This study presents a neural network & web-based decision support system (DSS) for foreign exchange (forex) forecasting and trading decision, which is adaptable to the needs of financial organizations and individual investors. In this study, we integrate the back-propagation neural network (BPNN)based forex rolling forecasting system to accurately predict the change in direction of daily exchan...

Neural network forecasting models have been widely used in the analyses of financial time series during the last decade. This paper attempts to fill this gap in the literature by examining a variety of univariate and multivariate, linear, nonlinear Economics empirical modes and neural network. In this paper, we construct an M-estimator based RBF (MRBF) neural network with growing and pruning te...

Forecasting in the financial domain is undoubtedly a challenging undertaking in data mining. While the majority of previous studies in this field utilize historical market data to predict future stock returns, we explore whether there is benefit in augmenting the prediction model with supplementary domain knowledge obtained from financial news reports. To this end, we empirically evaluate how t...

Financial investors often face an urgent need to predict the future. Accurate forecasting may allow investors to be aware of changes in financial markets in the future, so that they can reduce the risk of investment. In this paper, we present an intelligent computing paradigm, called the Complex Neuro-Fuzzy System (CNFS), applied to the problem of financial time series forecasting. The CNFS is ...

A two-sided Weibull is developed to model the conditional financial return distribution, for the purpose of forecasting Value at Risk (VaR) and conditional VaR. A range of conditional return distributions are combined with four volatility specifications to forecast tail risk in four international markets, two exchange rates and one individual asset series, over a four year forecast period that ...

Practices used to address economic forecasting problems have undergone substantial changes over recent years. We review how such changes have influenced the ways in which a range of forecasting questions are being addressed. We also discuss the promises and challenges arising from access to big data. Finally, we review empirical evidence and experience accumulated from the use of forecasting me...

This study proposes design concepts for a comprehensive home financial learning environment that individual investors can use as a reference in establishing web-based learning and investment platforms. This study also introduces a hybrid approach that demonstrates a data mining function of the financial learning environment. Known as Fuzzy BPN, this approach is comprised of backpropagation neur...

Accurate models for electric power load forecasting are essential to the operation and planning of a power utility company. Load forecasting helps electric utility to make important decisions on trading of power, load switching, and infrastructure development. Load forecasts are extremely important for power utilizes ISOs, financial institutions, and other stakeholder of power sector. Short ter...