Call for Paper - January 2024 Edition
IJCA solicits original research papers for the January 2024 Edition. Last date of manuscript submission is December 20, 2023. Read More

Short Term Stock Market Prediction by using Hybrid Approach

International Journal of Computer Applications
Foundation of Computer Science (FCS), NY, USA
Year of Publication: 2022
Chetan Gondaliya, Ajay Patel, Satyen Parikh

Chetan Gondaliya, Ajay Patel and Satyen Parikh. Short Term Stock Market Prediction by using Hybrid Approach. International Journal of Computer Applications 183(52):6-9, February 2022. BibTeX

	author = {Chetan Gondaliya and Ajay Patel and Satyen Parikh},
	title = {Short Term Stock Market Prediction by using Hybrid Approach},
	journal = {International Journal of Computer Applications},
	issue_date = {February 2022},
	volume = {183},
	number = {52},
	month = {Feb},
	year = {2022},
	issn = {0975-8887},
	pages = {6-9},
	numpages = {4},
	url = {},
	doi = {10.5120/ijca2022921934},
	publisher = {Foundation of Computer Science (FCS), NY, USA},
	address = {New York, USA}


Nowadays, finance market has become the most prevalent sector in the world. In finance market, the stock market is a main pillar which represent the major economy of the Country. The stock market nature is random which is dependent on the so many factors like fundamental, technical, overseas news, domestic news, Government policies, global demand and supply etc. Therefore, it is necessary consider each factor which are lies under timeline of the forecast. Most of the researcher have just used technical parameters for stock market prediction. It may happen that stock has good technical although it is not giving the good results. On the other side, stock has poor technical but good sentiment, given good result. Nowadays, the most of the peoples are expressing their views on the social media platforms. This news can be taken to process and discover the features which can be used for the stock market predictions. The main aim of this research paper is to develop hybrid model which can be used technical as well as sentiment parameters for stock market prediction in the short-term duration.


  1. Malkiel, B. G. and Fama, E. F.(1970). Efficient capital markets: A review of theory and empirical work. The Journal of Finance, 25(2), 383-417.
  2. Vijh, M., Chandola, D., Tikkiwal, V. A., & Kumar, A. (2020). Stock closing price prediction using machine learning techniques. Procedia Computer Science, 167, 599-606.
  3. Henrique, B. M., Sobreiro, V. A., & Kimura, H. (2018). Stock price prediction using support vector regression on daily and up to the minute prices. The Journal of finance and data science, 4(3), 183-201.
  4. John, J., Kumar, A., Abhishek, A., Dhule⁴, T. A., Roy, A., & Jha, A. (2020). STOCK MARKET PREDICTION USING MACHINE LEARNING.
  5. Kamble, R. A. (2017, June). Short and long term stock trend prediction using decision tree. In 2017 International Conference on Intelligent Computing and Control Systems (ICICCS) (pp. 1371-1375). IEEE.
  6. Reilly. F, and Brown, K. (2012). Investment Analysis and Portfolio Management. 12th Edition. Cengage Learning Publication.
  7. Wellington Garikai, Bonga. (2015). The Need for Efficient Investment: Fundamental Analysis and Technical Analysis. Finance & development. 10.2139/ssrn.2593315.
  8. Shantanu Pacharkar, Pavan Kulkarni, Yash Mishra, Amol Jagadambe, S.G.Shaikh, (2018, March). Predicting Stock Market Investment Using Sentiment Analysis. International Journal of Advanced Research in Computer and Communication Engineering (2278-1021)"
  9. Khatri, S. K., & Srivastava, A. (2016, September). Using sentimental analysis in prediction of stock market investment. In 2016 5th International Conference on Reliability, Infocom Technologies and Optimization (Trends and Future Directions)(ICRITO) (pp. 566-569). IEEE.
  10. Minh, D. L., Sadeghi-Niaraki, A., Huy, H. D., Min, K., & Moon, H. (2018). Deep learning approach for short-term stock trends prediction based on two-stream gated recurrent unit network. IEEE Access, 6, 55392-55404.
  11. Paredes-Valverde, M. A., Colomo-Palacios, R., Salas-Zárate, M. D., & Valencia-García, R. (2017). Sentiment Analysis in Spanish for Improvement of Products and Services: A Deep Learning Approach. Scientific Programming, 2017, 1-6. doi:10.1155/2017/1329281.


ML algorithms, Stock market prediction, Sentiment analysis, Technical analysis, Indian stock market, short term prediction