISTM, lightGBM, and autoregressive model in narrow-Based ETF market prediction with multi-ticker models, in Proceedings of the 6th International Conference on Machine Learning and Machine Intelligence, MLMI 23, pp. 10-16 (2024) – reference: [Blei 03] Blei, D. M., Ng, A. Y., and Jordan, M. I.: Latent dirichlet allocation, Journal of Machine Learning Research, Vol. 3, pp. 993– 1022 (2003) – reference: [Takamura 05] Takamura, H., Inui, T., and Okumura, M.: Extracting semantic orientations of words using spin model, in Proceedings of the 43rd Annual Meeting of the Association for Computational Linguistics (ACL'05), pp. 133-140 (2005) – reference: [Christie 82] Christie, A. A.: The stochastic behavior of common stock variances: Value, leverage and interest rate effects, Journal of Financial Economics, Vol. 10, No. 4, pp. 407-432 (1982) URL= https://doi.org/10.1016/0304-405X(82)90018-6 – reference: [Suwa 17] Suwa, H., Ogawa, Y., Umehara, E., Kakigi, K., Yasumoto, K., Yamashita, T., and Tsubouchi, K.: Develop method to predict the increase in the Nikkei VI index, in 2017 IEEE International Conference on Big Data (Big Data), pp. 3133-3138 (2017) – reference: [Kudo 06] Kudo, T.: MeCab: Yet another part-of-speech and morphological analyzer, http://mecab.sourceforge.jp (2006) – reference: [Ke 17] Ke, G., Meng, Q., Finley, T., Wang, T., Chen, W., Ma, W., Ye, Q., and Liu, T.-Y.: LightGBM: A highly efficient gradient boosting decision tree, in Advances in Neural Information Processing Systems 30, pp. 3146–3154 (2017) – reference: [Khan 22] Khan, W., Ghazanfar, M. A., Azam, M. A., Karami, A., Alyoubi, K. H., and Alfakeeh, A. S.: Stock market prediction using machine learning classifiers and social media, news, Journal of Ambient Intelligence and Humanized Computing, Vol. 13, No. 7, pp. 3433–3456 (2022) – reference: [Lundberg 17] Lundberg, S. M. and Lee, S.-L.: A unified approach to interpreting model predictions, in Proceedings of the 31st International Conference on Neural Information Processing Systems, Vol. 30, pp. 4765-4774 (2017) – reference: [Chen 15] Chen, K., Zhou, Y., and Dai, F.: A LSTM-based method for stock returns prediction: A case study of China stock market, in 2015 IEEE International Conference on Big Data (Big Data), pp. 2823-2824 (2015) – reference: [Nti 21] Nti, I. K., Adekoya, A. F., and Weyori, B. A.: A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction, Journal of Big Data, Vol. 8, No. 1. p. 17 (2021) URL= https://doi.org/10.1186/s40537-020-00400-y – reference: [Shirata 11] Shirata, C. Y., Takeuchi, H., Ogino, S., and Watanabe, H.: Extracting key phrases as predictors of corporate bankruptcy: empirical analysis of annual reports by text mining, Journal of Emerging Technologies in Accounting, Vol. 8, pp. 31-44 (2011) URL= https://doi.org/10.2308/jeta-10182 – reference: [Wu 22] Wu, S., Liu, Y., Zou, Z., and Weng, T.-H.: S I LSTM: Stock price prediction based on multiple data sources and sentiment analysis, Connection Science, Vol. 34, No. 1, pp. 44–62 (2022) – reference: [Bagla 24] Bagla, K., Kumar, A., Gupta, S., and Gupta, A.: Noisy Text Data: Foible of popular Transformer based NLP models, in Proceedings of the 3rd International Conference on AI-ML Systems, pp. 1-6 (2024) – reference: [Sato 15] Sato, T.: Neologism dictionary based on the language resources on the web for MeCab, https://github.com/ neologd/mecab-ipadic-neologd (2015) – reference: [Ramos-Pérez 19] Ramos-Pérez, E., Alonso-González, P. J., and Núñez-Velázquez, J. J.: Forecasting volatility with a stacked model based on a hybridized artificial neural network, Expert Systems with Applications, Vol. 129, pp. 1-9 (2019) URL= https://doi.org/10.1016/j.eswa.2019.03.046 – reference: [Long 19] Long, W., Lu, Z., and Cui, L.: Deep learning-based feature engineering for stock price movement prediction, Knowledge-Based Systems, Vol. 164, pp. 163-173 (2019) URL= https://doi.org/10.1016/j.knosys.2018.10.034 – reference: [Sasaki 20] Sasaki, K.. Suwa, H., Ogawa, Y., Umehara, Е., Yamashita, T., and Tsubouchi, K.: Evaluation of VI index forecasting model by machine learning for Yahoo! stock bbs using volatility trading simulation, in Hawaii International Conference on System Sciences (HICSS), pp. 1-9 (2020) – reference: [Tetlock 08] Tetlock, P. C., Saar-Tsechansky, M., and Macskassy, S.: More than words: Quantifying language to measure firms fundamentals, The Journal of Finance, Vol. 63, No. 3, pp. 1437-1467 (2008) URL= https://doi.org/10.1111/j.1540-6261.2008.01362.x – reference: [Zhao 22] Zhao, X. and Liu, Y.: False awareness stock market prediction by LightGBM with focal loss, in Proceedings of the Conference on Research in Adaptive and Convergent Systems (RACS), pp. 147--152 (2022) – reference: [Duan 18] Duan, J., Zhang, Y., Ding, X., Chang, C. Y., and Liu, T.: Learning target-specific representations of financial news documents for cumulative abnormal return prediction, in Proceedings of the 27th International Conference on Computational Linguistics, pp. 2823-2833 (2018) – reference: [Li 17] Li, B., Chan, K. C. C., Ou, C., and Sun, R.: Discovering public sentiment in social media for predicting stock movement of publicly listed companies, Information Systems, Vol. 69, pp. 81–92 (2017) – reference: [Huynh 21] Huynh, D., Audet, G., Alabi, N., and Tian, Y.: Stock price prediction leveraging reddit: The role of trust filter and sliding window, in 2021 IEEE International Conference on Big Data (Big Data). pp. 1054-1060 (2021) – reference: [Katsafados 21] Katsafados, A. G., Androutsopoulos, I., Chalkidis, I., Fergadiotis, E., Leledakis, G. N., and Pyrgiotakis, E. G.: Using textual analysis to identify merger participants: Evidence from the U.S. banking industry. Finance Research Letters, Vol. 42, p. 101949 (2021) URL= https://doi.org/10.1016/j.frl.2021.101949 – reference: [Oliveira 17] Oliveira, N., Cortez, P., and Areal, N.: The impact of microblogging data for stock market prediction: Using Twitter to predict returns, volatility, trading volume and survey sentiment indices, Expert Systems with Applications, Vol. 73, pp. 125–144 (2017) |
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| Title | 新聞と掲示板データを用いた日経平均VI予測モデルの提案と評価 |
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| ispartofPNX | 人工知能学会論文誌, 2025/07/01, Vol.40(4), pp.D-O94_1-14 |
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