In this research work, a soft computing or machine learning approach is used to design an algorithm which is a basic hybridized framework of the feature reduced adaptive resonance theory (ART) and support vector regression (SVR) to effectively predict stock market price as well as behaviour from the historical dataset.Ten different technical indicators are extracted and reduced using particle swarm optimization (PSO). Simulation results on different well-known stock market price like Adani Powers, BHEL, Reliance Industries, SBI and Infosys, stock exchange price is finally presented to test the performance of the established model. With the proposed model, it can achieve a better prediction capability to stocks. The proposed algorithm is compared with ART algorithm and analyzed that proposed model predicts better stock position behavior.

Machine learning, ART, SVR, PSO, Stock Market Indices, Technical indicators, Stock Prediction

[1] Xi, L., Muzhou, H., Lee, M. H., Li, J., Wei, D., Hai, H., & Wu, Y., “A new constructive neural network method for noise processing and its application on stock market prediction”, Applied Soft Computing, vol. 15, pp. 57–66, 2014.
[2] Yu, H., Chen, R., & Zhang, G., “A svm stock selection model within pca”, Procedia computer science, vol. 31, pp. 406–412, 2014.
[3] Chen MY. “A high-order fuzzy time series forecasting model for internet stock trading”, Future GenerComput System, vol. 37, pp. 461-467, 2014.
[4] Cervell´o-Royo, R., Guijarro, F., Michniuk, K., “Stock market trading rule based on pattern recognition and technical analysis: Forecasting the djia index with intraday data”, Expert systems with Applications, vol. 042, pp. 5963–5975, 2015.
[5] Hu, Y., Feng, B., Zhang, X., Ngai, E., & Liu, M., “Stock trading rule discovery with an evolutionary trend following model”, Expert Systems with Applications, vol. 42, pp. 212–222, 2015.
[6] Rahman, H. F., Sarker, R., &Essam, D., “A genetic algorithm for permutation flow shop scheduling under make to stock production system”, Computers & Industrial Engineering, vol. 90, pp. 12–24, 2015.
[7] Nayak, R. K., Mishra, D., &Rath, A. K., “A na¨ıvesvm-knn based stock market trend reversal analysis for indian benchmark price”, Applied Soft Computing, vol 35, pp. 670–680, 2015.
[8] Chiang, W.-C., Enke, D., Wu, T., & Wang, R., “An adaptive stock index trading decision support system”, Expert Systems with Applications, vol 59, pp. 195–207, 2016.
[9] Kim, Y., &Enke, D., “Developing a rule change trading system for the futures market using rough set analysis”, Expert Systems with Applications, vol 59, pp. 165–173, 2016.
[10] Podsiadlo, M., &Rybinski, H, “Financial time series forecasting using rough sets with time-weighted rule voting”, Expert Systems with Applications, vol 66, pp. 219–233, 2016.
[11] Zhong, X., &Enke, D., “Forecasting daily stock market return using dimensionality reduction”, Expert Systems with Applications, vol 67, pp. 126–139, 2017.
[12] Pankaj K. Bharne, Sameer S. Prabhune, “Survey on combined swarm intelligence and ANN for optimized daily stock market price”, International Conference on Soft Computing and its Engineering Applications,IEEE, 2017.
[13] ZhixiLi, Vincent Tam, “A comparative study of a recurrent neural network and support vector machine for predicting price movements of stocks of different volatilites”, IEEE Symposium Series on Computational Intelligence, 2017.
[14] Hasan S.S., Rahman R., Mannan N., Khan H., Moni J.N., Rahman R.M, “Improved Stock Price Prediction by Integrating Data Mining Algorithms and Technical Indicators: A Case Study on Dhaka Stock Exchange”, International Conference on Computational Collective Intelligence, pp 288-297, 2017.
[15] Lei Lei, “Wavelet Neural Network Prediction Method of Stock Price Trend Based on Rough Set Attribute Reduction”, Applied Soft Computing, Vol. 62, pp. 923-932, 2018.
[16] Manas Ranjan Senapati, Sumanjit Das, Sarojananda Mishra, “A Novel Model for Stock Price Prediction Using Hybrid Neural Network”, Journal of The Institution of Engineers, Vol. 99, Issue 6, pp 555–563, 2018.
[17] SotiriosP.ChatzisaVassilisSiakoulis, “Forecasting stock market crisis events using deep and statistical machine learning techniques”, Expert Systems with Applications, Volume 112, pp. 353-371, 2018.
[18] Tae Kyun, Leeab Joon, Hyung ChobDeuk, Sin Kwonb, “Global stock market investment strategies based on financial network indicators using machine learning techniques”, Expert Systems with Applications, vol. 117, pp. 228-242, 2018.
[19] Bruno Mirand, HenriqueVinicius, Amorim Sobreiro, Herbert Kimur, “Stock price prediction using support vector regression on daily and up to the minute prices”, The Journal of Finance and Data Science, Volume 4, Issue 3, pp. 183-201, 2018.
[20] Feng Zhou, Hao-min Zhou, Zhihua Yang, Lihua Yang, “EMD2FNN: A strategy combining empirical mode decomposition and factorization machine based neural network for stock market trend prediction”, Expert Systems with Applications, Vol. 115, pp. 136-151,2018.
[21] M.Janik, P.Bossew, O.Kurihara, “Machine learning methods as a tool to analyse incomplete or irregularly sampled radon time series data”, Science of The Total Environment Vol. 630, pp. 1155-1167, 2018.
[22] Salim Lahmiri, “Minute-ahead stock price forecasting based on singular spectrum analysis and support vector regression”, Applied Mathematics and Computation, Vol. 320, pp. 444-451, 2018.
[23] R.S. Walse, G.D. Kurundkar, P. U. Bhalchandra, "A Review: Design and Development of Novel Techniques for Clustering and Classification of Data", International Journal of Scientific Research in Computer Science and Engineering, Vol.06, Issue.01, pp.19-22, 2018.
[24] A. JenitaJebamalar, "Efficiency of Data Mining Algorithms Used In Agnostic Data Analytics Insight Tools", International Journal of Scientific Research in Network Security and Communication, Vol.6, Issue.6, pp.14-18, 2018.