Volume 4, Number 4 (2019)
Year Launched: 2016
Journal Menu
Archive
Previous Issues
Why Us
-  Open Access
-  Peer-reviewed
-  Rapid publication
-  Lifetime hosting
-  Free indexing service
-  Free promotion service
-  More citations
-  Search engine friendly
Contact Us
Email:   service@scirea.org
Home > Journals > SCIREA Journal of Mathematics > Archive > Paper Information

The Influence of Investors’ Sentiments to Trading Volumes and Stock Returns with Information Uncertainty

Volume 4, Issue 4, August 2019    |    PP. 72-103    |PDF (421 K)|    Pub. Date: July 15, 2019
45 Downloads     536 Views  

Author(s)
Fang-Ming Hsu, Department of Information Management, National Dong Hwa University 1, Sec.2, Dahsueh Road, Shoufeng, Hualien, Taiwan
Chien-Ho Liao, Department of Information Management, National Dong Hwa University, Taiwan
Qian-Feng Liu, Department of Information Management, National Dong Hwa University, Taiwan

Abstract
Earning profit is always the paramount issue for investors. Many factors have been proved to affect stock returns, although information uncertainty is rarely discussed. This study examines how the effect of investors’ sentiments on stock returns and trading volumes is moderated by information uncertainty. Text mining technology is used to analyze the opinions of investors on a popular financial forum to investigate their thoughts and feelings about investment. After that, structural equation modeling (SEM) is used to verify the hypotheses in weekly, monthly and quarterly intervals. Our findings demonstrate that investors’ sentiments positively affect the stock returns only in the short-term (weekly) interval, and this relation is also moderated by information uncertainty in the short-term interval. Investors’ sentiments negatively affect the trading volumes in the short-term and median-term intervals, and this relation is also moderated by the information uncertainty in the short-term and median-term intervals.

Keywords
Information Uncertainty; Stock Return; Sentiment Analysis; Text Mining.

Cite this paper
Fang-Ming Hsu, Chien-Ho Liao, Qian-Feng Liu, The Influence of Investors’ Sentiments to Trading Volumes and Stock Returns with Information Uncertainty, SCIREA Journal of Mathematics. Vol. 4 , No. 4 , 2019 , pp. 72 - 103 .

References

[ 1 ] Jiang, G.H., C.M.C. Lee, and Y. Zhang, Information uncertainty and expected returns. Review of Accounting Studies, 2005. 10(2-3): p. 185-221.
[ 2 ] Zhang, X.F., Information Uncertainty and Stock Returns. Journal of Finance, 2006. 61(1): p. 105-136.
[ 3 ] Serrano-Guerrero, J., et al., Sentiment analysis: A review and comparative analysis of web services. Information Sciences, 2015. 311: p. 18-38.
[ 4 ] Yan, Z.P. and Y. Zhao, When Two Anomalies Meet: The Post-Earnings Announcement Drift and the Value-Glamour Anomaly. Financial Analysts Journal, 2011. 67(6): p. 46-60.
[ 5 ] Chen, Y.F. and H.N. Zhao, Informed trading, information uncertainty, and price momentum. Journal of Banking & Finance, 2012. 36(7): p. 2095-2109.
[ 6 ] Cheema, M.A. and G.V. Nartea, Momentum returns and information uncertainty: Evidence from China. Pacific-Basin Finance Journal, 2014. 30: p. 173-188.
[ 7 ] Tetlock, P.C., Giving content to investor sentiment: The role of media in the stock market. The Journal of Finance, 2007. 62(3): p. 1139-1168.
[ 8 ] Butler, M. and V. Kešelj, Financial forecasting using character n-gram analysis and readability scores of annual reports, in Advances in artificial intelligence. 2009, Springer. p. 39-51.
[ 9 ] Schumaker, R.P. and H. Chen, Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS), 2009. 27(2): p. 12.
[ 10 ] Li, F., The information content of forward‐looking statements in corporate filings—A naïve Bayesian machine learning approach. Journal of Accounting Research, 2010. 48(5): p. 1049-1102.
[ 11 ] Schumaker, R.P., et al., Evaluating sentiment in financial news articles. Decision Support Systems, 2012. 53(3): p. 458-464.
[ 12 ] Chan, S.W.K. and M.W.C. Chong, Sentiment analysis in financial texts. Decision Support Systems.
[ 13 ] Ahmad, K., et al., Media-expressed negative tone and firm-level stock returns. Journal of Corporate Finance, 2016. 37: p. 152-172.
[ 14 ] Das, S.R. and M.Y. Chen, Yahoo! For Amazon: Sentiment Extraction from Small Talk on the Web. Management Science, 2007. 53(9): p. 1375-1388.
[ 15 ] Bollen, J., H.N. Mao, and X.J. Zeng, Twitter mood predicts the stock market. Journal of Computational Science, 2011. 2(1): p. 1-8.
[ 16 ] Yu, Y., W. Duan, and Q. Cao, The impact of social and conventional media on firm equity value: A sentiment analysis approach. Decision Support Systems, 2013. 55(4): p. 919-926.
[ 17 ] Li, Q., et al., Media-aware quantitative trading based on public Web information. Decision Support Systems, 2014. 61: p. 93-105.
[ 18 ] Piñeiro-Chousa, J.R., M.Á. López-Cabarcos, and A.M. Pérez-Pico, Examining the influence of stock market variables on microblogging sentiment. Journal of Business Research, 2016. 69(6): p. 2087-2092.
[ 19 ] Corea, F., Can Twitter Proxy the Investors' Sentiment? The Case for the Technology Sector. Big Data Research, 2016. 4: p. 70-74.
[ 20 ] Oliveira, N., P. Cortez, and N. Areal, Stock market sentiment lexicon acquisition using microblogging data and statistical measures. Decision Support Systems, 2016. 85: p. 62-73.
[ 21 ] Guo, K., Y. Sun, and X. Qian, Can investor sentiment be used to predict the stock price? Dynamic analysis based on China stock market. Physica A: Statistical Mechanics and its Applications, 2017. 469: p. 390-396.
[ 22 ] Antweiler, W. and M.Z. Frank, Is all that talk just noise? The information content of internet stock message boards. The Journal of Finance, 2004. 59(3): p. 1259-1294.
[ 23 ] Li, N., et al., Network Environment and Financial Risk Using Machine Learning and Sentiment Analysis. Human and Ecological Risk Assessment, 2009. 15(2): p. 227-252.
[ 24 ] Price, S.M., et al., Earnings conference calls and stock returns: The incremental informativeness of textual tone. Journal of Banking & Finance, 2012. 36(4): p. 992-1011.
[ 25 ] Liu, S.M., Investor Sentiment and Stock Market Liquidity. Journal of Behavioral Finance, 2015. 16(1): p. 51-67.
[ 26 ] Kim, S.H. and D. Kim, Investor sentiment from internet message postings and the predictability of stock returns. Journal of Economic Behavior & Organization, 2014. 107: p. 708-729.
[ 27 ] Schultz, P., Discussion of "information uncertainty and expected returns". Review of Accounting Studies, 2005. 10(2-3): p. 223-226.
[ 28 ] Kim, D., On the information uncertainty risk and the January effect. Journal of Business, 2006. 79(4): p. 2127-2162.
[ 29 ] Ciccone, S.J., Investor Optimism, False Hopes and the January Effect. Journal of Behavioral Finance, 2011. 12(3): p. 158-168.
[ 30 ] Berkman, H., et al., Sell on the news: Differences of opinion, short-sales constraints, and returns around earnings announcements. Journal of Financial Economics, 2009. 92(3): p. 376-399.
[ 31 ] Francis, J., et al., Information uncertainty and post-earnings-announcement-drift. Journal of Business Finance & Accounting, 2007. 34(3-4): p. 403-433.
[ 32 ] Cheng, M., D.S. Dhaliwal, and M. Neamtiu, Asset Securitization, Securitization Recourse, and Information Uncertainty. Accounting Review, 2011. 86(2): p. 541-568.
[ 33 ] Chang, S.C. and M.T. Tsai, Long-run performance of mergers and acquisition of privately held targets: evidence in the USA. Applied Economics Letters, 2013. 20(6): p. 520-524.
[ 34 ] Chen, S.S., et al., Information Uncertainty, Earnings Management, and Long-run Stock Performance Following Initial Public Offerings. Journal of Business Finance & Accounting, 2013. 40(9-10): p. 1126-1154.
[ 35 ] Gaspar, J.M. and M. Massa, Idiosyncratic volatility and product market competition. Journal of Business, 2006. 79(6): p. 3125-3152.
[ 36 ] Gerard, X., Information Uncertainty and the Post-Earnings Announcement Drift in Europe. Financial Analysts Journal, 2012. 68(2): p. 51-69.
[ 37 ] Leippold, M. and H. Lohre, International price and earnings momentum. European Journal of Finance, 2012. 18(6): p. 535-573.
[ 38 ] Hillert, A., H. Jacobs, and S. Muller, Media Makes Momentum. Review of Financial Studies, 2014. 27(12): p. 3467-3501.
[ 39 ] Bifet, A., G. Holmes, and B. Pfahringer, MOA-TweetReader: Real-Time Analysis in Twitter Streaming Data, in Discovery Science: 14th International Conference, DS 2011, Espoo, Finland, October 5-7, 2011. Proceedings, T. Elomaa, J. Hollmén, and H. Mannila, Editors. 2011, Springer Berlin Heidelberg: Berlin, Heidelberg. p. 46-60.
[ 40 ] Nagy, A. and J. Stamberger. Crowd sentiment detection during disasters and crises. in Proceedings of the 9th International ISCRAM Conference. 2012.
[ 41 ] Khan, F.H., S. Bashir, and U. Qamar, TOM: Twitter opinion mining framework using hybrid classification scheme. Decision Support Systems, 2014. 57: p. 245-257.
[ 42 ] Hair, J.F., Multivariate Data Analysis. 1998: Prentice Hall.

Submit A Manuscript
Review Manuscripts
Join As An Editorial Member
Most Views
Article
by Sergey M. Afonin
2923 Downloads 26339 Views
Article
by Syed Adil Hussain, Taha Hasan Associate Professor
2283 Downloads 16976 Views
Article
by Omprakash Sikhwal, Yashwant Vyas
2355 Downloads 14531 Views
Article
by Munmun Nath, Bijan Nath, Santanu Roy
2249 Downloads 14384 Views
Upcoming Conferences