Design and development of hybrid model for efficient prediction and classification of market stock prices

Abstract

Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. The volatility estimation of stock is one of the challenging tasks for traders. Accurate prediction of stock price help investors to reduce the risk in portfolio or investment. Stock prices are nonlinear. To deal with nonlinearity in data, we proposed a Hybrid stock prediction model using the Prediction Rule Ensembles(PRE) technique and Deep Neural Network(DNN). First, stock technical indicators are considered to identify the uptrend in stock prices. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. Second, using the PRE technique computed different rules for stock prediction, we selected the rules with the lowest Root Mean Square Error (RMSE) score. Third, the three-layer DNN is considered for stock prediction. We have fine-tuned the hyperparameters of the DNN method, such as the number of layers, learning rate, neurons, and number of epochs in the model. Fourth the average results of the PRE and DNN prediction model are combined. The Hybrid stock prediction model results are computed using the Mean Absolute Error (MAE) and RMSE metric. The performance of the Hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 10% improvement in RMSE score. The Indian stock price data are considered for the work.