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A User-Centric Analysis of Social Media for Stock Market Prediction

Published:27 March 2023Publication History
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Abstract

Social media platforms such as Twitter or StockTwits are widely used for sharing stock market opinions between investors, traders, and entrepreneurs. Empirically, previous work has shown that the content posted on these social media platforms can be leveraged to predict various aspects of stock market performance. Nonetheless, actors on these social media platforms may not always have altruistic motivations and may instead seek to influence stock trading behavior through the (potentially misleading) information they post. While a lot of previous work has sought to analyze how social media can be used to predict the stock market, there remain many questions regarding the quality of the predictions and the behavior of active users on these platforms. To this end, this article seeks to address a number of open research questions: Which social media platform is more predictive of stock performance? What posted content is actually predictive, and over what time horizon? How does stock market posting behavior vary among different users? Are all users trustworthy or do some user’s predictions consistently mislead about the true stock movement? To answer these questions, we analyzed data from Twitter and StockTwits covering almost 5 years of posted messages spanning 2015 to 2019. The results of this large-scale study provide a number of important insights among which we present the following: (i) StockTwits is a more predictive source of information than Twitter, leading us to focus our analysis on StockTwits; (ii) on StockTwits, users’ self-labeled sentiments are correlated with the stock market but are only slightly predictive in aggregate over the short-term; (iii) there are at least three clear types of temporal predictive behavior for users over a 144 days horizon: short, medium, and long term; and (iv) consistently incorrect users who are reliably wrong tend to exhibit what we conjecture to be “botlike” post content and their removal from the data tends to improve stock market predictions from self-labeled content.

REFERENCES

  1. [1] AlDayel Abeer and Magdy Walid. 2021. Stance detection on social media: State of the art and trends. Inf. Process. Manage. 58, 4 (2021), 102597. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  2. [2] Kumar Shamanth, Barbier Geoffrey, Abbasi Mohammad Ali, and Liu Huan. 2011. TweetTracker: An analysis tool for humanitarian and disaster relief. In Proceedings of the International AAAI Conference on Web and Social Media a (ICWSM’11). 7882.Google ScholarGoogle Scholar
  3. [3] Bouadjenek Mohamed Reda, Sanner Scott, and Du Yihao. 2020. Relevance- and interface-driven clustering for visual information retrieval. Inf. Syst. 94 (2020), 101592. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  4. [4] Bouadjenek Mohamed Reda and Sanner Scott. 2019. Relevance-driven clustering for visual information retrieval on Twitter. In Proceedings of the Conference on Human Information Interaction and Retrieval (CHIIR’19). Association for Computing Machinery, New York, NY, 349353. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  5. [5] Thomas Charles, McCreadie Richard, and Ounis Iadh. 2019. Event tracker: A text analytics platform for use during disasters. In Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR’19). Association for Computing Machinery, New York, NY, 13411344. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  6. [6] Cameron Michael P., Barrett Patrick, and Stewardson Bob. 2016. Can social media predict election results? Evidence from New Zealand. J. Pol. Market. 15, 4 (2016), 416432. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  7. [7] Colleoni Elanor, Rozza Alessandro, and Arvidsson Adam. 2014. Echo Chamber or public sphere? Predicting political orientation and measuring political homophily in Twitter using big data. J. Commun. 64, 2 (2014), 317332. arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/jcom.12084.Google ScholarGoogle ScholarCross RefCross Ref
  8. [8] Ni Ming, He Qing, and Gao Jing. 2016. Forecasting the subway passenger flow under event occurrences with social media. IEEE Trans. Intell. Transport. Syst. 18, 6 (2016), 16231632.Google ScholarGoogle Scholar
  9. [9] Feldman Ronen, Netzer Oded, Peretz Aviv, and Rosenfeld Binyamin. 2015. Utilizing text mining on online medical forums to predict label change due to adverse drug reactions. In Proceedings of the 21th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD’15). Association for Computing Machinery, New York, NY, 17791788. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  10. [10] Yates Andrew, Goharian Nazli, and Frieder Ophir. 2015. Extracting adverse drug reactions from social media. In Proceedings of the 29th AAAI Conference on Artificial Intelligence.Google ScholarGoogle ScholarCross RefCross Ref
  11. [11] Lira Vinicius Monteiro de, Macdonald Craig, Ounis Iadh, Perego Raffaele, Renso Chiara, and Times Valeria Cesario. 2019. Event attendance classification in social media. Inf. Process. Manage. 56, 3 (2019), 687703. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  12. [12] Bollen Johan and Mao Huina. 2011. Twitter mood as a stock market predictor. Computer 44, 10 (Ocber2011), 9194. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  13. [13] Bachelier Louis. 1900. Théorie de la spéculation. Ann. sci. l’École Norm. Sup. 3e, 17 (1900), 2186. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  14. [14] Nguyen Thien Hai and Shirai Kiyoaki. 2015. Topic modeling based sentiment analysis on social media for stock market prediction. In Proceedings of the Joint Conference of the Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP’15) (Volume 1: Long Papers). Association for Computational Linguistics, 13541364. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  15. [15] Sun Andrew, Lachanski Michael, and Fabozzi Frank J.. 2016. Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction. Int. Rev. Financ. Anal. 48 (2016), 272281. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  16. [16] Mahmoudi Nader, Docherty Paul, and Moscato Pablo. 2018. Deep neural networks understand investors better. Decis. Supp. Syst. 112 (2018), 2334. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  17. [17] Luo Xueming, Zhang Jie, and Duan Wenjing. 2013. Social media and firm equity value. Inf. Syst. Res. 24, 1 (2013), 146163.Google ScholarGoogle ScholarDigital LibraryDigital Library
  18. [18] Pagolu V. S., Reddy K. N., Panda G., and Majhi B.. 2016. Sentiment analysis of Twitter data for predicting stock market movements. In Proceedings of the International Conference on Signal Processing, Communication, Power and Embedded System (SCOPES’16). 13451350.Google ScholarGoogle ScholarCross RefCross Ref
  19. [19] Pineiro-Chousa Juan, Vizcaíno-González Marcos, and Pérez-Pico Ada María. 2017. Influence of social media over the stock market. Psychol. Market. 34, 1 (2017), 101108. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  20. [20] Nguyen Thien Hai, Shirai Kiyoaki, and Velcin Julien. 2015. Sentiment analysis on social media for stock movement prediction. Expert Syst. Appl. 42, 24 (2015), 96039611. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  21. [21] Attigeri G. V., M. Manohara Pai M., Pai R. M., and Nayak A.. 2015. Stock market prediction: A big data approach. In Proceedings of the IEEE Region 10 International Conference (TENCON’15). 15.Google ScholarGoogle ScholarCross RefCross Ref
  22. [22] Chen Chen, Dongxing Wu, Chunyan Hou, and Xiaojie Yuan. 2014. Exploiting social media for stock market prediction with factorization machine. In Proceedings of the IEEE/WIC/ACM International Joint Conferences on Web Intelligence (WI’14) and Intelligent Agent Technologies (IAT’14), Vol. 2. 142149.Google ScholarGoogle ScholarDigital LibraryDigital Library
  23. [23] Jin Fang, Wang Wei, Chakraborty Prithwish, Self Nathan, Chen Feng, and Ramakrishnan Naren. 2017. Tracking multiple social media for stock market event prediction. In Advances in Data Mining. Applications and Theoretical Aspects, Perner Petra (Ed.). Springer International Publishing, Cham, 1630.Google ScholarGoogle Scholar
  24. [24] Zong Shi, Ritter Alan, and Hovy Eduard. 2020. Measuring forecasting skill from text. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics. Association for Computational Linguistics, 53175331. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  25. [25] Chen Chung-Chi, Huang Hen-Hsen, and Chen Hsin-Hsi. 2021. Evaluating the rationales of amateur investors. In Proceedings of the International World Wide Web Conference (WWW’21). Association for Computing Machinery, New York, NY, 39873998. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  26. [26] Fama Eugene F., Fisher Lawrence, Jensen Michael C., and Roll Richard. 1969. The adjustment of stock prices to new information. Int. Econ. Rev. 10, 1 (1969), 121. http://www.jstor.org/stable/2525569.Google ScholarGoogle ScholarCross RefCross Ref
  27. [27] Fama Eugene F.. 1991. Efficient capital markets: II. J. Financ. 46, 5 (1991), 15751617. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  28. [28] Abarbanell Jeffrey S. and Bushee Brian J.. 1997. Fundamental analysis, future earnings, and stock prices. J. Account. Res. 35, 1 (1997), 124. http://www.jstor.org/stable/2491464.Google ScholarGoogle ScholarCross RefCross Ref
  29. [29] Greig Anthony C.. 1992. Fundamental analysis and subsequent stock returns. J. Account. Econ. 15, 2 (1992), 413442. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  30. [30] Zarkias Konstantinos Saitas, Passalis Nikolaos, Tsantekidis Avraam, and Tefas Anastasios. 2019. Deep reinforcement learning for financial trading using price trailing. In Proceedings of the IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP’19). 30673071.Google ScholarGoogle ScholarCross RefCross Ref
  31. [31] Cervello-Royo Roberto, Guijarro Francisco, and Michniuk Karolina. 2015. Stock market trading rule based on pattern recognition and technical analysis: Forecasting the DJIA index with intraday data. Expert Syst. Appl. 42, 14 (2015), 59635975. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  32. [32] Aboussalah Amine Mohamed and Lee Chi-Guhn. 2020. Continuous control with stacked deep dynamic recurrent reinforcement learning for portfolio optimization. Expert Syst. Appl. 140 (2020), 112891. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  33. [33] Bollen Johan, Mao Huina, and Zeng Xiaojun. 2011. Twitter mood predicts the stock market. J. Comput. Sci. 2, 1 (2011), 18. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  34. [34] Derakhshan Ali and Beigy Hamid. 2019. Sentiment analysis on stock social media for stock price movement prediction. Eng. Appl. Artif. Intell. 85 (2019), 569578. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  35. [35] Rao Tushar and Srivastava Saket. 2012. Using Twitter Sentiments and Search Volumes Index to Predict Oil, Gold, Forex and Markets Indices. Technical Report. Institute of Technology, Delhi, India.Google ScholarGoogle Scholar
  36. [36] Karabulut Yigitcan. 2013. Can facebook predict stock market activity? In AFA Meetings Paper.Google ScholarGoogle Scholar
  37. [37] Kramer Adam D. I., Guillory Jamie E., and Hancock Jeffrey T.. 2014. Experimental evidence of massive-scale emotional contagion through social networks. Proc. Natl. Acad. Sci. U.S.A. 111, 24 (2014), 87888790.Google ScholarGoogle ScholarCross RefCross Ref
  38. [38] Wang Charlie and Luo Ben. 2021. Predicting $ GME stock price movement using sentiment from Reddit r/wallstreetbets. In Proceedings of the 3rd Workshop on Financial Technology and Natural Language Processing. 2230.Google ScholarGoogle Scholar
  39. [39] Huynh Dennis, Audet Garrett, Alabi Nikolay, and Tian Yuan. 2021. Stock price prediction leveraging Reddit: The role of trust filter and sliding window. In Proceedings of the IEEE International Conference on Big Data (Big Data’21). 10541060. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  40. [40] Houlihan Patrick and Creamer Germán G.. 2019. Leveraging social media to predict continuation and reversal in asset prices. Comput. Econ. (2019), 121.Google ScholarGoogle Scholar
  41. [41] Li Quanzhi and Shah Sameena. 2017. Learning stock market sentiment Lexicon and sentiment-oriented word vector from StockTwits. In Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL’17). Association for Computational Linguistics, Vancouver, Canada, 301310. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  42. [42] Chen Chung-Chi, Huang Hen-Hsen, and Chen Hsin-Hsi. 2020. Issues and perspectives from 10,000 annotated financial social media data. In Proceedings of the 12th Language Resources and Evaluation Conference. European Language Resources Association, Paris, France, 61066110. https://aclanthology.org/2020.lrec-1.749.Google ScholarGoogle Scholar
  43. [43] Bar-Haim Roy, Dinur Elad, Feldman Ronen, Fresko Moshe, and Goldstein Guy. 2011. Identifying and following expert investors in stock microblogs. In Proceedings of the Conference on Empirical Methods in Natural Language Processing. Association for Computational Linguistics, 13101319.Google ScholarGoogle ScholarDigital LibraryDigital Library
  44. [44] Peng Bo, Chersoni Emmanuele, Hsu Yu-Yin, and Huang Chu-Ren. 2021. Is domain adaptation worth your investment? Comparing BERT and FinBERT on financial tasks. In Proceedings of the 3rd Workshop on Economics and Natural Language Processing. Association for Computational Linguistics, 3744. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  45. [45] Cortis Keith, Freitas André, Daudert Tobias, Huerlimann Manuela, Zarrouk Manel, Handschuh Siegfried, and Davis Brian. 2017. SemEval-2017 task 5: Fine-grained sentiment analysis on financial microblogs and news. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval’17). Association for Computational Linguistics, 519535. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  46. [46] Yang Steve Y., Mo Sheung Yin Kevin, and Liu Anqi. 2015. Twitter financial community sentiment and its predictive relationship to stock market movement. Quant. Financ. 15, 10 (2015), 16371656. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  47. [47] Mehrotra Rishabh, Sanner Scott, Buntine Wray, and Xie Lexing. 2013. Improving LDA topic models for microblogs via tweet pooling and automatic labeling. In Proceedings of the ACM SIGIR Conference (SIGIR’13). Association for Computing Machinery, New York, NY, 889892. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  48. [48] Alvarez-Melis David and Saveski Martin. 2016. Topic modeling in Twitter: Aggregating tweets by conversations. In Proceedings of the 10th International AAAI Conference on Web and Social Media.Google ScholarGoogle Scholar
  49. [49] Iman Zahra, Sanner Scott, Bouadjenek Mohamed Reda, and Xie Lexing. 2017. A longitudinal study of topic classification on Twitter. In Proceedings of the 11th International AAAI Conference on Web and Social Media a (ICWSM’17). 552555.Google ScholarGoogle ScholarCross RefCross Ref
  50. [50] Bouadjenek Mohamed Reda, Sanner Scott, Iman Zahra, Xie Lexing, and Shi Daniel Xiaoliang. 2022. A longitudinal study of topic classification on Twitter. PeerJ Comput. Sci. 8 (2022), e991.Google ScholarGoogle ScholarCross RefCross Ref
  51. [51] Hutto C. and Gilbert Eric. 2014. VADER: A parsimonious rule-based model for sentiment analysis of social media text. In Proceedings of the 8th International AAAI Conference on Web and Social Media a (ICWSM’14).Google ScholarGoogle ScholarCross RefCross Ref
  52. [52] Carvalho Jonnathan and Plastino Alexandre. 2021. On the evaluation and combination of state-of-the-art features in twitter sentiment analysis. Artif. Intell. Rev. 54, 3 (2021), 18871936.Google ScholarGoogle ScholarDigital LibraryDigital Library
  53. [53] Ribeiro Filipe N., Araújo Matheus, Gonçalves Pollyanna, Gonçalves Marcos André, and Benevenuto Fabrício. 2016. Sentibench-a benchmark comparison of state-of-the-practice sentiment analysis methods. EPJ Data Sci. 5, 1 (2016), 129.Google ScholarGoogle ScholarCross RefCross Ref
  54. [54] Bonta Venkateswarlu, Janardhan Nandhini Kumaresh, and N.. 2019. A comprehensive study on lexicon based approaches for sentiment analysis. As. J. Comput. Sci. Technol. 8, S2 (2019), 16.Google ScholarGoogle ScholarCross RefCross Ref
  55. [55] Borg Anton and Boldt Martin. 2020. Using VADER sentiment and SVM for predicting customer response sentiment. Expert Syst. Appl. 162 (2020), 113746. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  56. [56] Bose D. R., Aithal P. S., Roy Sandip, et al. 2021. Survey of Twitter viewpoint on application of drugs by VADER sentiment analysis among distinct countries. Int. J. Manage. Technol. Soc. Sci. 6, 1 (2021), 110127.Google ScholarGoogle Scholar
  57. [57] Pano Toni and Kashef Rasha. 2020. A complete VADER-based sentiment analysis of Bitcoin (BTC) tweets during the era of COVID-19. Big Data Cogn. Comput. 4, 4 (2020). DOI:Google ScholarGoogle ScholarCross RefCross Ref
  58. [58] Chen Yuxiao, Yuan Jianbo, You Quanzeng, and Luo Jiebo. 2018. Twitter sentiment analysis via Bi-sense emoji embedding and attention-based LSTM. In Proceedings of the 26th ACM International Conference on Multimedia (MM’18). Association for Computing Machinery, New York, NY, 117125. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  59. [59] Botchway Raphael Kwaku, Jibril Abdul Bashiru, Kwarteng Michael Adu, Chovancova Miloslava, and Oplatková Zuzana Komínková. 2019. A review of social media posts from UniCredit bank in Europe: A sentiment analysis approach. In Proceedings of the 3rd International Conference on Business and Information Management (ICBIM’19). Association for Computing Machinery, New York, NY, 7479. DOI:Google ScholarGoogle ScholarDigital LibraryDigital Library
  60. [60] Bishop Christopher M.. 2006. Pattern Recognition and Machine Learning. Springer, Berlin.Google ScholarGoogle ScholarDigital LibraryDigital Library
  61. [61] Dedeturk Bilge Kagan and Akay Bahriye. 2020. Spam filtering using a logistic regression model trained by an artificial bee colony algorithm. Appl. Soft Comput. 91 (2020), 106229. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  62. [62] Gupta Shagun, Ko Dennis T., Azizi Paymon, Bouadjenek Mohamed Reda, Koh Maria, Chong Alice, Austin Peter C., and Sanner Scott. 2020. Evaluation of machine learning algorithms for predicting readmission after acute myocardial infarction using routinely collected clinical data. Can. J. Cardiol. 36, 6 (2020), 878885. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  63. [63] Lee Kyoungjae and Cao Xuan. 2021. Bayesian group selection in logistic regression with application to MRI data analysis. Biometrics 77, 2 (2021), 391400.Google ScholarGoogle ScholarCross RefCross Ref
  64. [64] Fan Rong-En, Chang Kai-Wei, Hsieh Cho-Jui, Wang Xiang-Rui, and Lin Chih-Jen. 2008. LIBLINEAR: A library for large linear classification. J. Mach. Learn. Res. 9 (August 2008), 18711874.Google ScholarGoogle ScholarDigital LibraryDigital Library
  65. [65] Church Kenneth Ward and Hanks Patrick. 1990. Word association norms, mutual information, and lexicography. Comput. Ling. 16, 1 (1990), 2229. https://www.aclweb.org/anthology/J90-1003.Google ScholarGoogle ScholarDigital LibraryDigital Library
  66. [66] Brown Tom B., Mann Benjamin, Ryder Nick, Subbiah Melanie, Kaplan Jared, Dhariwal Prafulla, Neelakantan Arvind, Shyam Pranav, Sastry Girish, Askell Amanda, Agarwal Sandhini, Herbert-Voss Ariel, Krueger Gretchen, Henighan Tom, Child Rewon, Ramesh Aditya, Ziegler Daniel M., Wu Jeffrey, Winter Clemens, Hesse Christopher, Chen Mark, Sigler Eric, Litwin Mateusz, Gray Scott, Chess Benjamin, Clark Jack, Berner Christopher, McCandlish Sam, Radford Alec, Sutskever Ilya, and Amodei Dario. 2020. Language models are few-shot learners. arxiv:cs.CL/2005.14165. Retrieved from https://arxiv.org/abs/2005.14165.Google ScholarGoogle Scholar
  67. [67] Clark Elizabeth, August Tal, Serrano Sofia, Haduong Nikita, Gururangan Suchin, and Smith Noah A.. 2021. All that’s “human” is not gold: Evaluating human evaluation of generated text. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers). Association for Computational Linguistics, 72827296. DOI:Google ScholarGoogle ScholarCross RefCross Ref
  68. [68] Devlin Jacob, Chang Ming-Wei, Lee Kenton, and Toutanova Kristina. 2019. BERT: Pre-training of deep bidirectional transformers for language understanding. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers). Association for Computational Linguistics, 41714186.Google ScholarGoogle Scholar

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        cover image ACM Transactions on the Web
        ACM Transactions on the Web  Volume 17, Issue 2
        May 2023
        170 pages
        ISSN:1559-1131
        EISSN:1559-114X
        DOI:10.1145/3589222
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        Publication History

        • Published: 27 March 2023
        • Online AM: 29 September 2022
        • Accepted: 5 September 2022
        • Revised: 15 June 2022
        • Received: 17 December 2021
        Published in tweb Volume 17, Issue 2

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