The use of price–earnings ratios and dividend-price ratios as forecasting variables for the stock market is examined using aggregate annual US data 1871 to 2000 and aggregate quarterly data for twelve countries since 1970. Various simple efficient-markets models of financial markets imply that these ratios should be useful in forecasting future dividend growth, future earnings growth, or future...

Electricity price forecasting has become an integral part of power system operation and control. In this paper, a wavelet transform (WT) based neural network (NN) model to forecast price profile in a deregulated electricity market has been presented. The historical price data has been decomposed into wavelet domain constitutive sub series using WT and then combined with the other time domain va...

Taiwan has implemented a floating oil-price mechanism since 2007, which is aimed at connecting the domestic oil price with the international oil price. Given that the consumers’ behavior may change once the price truly responds to its cost and the air quality may consequently be improved, this study investigates the effect of oil price on the concentration of 7 air pollutants, taking the adopti...

Oil price forecasting has received a great deal of attention from practitioners and researchers alike, but it remains difficult topic because its dependency on variety factors, including the economic cycle, international relations, geopolitics. Forecasting oil is gratifying task. Motivated by this issue, we present robust model for accurate crude using ARIMA Prophet models based machine learnin...

This paper aims at providing an in-depth analysis of forecasting ability different GARCH (Generalized Autoregressive Conditional Heteroskedasticity) models and finding the best model for VaR estimation crude oil. Analysis performance is done using Kupiecs POF test, Christoffersens test Backtesting Loss Function. Crude oil one most important fuel sources has contributed to over a third world’s e...

This paper presents a hybrid wavelet support vector machines (WSVM) model that combines both wavelet technique and the SVM model for crude oil forecasting. Based on the purpose, the main time series was decomposed to some multi-frequently time series by wavelet theory and these time series were imposed as input data to the SVM for forecasting of crude oil series. To assess the effectiveness of ...