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...

In this paper, a hybrid model integrating wavelet and least squares support machines (LSSVM) is proposed for crude oil price forecasting. In this model, Haar à trous wavelet transform is first selected to decompose an original time series into several sub-series with different scales. Then the LSSVM is used to predict each sub-series. And the final oil price forecasting is obtained by reconstru...

The purpose of this study is to optimize the stock price forecasting model with meta-innovation method in pharmaceutical companies.In this research, stock portfolio optimization has been done in two separate phases.The first phase is related to forecasting stock futures based on past stock information, which is forecasting the stock price using artificial neural network.The neural network used ...

In a comment to our article entitled ‘‘Forecasting Pharmaceutical Prices for Economic Evaluations When There Is No Market: A Review’’, which was recently published in PharmacoEconomics Open [1], Holko stated that marketspecific aspects could affect efficiency of forecasting price using the price of a close substitute [2]. We thank the author for the comment on our article and agree that there a...

forecasting stock price had been paying attention to many analysts and stockholders. today, this issue more recent years has been do by new methods but new methods, good enough, not have analysis of description and changes effective variables on stock price whereas all this method rely on regression bases. ardl (autoregression distributed lag ) of one equation cumulative regression method obtai...

The purpose of this study is to develop a forecasting model of daily wholesale price with macro and micro variables for a shipment timing support system for stored apples based on the transaction quantity and the wholesale price information announced by Seoul Agricultural & Marine Products Corporation’s online system. This study identified two operational definitions for production volume and a...

Forecasting financial markets is an important issue in finance area and research studies. On one hand, the importance of prediction, and on the other hand, its complexity, have led to huge number of researches which have proposed many forecasting methods in this area. In this study, we propose a hybrid model including Wavelet Transform, ARMA-GARCH and Artificial Neural Network (ANN) for single-...

This project is focused on the design of price forecasting tools for market operators and for market traders, taking into account the distinct purposes, data availability, and time horizons of the distinct users. Empirical price data from the MISO and RTE have been analyzed. A combined model is developed to forecast MISO day-ahead nodal prices. An ARIMA model is constructed to forecast RTE week...

Accurate electricity price forecasting is a formidable challenge for market participants and managers owing to high volatility of the electricity prices. Price forecasting is also the most important management goal for market participants since it forms the basis of maximizing profits. This study investigates the performance of a novel neural network technique called Extreme Learning Machine (E...