Abstract
The forecast of the stock price attempts to assess the potential movement of the financial exchange's stock value. The exact estimation of the movement of share price would contribute more to investors' profit. This paper introduces a new stock market prediction model that includes three major phases: feature extraction, optimal feature selection, and prediction. Initially, statistical features like mean, standard deviation, variance, skewness, and kurtosis is extracted from the collected stock market data. Further, the indexed data collected are also computed concerning standard indicators like Average True Range (ATR), Exponential Moving Average (EMA), Relative Strength Index (RSI), and Rate of Change (ROC). To acquire best-predicted results, it is more crucial to select the most relevant features. Such that, the optimal features are selected from the extracted features (technical indicators based features, statistical features) by a new hybrid model referred to Red Deer Adopted Wolf Algorithm (RDAWA). Further, the selected features are subjected to the ensemble technique for predicting the stock movement. The ensemble technique involves the classifiers like Support Vector Machine (SVM), Random Forest1 (RF1), Random Forest2 (RF2), and optimized Neural Network (NN), respectively. The final predicted results are acquired from the Optimized Neural Network (NN). To make the precise prediction, the training of NN is carried out by the proposed RDAWA via fine-tuning the optimal weight. Finally, the performance of the proposed work is compared over other conventional models with respect to certain measures.