A Business Intelligence Expert System for Predicting Market Price in Stock Trading using Data Analysis: Deep Learning Model With Feature Selection Mechanism

Abstract

Because of the availability of data and reasonable processing capability, business intelligence methods are rapidly being used in finance, such as managing assets, trading using algorithms, credit financing, and blockchain-based financing. Machine learning (ML) algorithms use enormous amounts of data to automatically understand and enhance predictability and performance via knowledge and data without being programmed by someone. Due to the stock data’s dynamic, high-noise, non-parametric, non-linear, and chaotic qualities, the stock market prediction has been a challenge and has received much interest from scholars over the last decade. Some studies seek a method for accurately predicting stock prices; however, due to the high correlation between stock prices, stock market analysis is more complex. So, this paper proposes an improved stock price prediction (SPP) model using a novel optimal parameter tuned with cross entropy included bidirectional long short-term memory (OPCBLSTM) with efficient feature extraction and selection schemes. It starts with missing values imputation, and data standardization on the collected dataset. From the preprocessed dataset, the features are extracted using modified rectifier linear unit activation based residual network (MRResNet50). Then the optimal features are selected using the improved whale optimization algorithm (IWOA). Finally, the SPP is done using the OPCBLSTM. The experimental results proved that the proposed method achieves more high-level outcomes than the traditional methods.