Novel Approach for Detecting Stock Price Movements
DOI:
https://doi.org/10.26438/ijcse/v7i6.191196Keywords:
Corporate communication, data mining, organizational, performance, stock predictionAbstract
Grounded on communication theories, we propose to use a data-mining algorithm to detect communication patterns within a company to determine if such patterns may reveal the performance of the company. Specifically, we would like to find out whether or not there exist any association relationships between the frequency of e-mail exchange of the key employees in a company and the performance of the company as reected in its stock prices. If such relationships do exist, we would also like to know whether or not the companys stock price could be accurately predicted based on the detected relationships. To detect the association relationships, a data-mining algorithm is proposed here to mine e-mail communication records and historical stock prices so that based on the detected relationship, rules that can predict changes in stock prices can be constructed. Using the data-mining algorithm and a set of publicly available Enron e-mail corpus and Enrons stock prices recorded during the same period, we discovered the existence of interesting, statistically signi_cant, association relationships in the data. In addition, we also discovered that these relationships can predict stock price movements with an average accuracy of around 80 percent. Given the increasing popularity of social networks, the mining of interesting communication patterns could provide insights into the development of many useful applications in many areas.
References
[1] W. Duan, B. Gu, and A. B. Whinston, Do online reviews matter An empirical investigation of panel data, Decision Support Syst., vol. 45, no. 4, pp. 10071016, 2008.
[2] E. Vamsidhar, K. V. S. R. P. Varma, P. S. Rao, and R. Satapati, Prediction of rainfall using backpropagation neural network model, Int. J. Comput. Sci. Eng., vol. 2, no. 4, pp. 11191121, 2010.
[3] G. Miller, Social scientists wade into the tweet stream, Science, vol. 333, no. 6051, pp. 18141815, 2011.
[4] C. Hargreaves and Y. Hao, Prediction of stock performance using analytical techniques, J. Emerg. Technol. Web Intell., vol. 5, no. 2, pp. 136142, 2013.
[5] Sawant and P. M. Chawan, Study of Data Mining Techniques Used for Financial Data Analysis, Int. J. Eng. Sci. Innov. Technol., vol. 2, no. 3, pp. 503509, 2013.
[6] S. C. Nicholis and D. J. T. Sumpter, A dynamical approach to stock market uctuations, Int. J. Bifurcation Chaos, vol. 21, no. 12, pp. 35573564, 2011.
[7] M. Gahirwal and M. Vijayalakshmi, Inter time series sales forecasting, Int. J. Adv. Stud. Comput., Sci. Eng., vol. 2, no. 1, pp. 5566, 2013.
[8] [8] M. De Choudhury, H. Sundaram, A. John, and D. D. Seligmann, Can blog communication dynamics be correlated with stock market activity? in Proc. 19th ACM Conf. Hypertext Hypermedia, 2008, pp. 5560.
[9] R. Dash, R. L. Paramguru, and R. Dash, Comparative nalysis of supervised and unsupervised discretization techniques, Int. J. Adv. Sci. Technol., vol. 2, no. 3, pp.2937, 2011.
[10] J. Diesner, T. L. Frantz, and K. M. Carley, Communication networks from the Enron email corpus Its always about the people. Enron is no di_erent, School Comput. Sci., Inst. Softw. Res., Carnegie MellonUniv., Pittsburgh, PA, USA, Tech. Rep., vol. 11, no. 3, 2006. [Online]. Available: http://repository.cmu.edu/isr/46.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors contributing to this journal agree to publish their articles under the Creative Commons Attribution 4.0 International License, allowing third parties to share their work (copy, distribute, transmit) and to adapt it, under the condition that the authors are given credit and that in the event of reuse or distribution, the terms of this license are made clear.
