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Articles

Selective transfer learning with adversarial training for stock movement prediction

ORCID Icon, ORCID Icon & ORCID Icon
Pages 492-510 | Received 15 Sep 2021, Accepted 09 Dec 2021, Published online: 04 Jan 2022

References

  • Bollen, J., Mao, H., & Zeng, X. (2011). Twitter mood predicts the stock market. Computational Materials Science, 2(1), 1–8. https://doi.org/10.1016/j.jocs.2010.12.007
  • Brown, R. G. (2004). Smoothing, forecasting and prediction of discrete time series. Courier Corporation.
  • Camacho, D., Luzón, M. V., & Cambria, E. (2021). New trends and applications in social media analytics. Future Generation Computer Systems, 114(358), 318–321. https://doi.org/10.1016/j.future.2020.08.007
  • Cao, Z., Zhou, Y., Yang, A., & Peng, S. (2021). Deep transfer learning mechanism for fine-grained cross-domain sentiment classification. Connection Science, 33(4), 911–928. https://doi.org/10.1080/09540091.2021.1912711
  • Chen, P., & Tan, Y. (2021). Stock market movement prediction by gated hierarchical encoder. In International conference on swarm intelligence (ICSI) (pp. 511–521). Springer.
  • Cinar, Y. G., Mirisaee, H., Goswami, P., Gaussier, E., A-Bachir, A., & Strijov, V. (2017). Position-based content attention for time series forecasting with sequence-to-sequence rnns. In International conference on neural information processing (ICONIP) (pp. 533–544). Springer.
  • D'Angelo, G., Palmieri, F., Robustelli, A., & Castiglione, A. (2021). Effective classification of android malware families through dynamic features and neural networks. Connection Science, 33(3), 786–801. https://doi.org/10.1080/09540091.2021.1889977
  • Dai, H., Li, H., Tian, T., Huang, X., Wang, L., Zhu, J., & Song, L. (2018). Adversarial attack on graph structured data. In International conference on machine learning (ICML) (pp. 2578–2593). PMLR.
  • Deng, Y., Bao, F., Kong, Y., Ren, Z., & Dai, Q. (2017). Deep direct reinforcement learning for financial signal representation and trading. IEEE Transactions on Neural Networks and Learning Systems, 28(3), 653–664. https://doi.org/10.1109/TNNLS.2016.2522401
  • Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2019). BERT: Pre-training of deep bidirectional transformers for language understanding. In Association for Computational Linguistics (ACL) (pp. 4171–4186). Association for Computational Linguistics.
  • Ding, X., Zhang, Y., Liu, T., & Duan, J. (2015). Deep learning for event-driven stock prediction. In International joint conferences on artificial intelligence (IJCAI) (pp. 2627–2633). AAAI Press.
  • Fama, E. F., & French, K. R. (2012). Size, value, and momentum in international stock returns. Journal of Financial Economics, 105(3), 457–472. https://doi.org/10.1016/j.jfineco.2012.05.011
  • Feng, F., Chen, H., He, X., Ding, J., Sun, M., & Chua, T. S. (2019). Enhancing stock movement prediction with adversarial training. In International joint conferences on artificial intelligence (IJCAI) (pp. 5843–5849). ACM.
  • Feng, F., He, X., Wang, X., Luo, C., Liu, Y., & Chua, T. S. (2019). Temporal relational ranking for stock prediction. ACM Transactions on Information Systems (TOIS), 37(2), 1–30. https://doi.org/10.1145/3309547
  • Goodfellow, I. J., Shlens, J., & Szegedy, C. (2015). Explaining and harnessing adversarial examples. In International conference on learning representations (ICLR). arxiv.
  • Harel, M., & Mannor, S. (2011). Learning from multiple outlooks. In International conference on machine learning (ICML) (pp. 401–408). Omnipress.
  • He, Q. Q., Pang, P. C. I., & Si, Y. W. (2019). Transfer learning for financial time series forecasting. In Pacific Rim international conference on artificial intelligence (PRICAI) (pp. 24–36). Springer.
  • He, X., He, Z., Du, X., & Chua, T. S. (2018). Adversarial personalized ranking for recommendation. In International ACM Sigir conference on research and development in information retrieval (SIGIR) (pp. 355–364). ACM.
  • Hochreiter, S., & Schmidhuber, J. (1997). Long short-term memory. Neural Computation, 9(8), 1735–1780. https://doi.org/10.1162/neco.1997.9.8.1735
  • Jiang, W. (2021). Applications of deep learning in stock market prediction: recent progress. Expert Systems with Applications, 184, 115537. https://doi.org/10.1016/j.eswa.2021.115537
  • Jin, Z., Yang, Y., & Liu, Y. (2020). Stock closing price prediction based on sentiment analysis and LSTM. Neural Computing and Applications, 32(13), 9713–9729. https://doi.org/10.1007/s00521-019-04504-2
  • Kamilaris, A., & Prenafeta-Boldú, F. X. (2018). Deep learning in agriculture: A survey. Computers and Electronics in Agriculture, 147(2), 70–90. https://doi.org/10.1016/j.compag.2018.02.016
  • Khuwaja, P., Khowaja, S. A., & Dev, K. (2021). Adversarial learning networks for FinTech applications using heterogeneous data sources. IEEE Internet of Things Journal, 147, 1–1. https://doi.org/10.1109/JIOT.2021.3100742
  • Kumar, M., & Thenmozhi, M. (2006). Forecasting stock index movement: A comparison of support vector machines and random forest. In Indian Institute of Capital Markets (IICM). IEEE.
  • Kurakin, A., Goodfellow, I., & Bengio, S. (2017). Adversarial machine learning at scale. In International conference on learning representations (ICLR).
  • Li, Y., Wang, X., Wang, W., Zhang, Z., Wang, J., Luo, X., & Xie, S. (2021). Learning adversarial policy in multiple scenes environment via multi-agent reinforcement learning. Connection Science, 33(3), 407–426. https://doi.org/10.1080/09540091.2020.1832961
  • Li, Y., Zheng, W., & Zheng, Z. (2019). Deep robust reinforcement learning for practical algorithmic trading. IEEE Access, 7, 108014–108022. https://doi.org/10.1109/Access.6287639
  • Lin, H., Zhou, D., Liu, W., & Bian, J. (2021). Learning multiple stock trading patterns with temporal routing adaptor and optimal transport. In Proceedings of the 27th ACM Sigkdd conference on knowledge discovery & data mining (KDD) (pp. 1017–1026). ACM.
  • Lo, A. W., & MacKinlay, A. C. (1988). Stock market prices do not follow random walks: Evidence from a simple specification test. RFS, 1(1), 41–66. https://doi.org/10.3386/w2168
  • Miyato, T., Dai, A. M., & Goodfellow, I. (2017). Adversarial training methods for semi-supervised text classification. In International conference on learning representations (ICLR).
  • Miyato, T., Maeda, S i., Koyama, M., Nakae, K., & Ishii, S. (2016). Distributional smoothing with virtual adversarial training. In International conference on learning representations (ICLR).
  • Nguyen, T. T., & Yoon, S. (2019). A novel approach to short-Term stock price movement prediction using transfer learning. Applied Sciences, 9(22), 4745. https://doi.org/10.3390/app9224745
  • Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2020). A comprehensive evaluation of ensemble learning for stock-market prediction. Journal of Big Data, 7(1), 1–40. https://doi.org/10.1186/s40537-020-00299-5
  • Nti, I. K., Adekoya, A. F., & Weyori, B. A. (2021). A novel multi-source information-fusion predictive framework based on deep neural networks for accuracy enhancement in stock market prediction. Journal of Big Data, 8(1), 1–28. https://doi.org/10.1186/s40537-020-00400-y
  • Oquab, M., Bottou, L., Laptev, I., & Sivic, J. (2014). Learning and transferring mid-level image representations using convolutional neural networks. In IEEE conference on computer vision and pattern recognition (CVPR) (pp. 1717–1724). IEEE Computer Society.
  • Qin, Y., Song, D., Chen, H., Cheng, W., Jiang, G., & Cottrell, G. (2017). A dual-stage attention-based recurrent neural network for time series prediction. In International joint conferences on artificial intelligence (IJCAI) (pp. 2627–2633). ijcai.org
  • Saboia, J. L. M. (1977). Autoregressive integrated moving average (ARIMA) models for birth forecasting. Journal of the American Statistical Association, 72(358), 264–270. https://doi.org/10.1080/01621459.1977.10480989
  • Sawhney, R., Agarwal, S., Wadhwa, A., & Shah, R. (2020). Deep attentive learning for stock movement prediction from social media text and company correlations. In Conference on empirical methods in natural language processing (EMNLP) (pp. 8415–8426). Association for Computational Linguistics.
  • Sawhney, R., Agarwal, S., Wadhwa, A., & Shah, R. (2021). Exploring the scale-free nature of stock markets: Hyperbolic graph learning for algorithmic trading. In Proceedings of the web conference 2021 (WWW) (pp. 11–22). ACM/IW3C2.
  • Tran, D. T., Iosifidis, A., Kanniainen, J., & Gabbouj, M. (2019). Temporal attention-augmented bilinear network for financial time-series data analysis. IEEE Transactions on Neural Networks and Learning Systems, 30(5), 1407–1418. https://doi.org/10.1109/TNNLS.5962385
  • Wang, C., & Mahadevan, S. (2011). Heterogeneous domain adaptation using manifold alignment. In International joint conferences on artificial intelligence (IJCAI) (pp. 1717–1724). IJCAI/AAAI.
  • Wang, Q., Li, X., & Liu, Q. (2021). Empirical research of accounting conservatism, corporate governance and stock price collapse risk based on panel data model. Connection Science, 33(4), 995–1010. https://doi.org/10.1080/09540091.2020.1806204
  • Weiss, K., Khoshgoftaar, T. M., & Wang, D. (2016). A survey of transfer learning. Journal of Big Data, 3(1), 9. https://doi.org/10.1186/s40537-016-0043-6
  • Wu, L., Quan, C., Li, C., & Ji, D. (2018). Parl: Let strangers speak out what you like. In Proceedings of the 27th ACM international conference on information and knowledge management (CIKM) (pp. 677–686). ACM.
  • Wu, S., Liu, Y., Zou, Z., & Weng, T. H. (2021). S_I_LSTM: stock price prediction based on multiple data sources and sentiment analysis. Connection Science, 1–19. https://doi.org/10.1080/09540091.2021.1940101
  • Wu, X., Wang, L., Xia, Y., Liu, W., Wu, L., Xie, S., Qin, T., & Liu, T. Y. (2021). Temporally correlated task scheduling for sequence learning. In International conference on machine learning (ICML) (pp. 11274–11284). PMLR.
  • Xie, C., Wang, J., Zhang, Z., Zhou, Y., Xie, L., & Yuille, A. (2017). Adversarial examples for semantic segmentation and object detection. In IEEE international conference on computer vision (ICCV) (pp. 1369–1378). IEEE Computer Society.
  • Xu, Y., & Cohen, S. B. (2018). Stock movement prediction from tweets and historical prices. In Association for computational linguistics (ACL) (pp. 1970–1979). Association for Computational Linguistics.
  • Ye, R., & Dai, Q. (2018). A novel transfer learning framework for time series forecasting. Knowledge-Based Systems, 156(1), 74–99. https://doi.org/10.1016/j.knosys.2018.05.021
  • Ying, L., Qian Nan, Z., Fu Ping, W., Tuan Kiang, C., Keng Pang, L., Heng Chang, Z., Lu, C., Jun, L. G., & Nam, L. (2021). Adaptive weights learning in CNN feature fusion for crime scene investigation image classification. Connection Science, 33(3), 719–734. https://doi.org/10.1080/09540091.2021.1875987
  • Ying, L., Qiqi, L., Jiulun, F., Fuping, W., Jianlong, F., Qingan, Y., Kiang, C. T., & Nam, L. (2021). Tyre pattern image retrieval–current status and challenges. Connection Science, 33(2), 237–255. https://doi.org/10.1080/09540091.2020.1806207
  • You, Q., Luo, J., Jin, H., & Yang, J. (2015). Robust image sentiment analysis using progressively trained and domain transferred deep networks. In American Association for Artificial Intelligence (AAAI). AAAI Press.
  • Zhang, L., Aggarwal, C., & Qi, G. J. (2017). Stock price prediction via discovering multi-frequency trading patterns. In Knowledge discovery in database (KDD) (pp. 2141–2149). Association for Computing Machinery.