An Optimal Deep Learning Model With Effective Feature Learning Mechanism For Stock Market Prediction

Abstract

The stock market has considered the active research fields today, and forecasting its behaviour is an enormous necessity. Predicting the stock market is complex, necessitating a thorough examination of data patterns. Correct forecasting outcomes can provide significant insight to investors, lowering investment risk. This paper proposes a novel Weight and Bias Tuned Long Short-term Memory (WBTLSTM) with an efficient feature extraction model, Bilateral ReLU-based Two-Dimensional Convolutional Neural Network (BR2DCNN), for stock price prediction (SPP). First, the stock data was collected from the publicly available dataset. Then the missing values imputation and data normalization is performed on the collected dataset. The preprocessed dataset extracts the most relevant features using BR2DCNN. Finally, the future stock events are predicted using the WBTLSTM. The weights and biases are tuned with the help of the Enhanced Butterfly Optimization Algorithm (EBOA). Experimental findings prove that the proposed one achieves superior outcomes compared to the conventional methods regarding some performance metrics.