Abstract
In finance task domain, it is indispensable to get and analyze information as quickly as possible. Analyst’s reports are one of the important information in asset management, and these include a large amount of text information. However, it is very difficult to handle text information of analyst’s reports, few research and development have been conducted. In [5] and [6] we explored the feasibility to extract valuable knowledge for asset management through text mining using analyst’s reports as text data. And we found the effectiveness of keyword information. In this paper we make further research of analyst’s reports. From empirical study on the practical data, we have confirmed the effectiveness of using keyword information and numerical information together: (1) the effectiveness of keyword information is different by the direction of change of earning estimate; (2) the keyword of “Upward (or Downward) surprise in forecast” has strong effect to stock price return.
Preview
Unable to display preview. Download preview PDF.
Similar content being viewed by others
References
Antweiler, W., Frank, M.Z.: Is All That Talk Just Noise? The Information Content of Internet Stock Message Boards. The Journal of Finance 59, 1259–1294 (2004)
Chopra, V.K.: Why So Much Error in Analysts’ Earnings Forecasts? Financial Analysts Journal, 35–42 (November/December 1998)
Clement, M.B.: Analyst Forecast Accuracy: Do Ability, Resources, and Portfolio Complexity Matter? Journal of Accounting and Economics 27, 285–303 (1999)
Dreman, D., Berry, M.: Analyst Forecasting Error and Their Implications for Security Analysis. Finance Analysts Journal 51(3), 30–41 (1995)
Takahashi, S., Masakazu, T., Takahashi, H., Tsuda, K.: Learning Value-Added Information of Asset Management from Analyst Reports Through Text Mining. In: Khosla, R., Howlett, R.J., Jain, L.C. (eds.) KES 2005. LNCS (LNAI), vol. 3684, pp. 785–791. Springer, Heidelberg (2005)
Takahashi, S., Takahashi, H., Tsuda, K., Terano, T.: Analyzing Asset Management Knowledge from Analyst’s Reports through Text Mining, International IPSI 2004, 2004.11
Wuthrich, B., Cho, V., Leung, S., Permunetilleke, D., Sankaran, K., Zhang, J., Lam, W.: Daily Prediction of Major Stock Indices from Textual WWW Data. In: KDDM 1998 Conference, pp. 364–368. AAAI Press, NY (1998)
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2006 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Takahashi, S., Takahashi, M., Takahashi, H., Tsuda, K. (2006). Analysis of Stock Price Return Using Textual Data and Numerical Data Through Text Mining. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds) Knowledge-Based Intelligent Information and Engineering Systems. KES 2006. Lecture Notes in Computer Science(), vol 4252. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11893004_40
Download citation
DOI: https://doi.org/10.1007/11893004_40
Publisher Name: Springer, Berlin, Heidelberg
Print ISBN: 978-3-540-46537-9
Online ISBN: 978-3-540-46539-3
eBook Packages: Computer ScienceComputer Science (R0)