HM-SMF: An Efficient Strategy Optimization using a Hybrid Machine Learning Model for Stock Market Prediction

Abstract

Stock market forecasting is a significant task, and investing in the stock marketplace is a significant part of monetary research due to its high risk. Therefore, accurate forecasting of stock market analysis is still a challenge. Due to stable and volatile data, stock market forecasting remains a major challenge for investors. Recent machine learning (ML) models have been able to reduce the risk of stock market forecasting. However, diversity remains a key challenge in developing better erudition models and extracts more intellectually priceless qualities to auxiliary advanced predictability. In this paper, we propose an efficient strategy optimization using a hybrid ML model for stock market prediction (HM-SMP). The first contribution of the proposed HM-SMP model is to introduce chaos-enhanced firefly bowerbird optimization (CEFBO) algorithm for optimal feature selection among multiple features which reduce the data dimensionality. Second, we develop a hybrid multi-objective capuchin with a recurrent neural network (HC-RNN) for the prediction of the stock market which enhances the prediction accuracy. We use supervised RNN to predict the closing price. Finally, to estimate the presence of the proposed HM-SMP model through the benchmark, stock market datasets and the performance can be compared with the existing state-of-the-art models in terms of accuracy, precision, recall, and [Formula: see text]-measure.