One is up class, and the second is down class. Stock prices are classified into two classes. SVM is the most popular method for stock price classification. A support-vector-machine (SVM) -based model was considered for stock price prediction. The output of neurons is computed using nonlinear functions such as sigmoid and rectifier units. ANN was inspired by a biological neural network, where each neuron accepts the input as stock price data and performs the task. The Indian stock price data are considered for the work.Īn artificial-neural-network (ANN) -based model was considered for stock price forecasting. The performance of the hybrid stock prediction model is better than the single prediction model, namely DNN and ANN, with a 5% to 7% improvement in RMSE score. The hybrid stock prediction model results are computed using the mean absolute error (MAE) and RMSE metric. Fourth, the average results of the PRE and DNN prediction model are combined. We have fine-tuned the hyperparameters of DNN, such as the number of layers, learning rate, neurons, and number of epochs in the model. Third, the three-layer DNN is considered for stock prediction. Second, using the PRE technique-computed different rules for stock prediction, we selected the rules with the lowest root mean square error (RMSE) score. We considered moving average technical indicators: moving average 20 days, moving average 50 days, and moving average 200 days. First, stock technical indicators are considered to identify the uptrend in stock prices. To deal with nonlinearity in data, we propose a hybrid stock prediction model using the prediction rule ensembles (PRE) technique and deep neural network (DNN). Accurate prediction of stock price helps investors to reduce the risk in portfolio or investment. The volatility estimation of stock is one of the challenging tasks for traders. Sometimes stock prices react to domestic uncertainty such as reserve bank policy, government policy, inflation, and global market uncertainty. Stock prices are volatile due to different factors that are involved in the stock market, such as geopolitical tension, company earnings, and commodity prices, affecting stock price.
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