A Systematic Review on Artificial Neural Networks for Stock Market Prediction

Abstract

Prediction of the stock market price is one of the most problematic tasks in the financial sector because its time series is complicated, chaotic, noisy, volatile dynamic, and non-parametric.  However, investors and professional analysts can use the computation intelligence technique to help them reduce the risk of their investments.  Artificial neural network (ANNs) models have received a lot of attention, and various research works have looked into how these models might be used to predict the stock price based on chronological data.Because the goal is to generate financial market forecasts, the method must be validated using profitability indicators, and performance was evaluated.  Therefore, this systematic review focuses on ANNs models for stock market prediction. The reviews of systematic were based on four essential components: a prediction algorithm, financial analyst technique, performance indicators, and prediction strategy.  Therefore, even though they have been extensively studied, there are still exciting research and development opportunities