forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the recurrent …, recurrent artificial neural network based ARMA model and feed-forward SVR based ARMA model) by using two forecasting accuracy … evaluation metrics (NSME and sign) and robust Diebold–Mariano test. The results reveal that for one-step-ahead forecasting, the …

topological stock market changes as well as the incorporation of these topological changes into forecasting realized volatility …

explain risk premium. Investigating whether these factors are useful in forecasting stock returns remains active research in …

innovative approach towards forecasting based on Neural Nets and high frequency stock market data to predict the next day's stock …

Recurrent Support Vector Regression for a Nonlinear ARMA Model with Applications to Forecasting Financial Returns … to forecast nonlinear ARMA model based simulated data and real data of financial returns. The forecasting ability of the … focus on fit in-sample, but SVR considers both fit and forecast out-of-sample which endows SVR with an excellent forecasting …

Forecasting the stock returns in the emerging markets is challenging due to their peculiar characteristics. These … markets exhibit linear as well as nonlinear features and Conventional forecasting methods partially succeed in dealing with … which caters both the linear and nonlinear markets. This paper investigates the forecasting ability of ANN by using Fama and …

possible to be retrieved with the traditional econometric modelling. Furthermore we examine the forecasting performance of both …