SOCIAL NETWORK NEWS SENTIMENTS AND STOCK PRICE MOVEMENT: A CORRELATION ANALYSIS

Authors

  • Anupong Sukprasert Mahasarakham Business School, Mahasarakham University, Mahasarakham, Thailand
  • Kasturi Kanchymalay Universiti Teknikal Malaysia Melaka, Melaka, Malaysia
  • Naomie Salim Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia
  • Atif Khan Faculty of Computing, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

DOI:

https://doi.org/10.11113/jt.v77.6565

Keywords:

Stock market, news sentiment, correlation analysis

Abstract

The stock market prediction is one of the most important issues extensively investigated in the existing academic literatures. Researchers have discovered that real–time news has much bearing on the movement of stock prices. Analysts now have to deal with vast amounts of real time, unstructured streaming data due to the advent of electronic and online news sources. This paper aims to investigate the relationship between online news and actual stock price movement.  R programming together with R package are applied to capture and analyze the online news data from Yahoo Financial. The data are plotted into graphs to analyze the relationship between the two variables. In addition, to ensure the levels of the relationship, the Pearson’s correlation and Spearman’s Rank are applied to test whether there is a statistical association between these two variables. This initial analysis of dynamic online news based on sentimental words is relatively constructive.

References

P. Chang, C. Fan, and C. Liu. 2009. Integrating a Piecewise Linear Representation Method and a Neural Network Model for Stock Trading Points Prediction. IEEE Trans. Syst. Man, Cybern. Part C (Applications Rev.). 39(1): 80-92.

R. D. Edwards, J. Magee, and W. H. C. Bassetti. 2012. Technical Analysis of Stock Trends. 9th ed. CRC Press.

R. J. Bauer and J. R. Dahlquist. 1999. Technical Markets Indicators: Analysis & Performance.vol. 64. John Wiley & Sons,

C. D. Kirkpatrick II and J. Dahlquist. 2010. Technical Analysis: The Complete Resource for Financial Market Technicians. FT Press.

R. Goonatilake and S. Herath. 2007. The Volatility of the Stock Market and News. Int. Res. J. Financ. Econ. 3(11): 53-65.

N. Godbole, M. Srinivasaiah, and S. Skiena. 2007. Large-Scale Sentiment Analysis for News and Blogs. ICWSM. 7.

J. Leskovec, L. Backstrom, and J. Kleinberg. 2009. Meme-tracking and the Dynamics of the News Cycle. In Proceedings of the 15th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. 497-506.

W.-B. Yu, B.-R. Lea, and B. Guruswamy. 2007. A Theoretic Framework Integrating Text Mining and Energy Demand Forecasting. IJEBM. 5(3): 211-224.

S. Theußl, I. Feinerer, and K. Hornik. 2009. Distributed Text Mining with tm. In The R User Conference.

P. Hofmarcher, S. Theussl, and K. Hornik, 2011. Do Media Sentiments Reflect Economic Indices. Chinese Bus. Rev. 10(7): 487-492.

A. Nagar and M. Hahsler. 2012. Using Text and Data Mining Techniques to extract Stock Market Sentiment from Live News Streams. In 2012 International Conference on Computer Technology and Science. 47(Iccts): 91-95.

I. Feinerer. 2014. Introduction to the tm Package Text Mining in R,†nd) n. pag. Web. 1-8.

M. Annau. Package ‘tm.plugin.webmining’: Retrieve structured, textual data from various web sources. 2014. [Online]. Available: http://cran.r-project.org/web/packages/tm.plugin.webmining/tm.plugin.webmining.pdf.

M. Hu and B. Liu. 2004. Mining and summarizing customer reviews. In Proceedings of the Tenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD ’04. 168-177.

J. Hair, R. Anderson, R. Tatham, and W. Black. 1995. Multivariate Data Analysis. 4th Edition with readings. New Jersey: Prentice Hall.

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Published

2015-12-01

How to Cite

SOCIAL NETWORK NEWS SENTIMENTS AND STOCK PRICE MOVEMENT: A CORRELATION ANALYSIS. (2015). Jurnal Teknologi, 77(20). https://doi.org/10.11113/jt.v77.6565