IDEAS home Printed from https://ideas.repec.org/r/eee/finana/v48y2016icp272-281.html
   My bibliography  Save this item

Trade the tweet: Social media text mining and sparse matrix factorization for stock market prediction

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Fan, Rui & Talavera, Oleksandr & Tran, Vu, 2023. "Information flows and the law of one price," International Review of Financial Analysis, Elsevier, vol. 85(C).
  2. Shaen Corbet & Yang (Greg) Hou & Yang Hu & Les Oxley, 2022. "We Reddit in a Forum: The Influence of Message Boards on Firm Stability," Review of Corporate Finance, now publishers, vol. 2(1), pages 151-190, March.
  3. Bouteska, Ahmed & Sharif, Taimur & Abedin, Mohammad Zoynul, 2023. "COVID-19 and stock returns: Evidence from the Markov switching dependence approach," Research in International Business and Finance, Elsevier, vol. 64(C).
  4. Joseph D. Prusa & Ryan T. Sagul & Taghi M. Khoshgoftaar, 2019. "Extracting Knowledge from Technical Reports for the Valuation of West Texas Intermediate Crude Oil Futures," Information Systems Frontiers, Springer, vol. 21(1), pages 109-123, February.
  5. Jean-Charles Bricongne & Baptiste Meunier & Raquel Caldeira, 2024. "Should Central Banks Care About Text Mining? A Literature Review," Working papers 950, Banque de France.
  6. Heba Ali, 2018. "Twitter, Investor Sentiment and Capital Markets: What Do We Know?," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 10(8), pages 158-158, August.
  7. Afees A. Salisu & Raymond Swaray & Tirimisyu F. Oloko, 2017. "A multi-factor predictive model for oil-US stock nexus with persistence, endogeneity and conditional heteroscedasticity effects," Working Papers 024, Centre for Econometric and Allied Research, University of Ibadan.
  8. Shen, Dehua & Urquhart, Andrew & Wang, Pengfei, 2019. "Does twitter predict Bitcoin?," Economics Letters, Elsevier, vol. 174(C), pages 118-122.
  9. Na, Haejung & Kim, Soonho, 2021. "Predicting stock prices based on informed traders’ activities using deep neural networks," Economics Letters, Elsevier, vol. 204(C).
  10. Wang, Fang & Gacesa, Marko, 2023. "Semi-strong efficient market of Bitcoin and Twitter: An analysis of semantic vector spaces of extracted keywords and light gradient boosting machine models," International Review of Financial Analysis, Elsevier, vol. 88(C).
  11. U, JuHyok & Lu, PengYu & Kim, ChungSong & Ryu, UnSok & Pak, KyongSok, 2020. "A new LSTM based reversal point prediction method using upward/downward reversal point feature sets," Chaos, Solitons & Fractals, Elsevier, vol. 132(C).
  12. Qiong Wu & Christopher G. Brinton & Zheng Zhang & Andrea Pizzoferrato & Zhenming Liu & Mihai Cucuringu, 2019. "Equity2Vec: End-to-end Deep Learning Framework for Cross-sectional Asset Pricing," Papers 1909.04497, arXiv.org, revised Oct 2021.
  13. Fang Wang & Marko Gacesa, 2024. "Semi-strong Efficient Market of Bitcoin and Twitter: an Analysis of Semantic Vector Spaces of Extracted Keywords and Light Gradient Boosting Machine Models," Papers 2409.15988, arXiv.org.
  14. Toan Luu Duc Huynh, 2023. "When Elon Musk Changes his Tone, Does Bitcoin Adjust Its Tune?," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 639-661, August.
  15. Anila Arif & Kashif Shafique & Khuram Ahmad Khan & Shahida Haji, 2021. "Analysis of Water Policy & Sustainable Development in Pakistan," International Journal of Agriculture & Sustainable Development, 50sea, vol. 3(4), pages 87-93, November.
  16. Yingxia Xue & Honglei Liu, 2023. "Exploration of the Dynamic Evolution of Online Public Opinion towards Waste Classification in Shanghai," IJERPH, MDPI, vol. 20(2), pages 1-15, January.
  17. Santi, Caterina, 2023. "Investor climate sentiment and financial markets," International Review of Financial Analysis, Elsevier, vol. 86(C).
  18. Maciej Wujec, 2021. "Analysis of the Financial Information Contained in the Texts of Current Reports: A Deep Learning Approach," JRFM, MDPI, vol. 14(12), pages 1-17, December.
  19. Shilpa Srivastava & Millie Pant & Varuna Gupta, 2023. "Analysis and prediction of Indian stock market: a machine-learning approach," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 14(4), pages 1567-1585, August.
  20. Kumar, Rahul & Deb, Soumya Guha & Mukherjee, Shubhadeep, 2020. "Do words reveal the latent truth? Identifying communication patterns of corporate losers," Journal of Behavioral and Experimental Finance, Elsevier, vol. 26(C).
  21. Mohammad Alomari & Abdel Razzaq Al rababa’a & Ghaith El-Nader & Ahmad Alkhataybeh, 2021. "Who’s behind the wheel? The role of social and media news in driving the stock–bond correlation," Review of Quantitative Finance and Accounting, Springer, vol. 57(3), pages 959-1007, October.
  22. Ning Wang & Shanhui Ke & Yibo Chen & Tao Yan & Andrew Lim, 2019. "Textual Sentiment of Chinese Microblog Toward the Stock Market," International Journal of Information Technology & Decision Making (IJITDM), World Scientific Publishing Co. Pte. Ltd., vol. 18(02), pages 649-671, March.
  23. Andrea Fronzetti Colladon & Stefano Grassi & Francesco Ravazzolo & Francesco Violante, 2023. "Forecasting financial markets with semantic network analysis in the COVID‐19 crisis," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 42(5), pages 1187-1204, August.
  24. Teti, Emanuele & Dallocchio, Maurizio & Aniasi, Alberto, 2019. "The relationship between twitter and stock prices. Evidence from the US technology industry," Technological Forecasting and Social Change, Elsevier, vol. 149(C).
  25. Naderi Semiromi, Hamed & Lessmann, Stefan & Peters, Wiebke, 2020. "News will tell: Forecasting foreign exchange rates based on news story events in the economy calendar," The North American Journal of Economics and Finance, Elsevier, vol. 52(C).
  26. Francisco de Arriba-P'erez & Silvia Garc'ia-M'endez & Jos'e A. Regueiro-Janeiro & Francisco J. Gonz'alez-Casta~no, 2024. "Detection of financial opportunities in micro-blogging data with a stacked classification system," Papers 2404.07224, arXiv.org.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.