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Cited by 146Stock Market Forecasting MethodsFinancial Markets and Investment StrategiesFinancial Analysis and Corporate Governance

Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations

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Sawhney, Ramit, Agarwal, Shivam, Wadhwa, Arnav, Shah, Rajiv Ratn·2020·Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
DOI 10.18653/v1/2020.emnlp-main.676
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Abstract

In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.Stock forecasting is complex, given the stochastic dynamics and non-stationary behavior of the market.Stock movements are influenced by varied factors beyond the conventionally studied historical prices, such as social media and correlations among stocks.The rising ubiquity of online content and knowledge mandates an exploration of models that factor in such multimodal signals for accurate stock forecasting.We introduce an architecture that achieves a potent blend of chaotic temporal signals from financial data, social media, and inter-stock relationships via a graph neural network in a hierarchical temporal fashion.Through experiments on real-world S&P 500 index data and English tweets, we show the practical applicability of our model as a tool for investment decision making and trading.

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Research works printed but not linked: 57

  1. Yaser S. Abu-Mostafa and Amir F. Atiya. 1996. In- troduction to financial forecasting. Applied Intelli- gence, 6(3):205-213. · DOI
  2. Klaus Adam, Albert Marcet, and Juan Pablo Nicoli. 2016. Stock market volatility and learning. The Journal of Finance, 71(1):33-82. · DOI
  3. Leif B. G. Andersen. 2007. Efficient simulation of the heston stochastic volatility model. SSRN Electronic Journal. · DOI
  4. Joseph H Anthony and K Ramesh. 1992. Associa- tion between accounting performance measures and stock prices. Journal of Accounting and Economics, 15(2-3):203-227. · DOI
  5. Uma Devi B, Sundar D, and Alli P. 2013. An effective time series analysis for stock trend prediction using ARIMA model for nifty midcap-50. International Journal of Data Mining & Knowledge Management Process, 3(1):65-78. · DOI
  6. Dzmitry Bahdanau, Kyunghyun Cho, and Yoshua Ben- gio. 2014. Neural machine translation by jointly learning to align and translate. arXiv preprint arXiv:1409.0473. · arXiv
  7. Fischer Black and Myron Scholes. 1973. The pricing of options and corporate liabilities. Journal of Polit- ical Economy, 81(3):637-654. · DOI · Paper page
  8. David C Blitz and Pim Van Vliet. 2007. The volatil- ity effect. The Journal of Portfolio Management, 34(1):102-113.
  9. Tim Bollerslev. 1986. Generalized autoregressive con- ditional heteroskedasticity. Journal of Economet- rics, 31(3):307-327. · DOI
  10. Daniel Cer, Yinfei Yang, Sheng-yi Kong, Nan Hua, Nicole Limtiaco, Rhomni St John, Noah Constant, Mario Guajardo-Cespedes, Steve Yuan, Chris Tar, et al. 2018. Universal sentence encoder for english. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing: System Demonstrations, pages 169-174.
  11. Yingmei Chen, Zhongyu Wei, and Xuanjing Huang. 2018. Incorporating corporation relationship via graph convolutional neural networks for stock price prediction. In Proceedings of the 27th ACM Inter- national Conference on Information and Knowledge Management, CIKM '18, page 1655-1658, New York, NY, USA. Association for Computing Machin- ery. · DOI
  12. Kyunghyun Cho, Bart Van Merriënboer, Dzmitry Bah- danau, and Yoshua Bengio. 2014. On the properties of neural machine translation: Encoder-decoder ap- proaches. arXiv preprint arXiv:1409.1259. · arXiv
  13. Djork-Arné Clevert, Thomas Unterthiner, and Sepp Hochreiter. 2015. Fast and accurate deep network learning by exponential linear units (elus). arXiv preprint arXiv:1511.07289. · arXiv
  14. Shumin Deng, Ningyu Zhang, Wen Zhang, Jiaoyan Chen, Jeff Z. Pan, and Huajun Chen. 2019. Knowledge-driven stock trend prediction and expla- nation via temporal convolutional network. In Com- panion Proceedings of The 2019 World Wide Web Conference, WWW '19, page 678-685, New York, NY, USA. Association for Computing Machinery. · DOI
  15. Ilia D. Dichev and Vicki Wei Tang. 2006. Earnings volatility and earnings predictability. SSRN Elec- tronic Journal. · DOI
  16. F.X. Diebold and K. Yilmaz. 2014. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics, 182(1):119-134. Cited By 416. · DOI
  17. Guangyu Ding and Liangxi Qin. 2019. Study on the prediction of stock price based on the associated net- work model of lstm. International Journal of Ma- chine Learning and Cybernetics. · DOI
  18. Xiao Ding, Yue Zhang, Ting Liu, and Junwen Duan. 2014. Using structured events to predict stock price movement: An empirical investigation. In Proceed- ings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 1415-1425.
  19. Edwin J Elton, Martin J Gruber, Stephen J Brown, and William N Goetzmann. 2009. Modern portfolio the- ory and investment analysis. John Wiley & Sons.
  20. Claude B Erb, Campbell R Harvey, and Tadas E Viskanta. 1994. Forecasting international equity cor- relations. Financial analysts journal, 50(6):32-45.
  21. Fuli Feng, Huimin Chen, Xiangnan He, Ji Ding, Maosong Sun, and Tat-Seng Chua. 2019a. En- hancing stock movement prediction with adversar- ial training. In Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intel- ligence, IJCAI-19, pages 5843-5849. International Joint Conferences on Artificial Intelligence Organi- zation. · DOI
  22. Gabriel Pui Cheong Fung, Jeffrey Xu Yu, and Wai Lam. 2002. News sensitive stock trend prediction. In Ad- vances in Knowledge Discovery and Data Mining, pages 481-493. Springer Berlin Heidelberg. · DOI
  23. C. Lee Giles, Steve Lawrence, and Ah Chung Tsoi. 2001. Noisy time series prediction using a recurrent neural network and grammatical inference. Machine Learning, 44(1/2):161-183. · DOI
  24. Justin Gilmer, Samuel S. Schoenholz, Patrick F. Ri- ley, Oriol Vinyals, and George E. Dahl. 2017. Neu- ral message passing for quantum chemistry. In Proceedings of the 34th International Conference on Machine Learning -Volume 70, ICML'17, page 1263-1272. JMLR.org.
  25. Alexandre Hocquard, Sunny Ng, and Nicolas Papa- georgiou. 2013. A constant-volatility framework for managing tail risk. The Journal of Portfolio Man- agement, 39(2):28-40.
  26. Ziniu Hu, Weiqing Liu, Jiang Bian, Xuanzhe Liu, and Tie-Yan Liu. 2018. Listening to chaotic whispers: A deep learning framework for news-oriented stock trend prediction. In Proceedings of the eleventh ACM international conference on web search and data mining, pages 261-269.
  27. Monique Jeanblanc, Marc Yor, and Marc Chesney. 2009. Mathematical methods for financial markets. Springer Science & Business Media.
  28. Wei Du Jue Liu, Zhuocheng Lu. 2019. Combining en- terprise knowledge graph and news sentiment analy- sis for stock price volatility prediction. Proceedings of the 52nd Hawaii International Conference on Sys- tem Sciences.
  29. Raehyun Kim, Chan Ho So, Minbyul Jeong, Sanghoon Lee, Jinkyu Kim, and Jaewoo Kang. 2019. Hats: A hierarchical graph attention network for stock move- ment prediction. arXiv preprint arXiv:1908.07999. · arXiv
  30. Kazuhiro Kohara, Tsutomu Ishikawa, Yoshimi Fukuhara, and Yukihiro Nakamura. 1997. Stock price prediction using prior knowledge and neu- ral networks. Intelligent Systems in Accounting, Finance & Management, 6(1):11-22.
  31. Thomas Fischer Christopher Krauss. 2018. Deep learn- ing with long short-term memory networks for finan- cial market predictions. European Journal of Oper- ational Research.
  32. Hao Li, Yanyan Shen, and Yanmin Zhu. 2018. Stock price prediction using attention-based multi-input lstm. In ACML, volume 95 of Proceedings of Ma- chine Learning Research, pages 454-469.
  33. PMLR.
  34. Xiaodong Li, Haoran Xie, Ran Wang, Yi Cai, Jingjing Cao, Feng Wang, Huaqing Min, and Xiaotie Deng. 2016. Empirical analysis: stock market prediction via extreme learning machine. Neural Computing and Applications, 27(1):67-78.
  35. Burton G. Malkiel. 1989. Efficient market hypothe- sis. In Finance, pages 127-134. Palgrave Macmillan UK. · DOI
  36. Daiki Matsunaga, Toyotaro Suzumura, and Toshihiro Takahashi. 2019. Exploring graph neural networks for stock market predictions with rolling window analysis. ArXiv, abs/1909.10660. · arXiv
  37. Tomas Mikolov, Kai Chen, Greg Corrado, and Jef- frey Dean. 2013. Efficient estimation of word representations in vector space. arXiv preprint arXiv:1301.3781. · arXiv
  38. Robert Neal and Simon M Wheatley. 1998. Do mea- sures of investor sentiment predict returns? Journal of Financial and Quantitative Analysis, 33(4):523- 547.
  39. Duc Huu Dat Nguyen, Loc Phuoc Tran, and Vu Nguyen. 2019. Predicting stock prices using dynamic LSTM models. In Communications in Computer and Information Science, pages 199-212. Springer International Publishing. · DOI
  40. Thi-Thu Nguyen and Seokhoon Yoon. 2019. A novel approach to short-term stock price movement pre- diction using transfer learning. Applied Sciences, 9(22):4745. · DOI
  41. Thien Hai Nguyen and Kiyoaki Shirai. 2015. Topic modeling based sentiment analysis on social media for stock market prediction. In Proceedings of the 53rd Annual Meeting of the Association for Compu- tational Linguistics and the 7th International Joint Conference on Natural Language Processing (Vol- ume 1: Long Papers), pages 1354-1364.
  42. Mahla Nikou, Gholamreza Mansourfar, and Jamshid Bagherzadeh. 2019. Stock price prediction using deep learning algorithm and its comparison with ma- chine learning algorithms. Intelligent Systems in Ac- counting, Finance and Management, 26. · DOI
  43. Jeffrey Pennington, Richard Socher, and Christopher Manning. 2014. GloVe: Global vectors for word representation. In Proceedings of the 2014 Confer- ence on Empirical Methods in Natural Language Processing (EMNLP), pages 1532-1543, Doha, Qatar. Association for Computational Linguistics. · DOI · Paper page
  44. Yao Qin, Dongjin Song, Haifeng Chen, Wei Cheng, Guofei Jiang, and Garrison Cottrell. 2017. A dual-stage attention-based recurrent neural net- work for time series prediction. arXiv preprint arXiv:1704.02971. · arXiv
  45. Yu Qin and Yi Yang. 2019. What you say and how you say it matters: Predicting financial risk using verbal and vocal cues. In 57th Annual Meeting of the Association for Computational Linguistics (ACL 2019), page 390.
  46. Robert Rich and Joseph Tracy. 2004. Uncertainty and labor contract durations. Review of Economics and Statistics, 86(1):270-287.
  47. Hsinchun Chen Robert P. Schumaker. 2019. Textual analysis of stock market prediction using breaking financial news: The azfin text system. ACM Trans- actions on Information Systems.
  48. Ramit Sawhney, Shivam Agarwal, Arnav Wadhwa, and Rajiv Ratn Shah. 2020a. Spatiotemporal hyper- graph convolution network for stock forecasting. In 2020 IEEE International Conference on Data Min- ing (ICDM).
  49. Ramit Sawhney, Puneet Mathur, Ayush Mangal, Piyush Khanna, Rajiv Shah, and Roger Zimmermann. 2020b. Multimodal multi-task financial risk fore- casting. In Proceedings of the 28th ACM Interna- tional Conference on Multimedia, MM '20, New York, NY, USA. Association for Computing Machin- ery. · DOI
  50. Sreelekshmy Selvin, R Vinayakumar, EA Gopalakrish- nan, Vijay Krishna Menon, and KP Soman. 2017. Stock price prediction using lstm, rnn and cnn- sliding window model. In 2017 international con- ference on advances in computing, communications and informatics (icacci), pages 1643-1647. IEEE.
  51. William F Sharpe. 1994. The sharpe ratio. Journal of portfolio management, 21(1):49-58.
  52. Matheus José Silva de Souza, Danilo Guimarães Franco Ramos, Marina Garcia Pena, Vinicius Amorim Sobreiro, and Herbert Kimura. 2018. Examination of the profitability of technical analysis based on moving average strategies in brics. Financial Innovation, 4(1):3.
  53. Yumo Xu and Shay B. Cohen. 2018. Stock move- ment prediction from tweets and historical prices. In Proceedings of the 56th Annual Meeting of the As- sociation for Computational Linguistics (Volume 1: Long Papers), pages 1970-1979, Melbourne, Aus- tralia. Association for Computational Linguistics.
  54. Linyi Yang, Tin Lok James Ng, Barry Smyth, and Ri- uhai Dong. 2020. Html: Hierarchical transformer- based multi-task learning for volatility prediction. In Proceedings of The Web Conference 2020, WWW '20, page 441-451, New York, NY, USA. Associa- tion for Computing Machinery. · DOI
  55. Zichao Yang, Diyi Yang, Chris Dyer, Xiaodong He, Alex Smola, and Eduard Hovy. 2016. Hierarchi- cal attention networks for document classification. In Proceedings of the 2016 conference of the North American chapter of the association for computa- tional linguistics: human language technologies, pages 1480-1489.
  56. Zhou Yu, Jun Yu, Jianping Fan, and Dacheng Tao. 2017. Multi-modal factorized bilinear pooling with co-attention learning for visual question answering. In Proceedings of the IEEE international conference on computer vision, pages 1821-1830.
  57. Z. Zhao, R. Rao, S. Tu, and J. Shi. 2017. Time- weighted lstm model with redefined labeling for stock trend prediction. In 2017 IEEE 29th Inter- national Conference on Tools with Artificial Intelli- gence (ICTAI), pages 1210-1217.

Cited by

Sorted by citations

Authors
Sawhney, R., Agarwal, S., Wadhwa, A., & Shah, R. R.
Affiliations
Indraprastha Institute of Information Technology Delhi (IN)
ORCID
Venue
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)
Publisher
Association for Computational Linguistics
Published
2020
Pages
8415-8426
DOI
10.18653/v1/2020.emnlp-main.676
OpenAlex
W3099645789
Cited by
146
References
5
Citations by year
2026: 16 · 2025: 43 · 2024: 31 · 2023: 20 · 2022: 21 · 2021: 15
FWCI
19.01
Type
conference paper
Language
en
Open access
Licence
cc-by
Locations
Conference on Empirical Methods in Natural Language Processing (EMNLP) (publishedVersion, cc-by)
Topics
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Economics, Econometrics and Finance › Finance › Financial Markets and Investment Strategies
  • Social Sciences › Business, Management and Accounting › Accounting › Financial Analysis and Corporate Governance
Keywords
stock movement prediction, social media text, graph neural networks, attention mechanism, Twitter sentiment, S&P 500, financial forecasting, investment decision-making

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