The stock market prices are dynamic, thus remaining a major challenge in forecasting future
stock trends. The Competitive Feedback Particle Swarm Optimization-based Deep …

The stock market prediction has been considered a significant time-series forecasting
research domain. The accurate prediction of stock prices helps the investors to gain more …

In this paper, a two-stage swarm intelligence based hybrid feed-forward neural network
approach is designed for optimal feature selection and joint optimization of trainable …

Deep Learning provides useful insights by analyzing information especially in the field of
finance with advanced computing technology. Although, RNN–LSTM network with the …

The number of stock investors is growing every day, so providing more accurate predictions
for trend and stock value is essential to earning money. Neural networks have been used in …

Stock market is a significant element of economic market. Accurate forecasting of stock
market is very helpful to shareholders because future prediction of a stock value will elevate …

Owing to the dynamic nature of the financial industry, determining accurate stock market
forecasts remains a significant challenge. Traditional forecasting methods often struggle to …

Stock market prediction acts as a challenging area for the investors for obtaining the profits
in the financial markets. A greater number of models used in stock market forecasting is not …

This paper is to design a new hybrid deep learning model for stock market prediction.
Initially, the collected stock market data from the benchmark sources are pre-processed …

Deep artificial neural networks have been popular for time series forecasting literature in
recent years. The recurrent neural networks present more suitable architectures for …