This research examines and analyses the use of Neural Network Regression (NNR) models in foreign exchange (FX) forecasting and trading models. The NNR models are benchmarked against traditional forecasting techniques to ascertain their potential added value as a forecasting and quantitative trading tool. In addition to evaluating the various models using traditional forecasting accuracy measure...

Currency trading is an important area for individual investors, government policy decisions, and organization investments. In this study, we propose a hybrid approach referred to as MKL-DE, which combines multiple kernel learning (MKL) with differential evolution (DE) for trading a currency pair. MKL is used to learn a model that predicts changes in the target currency pair, whereas DE is used ...

We study the cluster dynamics of multichannel (multivariate) time series by representing their correlations as time-dependent networks and investigating the evolution of network communities. We employ a node-centric approach that allows us to track the effects of the community evolution on the functional roles of individual nodes without having to track entire communities. As an example, we con...

in this article using autoregressive (ar), autoregressive conditional heteroskedasticity (arch), generalized autoregressive conditional heteroskedasticity (garch) models we assess the weekend effect and also compare the trading patterns of individual and legal investors during 1381-1385 in tehran stock exchange. our findings suggest that weekend effect exists in tehran stock exchanges which are...

in this paper, we will review the foreign exchange market and will try to extract an exchange market pressure and an intervention index for iran by following the weymark (1995) approach to evaluate the central bank of iran’s exchange rate policy during 1368:q1 to 1391:q3. the estimation method employed, is the econometric technique known in the literature as the two-stage least squares (2sls).t...