154
Views
0
CrossRef citations to date
0
Altmetric
Research Article

Machine learning in predicting stock indexes: the role of online stock forum sentiment in MIDAS model

, & ORCID Icon
Pages 618-637 | Received 11 Jul 2021, Accepted 05 Apr 2023, Published online: 22 May 2023

References

  • Baker, M., and J. C. Stein. 2004. “Market Liquidity as a Sentiment Indicator.” Journal of Financial Markets 7 (3): 271–299. doi:10.1016/j.finmar.2003.11.005.
  • Baker, M., and J. Wurgler. 2006. “Investor Sentiment and the Cross-Section of Stock Returns.” The Journal of Finance 61 (4): 1645–1680. doi:https://doi.org/10.1111/j.1540-6261.2006.00885.x.
  • Brown, G. W., and M. T. Cliff. 2004. “Investor Sentiment and the Near-Term Stock Market.” Journal of Empirical Finance 11 (1): 1–27. doi:10.1016/j.jempfin.2002.12.001.
  • Brown, G. W., and M. T. Cliff. 2005. “Investor Sentiment and Asset Valuation.” The Journal of Business 78 (2): 405–440. doi:10.1086/427633.
  • Chen, M. Y., and T. H. Chen. 2019. “Modeling Public Mood and Emotion: Blog and News Sentiment and Socio-Economic Phenomena.” Future Generation Computer Systems 96: 692–699. doi:10.1016/j.future.2017.10.028.
  • Chen, J., Y. J. Liu, L. Lu, and Y. Tang. 2016. ““Investor Attention and Macroeconomic News Announcements: Evidence from Stock Index Futures.” Journal of Futures Markets 36 (3): 240–266. doi:10.1002/fut.21727.
  • Chen, Z. Y., R. Zhang, T. Xu, Y. Q. Yang, J. Y. Wang, and T. Y. Feng. 2020. “Emotional Attitudes Towards Procrastination in People: A Large-Scale Sentiment-Focused Crawling Analysis.” Computers in Human Behavior 110: 106391. doi:10.1016/j.chb.2020.106391.
  • Clements, M. P., and A. B. Galvão. 2008. “Macroeconomic Forecasting with Mixed-Frequency Data.” Journal of Business & Economic Statistics 26 (4): 546–554. doi:10.1198/073500108000000015.
  • Clements, M. P., and A. B. Galvão. 2009. “Forecasting US Output Growth Using Leading Indicators: An Appraisal Using Midas Models.” Journal of Applied Econometrics 24 (7): 1187–1206. doi:10.1002/jae.1075.
  • Crawley, E. M., D. M. Gaunt, K. Garfield, W. Hollingworth, J. A. C. Sterne, L. Beasant, S. M. Collin, N. Mills, and A. A. Montgomery. 2018. “Clinical and Cost-Effectiveness of the Lightning Process in Addition to Specialist Medical Care for Paediatric Chronic Fatigue Syndrome: Randomised Controlled Trial.” Archives of Disease in Childhood 103 (2): 155–164. doi:10.1136/archdischild-2017-313375.
  • Ding, L. L., Z. L. Lv, M. Han, X. Zhao, and W. Wang. 2019. “Forecasting China’s Wastewater Discharge Using Dynamic Factors and Mixed-Frequency Data.” Environmental Pollution 255: 113148. doi:10.1016/j.envpol.2019.113148.
  • Ding, L., Z. Zhao, and M. Han. 2021. “Probability Density Forecasts for Steam Coal Prices in China: The Role of High-Frequency Factors.” Energy 220: 119758. doi:10.1016/j.energy.2021.119758.
  • Ding, L., Z. Zhao, and L. Wang. 2022a. “A Bibliometric Review on Institutional Investor: Current Status, Development and Future Directions.” Management Decision 60 (3): 673–706. doi:https://doi.org/10.1108/MD-09-2020-1302.
  • Ding, L., Z. Zhao, and L. Wang. 2022b. “Probability Density Forecasts for Natural Gas Demand in China: Do Mixed-Frequency Dynamic Factors Matter?” Applied Energy 312: 118756. doi:10.1016/j.apenergy.2022.118756.
  • Filiz, I., T. Nahmer, and M. Spiwoks. 2019. “Herd Behavior and Mood: An Experimental Study on the Forecasting of Share Prices.” Journal of Behavioral and Experimental Finance 24: 100232. doi:10.1016/j.jbef.2019.07.004.
  • Foroni, C., M. Marcellino, and C. Schumacher. 2015. “Unrestricted Mixed Data Sampling (MIDAS): MIDAS Regressions with Unrestricted Lag Polynomials.” Journal of the Royal Statistical Society: Series A 178 (1): 57–82. doi:10.1111/rssa.12043.
  • Fu, X., G. Liu, Y. Guo, and Z. Wang. 2013. “Multi-Aspect Sentiment Analysis for Chinese Online Social Reviews Based on Topic Modeling and HowNet Lexicon.” Knowledge-Based Systems 37: 186–195. doi:10.1016/j.knosys.2012.08.003.
  • Gan, D., J. Shen, and M. Xu. 2019. “Adaptive Learning Emotion Identification Method of Short Texts for Online Medical Knowledge Sharing Community.” Computational Intelligence and Neuroscience 2019: 1–10. doi:10.1155/2019/1604392.
  • Ghysels, E., P. Santa-Clara, and R. Valkanov. 2004. “The MIDAS Touch: Mixed Data Sampling Regression Models.” Cirano Working Papers 5 (1): 512–517. https://cirano.qc.ca/files/publications/2004s-20.pdf.
  • Ghysels, E., P. Santa-Clara, and R. Valkanov. 2005. “There is a Risk-Return Trade-Off After All.” Journal of Financial Economics 76 (3): 509–548. doi:10.1016/j.jfineco.2004.03.008.
  • Ghysels, E., A. Sinko, and R. Valkanov. 2007. “MIDAS Regressions: Further Results and New Directions.” Econometric Reviews 26 (1): 53–90. doi:10.1080/07474930600972467.
  • Hirshleifer, D., and S. H. Teoh. 2003. “Herd Behaviour and Cascading in Capital Markets: A Review and Synthesis.” European Financial Management 9 (1): 25–66. doi:10.1111/1468-036X.00207.
  • Lemmon, M., and E. Portniaguina. 2006. “Consumer Confidence and Asset Prices: Some Empirical Evidence.” The Review of Financial Studies 19 (4): 1499–1529. doi:10.1093/rfs/hhj038.
  • Li, X., W. Shang, S. Y. Wang, and J. Ma. 2015. “A MIDAS Modelling Framework for Chinese Inflation Index Forecast Incorporating Google Search Data.” Electronic Commerce Research and Applications 14 (2): 112–125. doi:10.1016/j.elerap.2015.01.001.
  • Liu, C., J. Y. Ren, W. Li, and Q. Q. Zhang. 2018. “The Personalized Recommendation-Oriented Education News Crawling and Displaying System.” Software Engineering 21 (2): 38–40.
  • Long, S., M. Zhang, K. Li, and S. Wu. 2021. “Do the RMB Exchange Rate and Global Commodity Prices Have Asymmetric or Symmetric Effects on China’s Stock Prices?” Financial Innovation 7 (1): 1–21. doi:10.1186/s40854-021-00262-0.
  • Marcellino, M., and C. Schumacher. 2010. “Factor MIDAS for Nowcasting and Forecasting with Ragged-Edge Data: A Model Comparison for German GDP.” Oxford Bulletin of Economics and Statistics 72 (4): 518–550. doi:10.1111/j.1468-0084.2010.00591.x.
  • Schmeling, M. 2009. “Investor Sentiment and Stock Returns: Some International Evidence.” Journal of Empirical Finance 16 (3): 394–408. doi:10.1016/j.jempfin.2009.01.002.
  • Skuza, M., and A. Romanowski. 2015. “Sentiment Analysis of Twitter Data Within Big Data Distributed Environment for Stock Prediction.” In: 2015 Federated Conference on Computer Science and Information Systems (FedCSIS). Lódz: Poland. Washington, DC, USA: IEEE Computer Society, pp. 1349–1354. http://dx.doi.org/10.15439/2015F230.
  • Sun, B. W., N. M. Yao, and Y. X. Sun. 2020. “Research on Economic Data of Listed Companies for Ranking Forecasting.” Journal of Chinese Computer Systems 41 (1): 35–39.
  • Tetlock, P. C. 2007. “Giving Content to Investor Sentiment: The Role of Media in the Stock Market.” The Journal of Finance 62 (3): 1139–1168. doi:10.1111/j.1540-6261.2007.01232.x.
  • Vargiu, E., and M. Urru. 2013. “Exploiting Web Scraping in a Collaborative Filtering-Based Approach to Web Advertising.” Artificial Intelligence Research 2 (1): 44–54. doi:10.5430/air.v2n1p44.
  • Wang, G. S., G. J. Yu, X. H. Shen, and A. K. Mojtaba. 2020. “The Effect of Online Investor Sentiment on Stock Movements: An LSTM Approach.” Complexity 2020: 1–11. doi:10.1155/2020/4754025.
  • Yao, J. Q., X. Feng, Z. J. Wang, R. R. Ji, and W. Zhang. 2021. “Tone, Sentiment and Market Impacts: The Construction of Chinese Sentiment Dictionary in Finance.” Journal of Management Sciences in China 24 (5): 26–46. doi:10.1108/CFRI-08-2019-0134.
  • Zaier, L. H., and T. Abdelwahed. 2007. “A Polynomial Method for Temporal Disaggregation of Multivariate Time Series.” Communications in Statistics-Simulation and Computation 36 (3): 741–759. doi:10.1080/03610910601096296.
  • Zaier, L. H., and M. Abed. 2014. “Temporal Disaggregation of Economic Time Series Using Artificial Neural Networks.” Communications in Statistics-Theory and Methods 43 (8): 1824–1833. doi:10.1080/03610926.2012.677088.
  • Zhang, W., K. Huang, X. Feng, and Y. Zhang. 2017. “Market Maker Competition and Price Efficiency: Evidence from China.” Economic Modelling 66: 121–131. doi:10.1016/j.econmod.2017.06.004.
  • Zhao, X., M. Han, L. L. Ding, and A. C. Calin. 2018. “Forecasting Carbon Dioxide Emissions Based on a Hybrid of Mixed Data Sampling Regression Model and Back Propagation Neural Network in the USA.” Environmental Science and Pollution Research International 25 (3): 2899–2910. doi:10.1007/s11356-017-0642-6.
  • Zhao, M. Q., and S. Q. Wu. 2019. “Research on Stock Market Weighted Prediction Method Based on Micro-Blog Sentiment Analysis.” Data Analysis and Knowledge Discovery 3 (2): 43–51. doi:10.1109/IJCNN.2016.7727786.
  • Zheng, P., P. C. Adams, and J. Wang. 2021. “Shifting Moods on Sina Weibo: The First 12 Weeks of COVID-19 in Wuhan.” New Media & Society 146144482110588. doi:10.1177/14614448211058850.
  • Zheng, Y., D. Y. Dong, and H. Q. Zhu. 2016. “Does Heterogeneous Sentiment Influence on Stock Market Herding: Evidence from Internet Stock Community.” Systems Engineering 34 (9): 9–14.
  • Zhong, X., and D. Enke. 2017. “Forecasting Daily Stock Market Return Using Dimensionality Reduction.” Expert Systems with Applications 67: 126–139. doi:https://doi.org/10.1016/j.eswa.2016.09.027.
  • Zhong, X., and D. Enke. 2019. “Predicting the Daily Return Direction of the Stock Market Using Hybrid Machine Learning Algorithms.” Financial Innovation 5 (1): 1–20. doi:10.1186/s40854-019-0138-0.
  • Zhou, Y., R. Ji, J. Su, and J. Yao. 2020. “Uncovering Media Bias via Social Network Learning.” ACM Transactions on Intelligent Systems and Technology (TIST) 12 (1): 1–12. doi:10.1145/3422181.
  • Zweig, M. E. 1973. “An Investor Expectations Stock Price Predictive Model Using Closed-End Fund Premiums.” The Journal of Finance 28 (1): 67–78. doi:10.1111/j.1540-6261.1973.tb01346.x.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.