Stock Market Analysis using ART-SVR based on Technical Parameters - Lipton, Manoj; Dixit, Sarvottam; Khan, Asif Ullah (International Journal of Computer Sciences and Engineering, 2019)
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.