Total views : 272

An Automated Framework for Incorporating Fine- Grained News Data into S&P BSE SENSEX Stock Trading Strategies


  • Computer Science Department, Chandigarh University, Rohtak - 124001, Haryana, India
  • Computer Science Department, Chandigarh University, Ludhiana - 141001, Punjab, India


Objective: In this paper, we purpose a framework for automatic incorporation of fine-grained news data to generate S&P BSE Sensex trading strategies. Method/Statistical Analysis: Financial news containing information about companies events, share prices, sales, Government and economic policies etc., are fetched from web. For fine-grained news data, text mining is applied to pre-process the news. Sentiments analysis is done to estimate the exact sentiment of news messages containing contradictory words and input at right place into the framework. News entities specific to stock market behavior prediction are extracted from fine-grained news data. Pearson Correlation characterizes the relation between stock returns and expert defined Impact Factor (IF). Findings: The purposed framework incorporates the news dataset specific to 30 companies listed under S&P BSE Sensex stock index. Stock returns corresponding to news entities generates the buy or sell signal. The results depicting positive value of Pearson’s correlation describes that market moving in right direction. It is proved that the specified news entities incorporated at right place into the framework have significant effect on stock movement and generates higher magnitude of returns as compared to other entities used in previous work. The framework gives approximately 94% accuracy which is greater than other news based trading model. Application/Improvements: The accuracy of purposed framework can be improved by combining a number of technical trading indicators with news entities. Also optimal trading strategies can be generated by using optimal programming.


Impact Factor, News Preprocessing, News Entities, Sentiment Analysis, Trading Strategies.

Full Text:

 |  (PDF views: 225)


  • Schumaker RP, Chen H. Textual analysis of stock market prediction using breaking financial news: The AZFin text system. ACM Transactions on Information Systems (TOIS). 2009 Feb; 27(2):12.
  • Chan WS. Stock price reaction to news and no-news: Drift and reversal after headlines. Journal of Financial Economics. 2003 Nov; 70(2):223–60.
  • Atsalakis GS, Valavanis KP. Surveying stock market forecasting techniques–Part II: Soft computing methods. Expert Systems with Applications. 2009 Apr; 36(3):5932–41.
  • Velumoni D, Rau SS. Cognitive intelligence based expert system for predicting stock markets using prospect theory. Indian Journal of Science and Technology. 2016 Mar 16; 9(10):1–6.
  • Witten IH, Frank E. Data mining: Practical machine learning tools and techniques. Morgan Kaufmann; 2005.
  • Enke D, Thawornwong S. The use of data mining and neural networks for forecasting stock market returns. Expert Systems with Applications. 2005 Nov; 29(4):927–40.
  • Chandar SK, Sumathi M, Sivanandam SN. Prediction of stock market price using hybrid of wavelet transform and artificial neural network. Indian Journal of Science and Technology. 2016 Feb 14; 9(8):1–5.
  • Nikfarjam A, Emadzadeh E, Muthaiyah S. Text mining approaches for stock market prediction. The 2nd International Conference on Computer and Automation Engineering (ICCAE); Malaysia. 2010 Feb 4. p. 256–60.
  • Li X, Deng X, Wang F, Dong K. Empirical analysis: News impact on stock prices based on news density. IEEE International Conference on Data Mining Workshops; China. 2010 Dec. p. 585–92.
  • Li X, Wang C, Dong J, Wang F, Deng X, Zhu S. Improving stock market prediction by integrating both market news and stock prices. International Conference on Database and Expert Systems Applications. Springer Berlin Heidelberg. 2011 Aug; 6861:279–93.
  • Nassirtoussi AK, Aghabozorgi S, Wah TY, Ngo DC. Text mining for market prediction: A systematic review. Expert Systems with Applications. 2014 Nov; 41(16):7653–70.
  • Kanhabua N, Blanco R, Matthews M. Ranking related news predictions. Proceedings of the 34th International ACM SIGIR Conference on Research and Development in Information Retrieval; Spain. 2011 Jul. p. 755–64.
  • Li X, Xie H, Chen L, Wang J, Deng X. News impact on stock price return via sentiment analysis. Knowledge-Based Systems. 2014 Oct; 69:14–23.
  • Daigo K, Tomoharu N. Stock prediction using multiple time series of stock prices and news articles. IEEE Symposium on Computers and Informatics (ISCI); 2012 Mar. p. 11–6.
  • Zhang X, Yin H, Wang C, Wang J, Zhang Y. Forecast the price of chemical products with multivariate data. 2015 International Conference on Behavioral, Economic and Socio-Cultural Computing (BESC); China. 2015. p. 76–82.
  • Shynkevich Y, McGinnity TM, Coleman S, Belatreche A. Stock price prediction based on stock-specific and sub-industry-specific news articles. IEEE International Joint Conference on Neural Networks (IJCNN); US. 2015 Jul. p. 1–8.
  • Lee MS, Hong JY. Impact factors relating to effectiveness of health information in company and public services workers of urban-rural city. Indian Journal of Science and Technology. 2015 Jan 1; 8(S1):9–13.
  • Li X, Huang X, Deng X, Zhu S. Enhancing quantitative intra-day stock return prediction by integrating both market news and stock prices information. Neurocomputing. 2014 Oct; 142:228–38.
  • Nuij W, Milea V, Hogenboom F, Frasincar F, Kaymak U. An automated framework for incorporating news into stock trading strategies. IEEE Transactions on Knowledge and Data Engineering. 2014 Apr; 26(4):823–35.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.