In this literature review, we investigate machine learning techniques that are applied for
stock market prediction. A focus area in this literature review is the stock markets …

The financial market volatility has been a focus of study for experts over past decades. While
stockbrokers and investors expect reliable projections of future stock indices, it instead …

Many studies have been carried out on stock price trend prediction, but most of them
focused on the public market data and did not utilize the trading behaviors owing to the …

Abstract The Elman neural network (ElmanNN) is well-known for its capability of processing
dynamic information, which has led to successful applications in stock forecasting. In this …

A hybridized method that relies on using the support vector regression (SVR) method with
equilibrium optimizer (EO) is proposed to foresee the closing prices of Egyptian Exchange …

This paper analyzes the factor zoo, which has theoretical and empirical implications for
finance, from a machine learning perspective. More specifically, we discuss feature selection …

The prediction of stock price has always been a main challenge. The time series of stock
price tends to exhibit very strong nonlinear characteristics. In recent years, with the rapid …

Stock market prediction is considered as an important yet challenging aspect of financial
analysis. The difficulty of forecasting arises from volatile and non-linear nature of stock …

ABSTRACT A stock exchange is an entity that provides ''trading''facilities for stock brokers
and traders to trade stocks and other securities. How to invest in stock exchange is one of …

Predicting direction of stock price movement is notably important to provide a better
guidance to assist market participants in making their investment decisions. This study …