Stock Market Prediction by Regression Model with Social Moods

This paper presents a regression model with
autocorrelated errors in which the inputs are social moods obtained by
analyzing the adjectives in Twitter posts using a document topic
model, where document topics are extracted using LDA. The
regression model predicts Dow Jones Industrial Average (DJIA) more
precisely than autoregressive moving-average models.

• Masahiro Ohmura
• Koh Kakusho
• Takeshi Okadome

• Regression model
• social mood
• stock market prediction
• Twitter.

[1] Bollen, J., H. Mao, and X.-J. Zeng (2011). Twitter mood predicts the
stock market, Journal of Computational Science, 2, 1, pp.1-8.
[2] Lorr, M., D. M. McNair, and J. Heuchert (2011). Poms bi-polar manual
supplement. Multi-Health System Inc.
[3] Ohomura, M., K. Kakusho, T. Okadome (2014). Social mood extraction
from Twitter posts with document topic model, Proceedings of the Fifth
International Conference on Information Science and Applications
(ICISA2014), 357-360, Soul, May 2014.
[4] Golder, S. A. and M. W. Macy (2011). Diurnal and seasonal mood vary
with work, sleep, and daylength across diverse cultures. Science, 333,
6051, pp. 1878-1881.
[5] Lee, C.-H., C.-H. Wu, and T.-F. Chien (2011). BursT: A dynamic term
weighting scheme for mining microblogging messages, Proceedings of
the 8th international Conference on Advances in Neural Networks -
Volume Part III, pp. 548-557.
[6] Blei, D. M., A. Y. Ng, and M. I. Jordan (2003). Latent Dirichlet
allocation, Journal of Machine Learning Research, 3, pp. 993-1022.
[7] Blei, D. M. (2012). Probabilistic topic model, Communications of the
Association for Computing Machinery, 55, 4, pp. 77-84.
[8] Fellbaum, C. and G. Miller (1988). WordNet: An Electronic Lexical
Database (Language, Speech, and Communication). MIT Press.
[9] Shumway, R. H. and D. S. Stoffer (2011). Time Series Analysis and Its
Applications (Third Edition). Springer, New York.