Forecasting exchange rate movements is challenging, as they exhibit high volatility, complexity and noise. Most traditional models cannot forecast exchange rates, with significantly higher accuracy, than a random walk model. In this study, a non-linear model called artificial neural network...

The purpose of this paper is to develop and identify the best hybrid model to predict stock index returns. We develop three different hybrid models combining linear ARIMA and non-linear models such as support vector machines (SVM), artificial neural network (ANN) and random forest (RF) models to...

This study attempts to forecast the next day’s returns of two time series in the Hang Seng Index (HSI) and Standard & Poor’s (S&P) 500 indices using Artificial Neural Networks (ANN) with past returns as input variables. Results from ANN are compared with those from the...

The purpose of the study is to examine whether the returns and volatility for Indian exchange rates possess non-linear dependence. Furthermore, an attempt is made through a rolling-window approach to check whether non-linear dependence is time-varying. The study employs approximate entropy...