Farshchian and Jahan (2015) used Hidden Markov Model (HMM) to predict the changes in Tehran Stock Exchanges. In this technique, the normal factors that affect stock prices are used along with abnormal conditions such as political effect and other factors. All the factors in the data are trained with the help of Baum-Welch Algorithm after that the progressive prediction was achieved by HMM method. The overall accuracy, specificity and sensitivity will be increased up to 2% compared to other previous systems. The periodic accuracy is found to be non-linear compared to original stock value data’s (Saadat & Rahmani, 2016).
Budhani and Budhani (2014) proposed stock prediction method using Artificial Neural Network (ANN) algorithm. ANN is capable of progressive learning so the prediction output will be more efficient than the other soft computing. The main difference between ANN and other soft computing algorithm were the nonlinear behaviour of input dataset with no assumption results. In this technique, the feed forward neural network with back propagation technique was used but the performance of ANN algorithm is not reliable, so it can’t be applied to all the types of input datasets (Budhani, Jha, & Budhani, 2014).