Open Access Open Access  Restricted Access Subscription Access

Analysis of Stock Market using Data Mining Techniques

Ashmita Phuyal, Aditi Pokharel, Nilima Dahal, Sushil Shrestha


Long term investments are one of the major leading investment strategies in the modern financial market. However, calculating intrinsic value of some company and evaluating shares for long term investment is not easy, since analysts have to visualize a large number of financial indicators and evaluate them in the right manner. The prediction of stock markets is considered as one of the major challenging tasks of financial time series. Due to the presence of non-linear data sets and dynamic nature, there is an increasing demand in analysis of the market and prediction of future stock trends. In this paper we present a data mining and machine learning aided approach to evaluate the equity’s future price over the long term. However, the main objective of this paper is to find the best algorithm for prediction to predict the values of the stock market.

Full Text:



J. Lu, K. Dang & K. Sakakibara (2020). Parameters for Stock Market Prediction., 2020. [Online]. Available: /Parameters-for-Stock-Market-Prediction-N’guyenLu/be9880553b0e423b64b9a2b2e27f07a71d7661e7.[Accessed: 28-Jan-2020]

Prasanna, S., & Ezhilmaran, D. (2013). An analysis on stock market prediction using data mining techniques. International Journal of Computer Science & Engineering Technology (IJCSET), 4(3), 49-51.

Ballings, M., Van den Poel, D., Hespeels, N., & Gryp, R. (2015). Evaluating multiple classifiers for stock price direction prediction. Expert systems with Applications, 42(20), 7046-7056.

Weng, B., Ahmed, M. A., & Megahed, F. M. (2017). Stock market one-day ahead movement prediction using disparate data sources. Expert Systems with Applications, 79, 153-163.

Chen, Y., & Hao, Y. (2017). A feature weighted support vector machine and K-nearest neighbor algorithm for stock market indices prediction. Expert Systems with Applications, 80, 340-355.

Chong, E., Han, C., & Park, F. C. (2017). Deep learning networks for stock market analysis and prediction: Methodology, data representations, and case studies. Expert Systems with Applications, 83, 187-205.

”A Complete Guide to Scatter Plots”, Chartio, 2020. [Online]. Available: scatter-plot/. [Accessed: 14- Mar 2020].

”Linear Regression”,, 2020. [Online]. Available: 98/101/linreg.htm. [Accessed: 26- Feb 2020].

”Support Vector Machine — Introduction to Machine Learning Algorithms”, Medium, 2020. [Online]. Available: vector-machine-introduction-to-machine learning-algorithms-934a444fca47. [Accessed: 23- May- 2020].

U. code), “Understanding Support Vector Machines(SVM) algorithm (along with code)”, Analytics Vidhya, 2020. [Online]. Available: 17/09/understaing-support-vector-machine example-code/. [Accessed: 7- Mar 2020].


  • There are currently no refbacks.