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 ...

Since its founding in 2008, Bitcoin (financial code: BTC) has emerged as a digital currency market cap and continues to attract investors policymakers' attention. In recent years, BTC high price volatility, substantial increase 2016, followed by significant decline 2018. Unlike stock markets, is open for 24x7 dan no closing period. It means everyone can trade it any time. However, this flexibil...

This paper presents a forecasting technique of the short-term system marginal price (SMP) using an Artificial Neural Network (ANN). The SMP forecasting is a very important element in an electricity market for the optimal biddings of market participants as well as for market stabilization of regulatory bodies. Input data are organized in two different approaches, time-axis and day-axis approache...

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...

In this paper the problem of estimating forecasts, for the Official Market of future contracts for olive oil in Spain, is addressed. Time series analysis and their applications is an emerging research line in the Intelligent Systems field. Among the reasons for carry out time series analysis and forecasting, the associated increment in the benefits of the implied organizations must be highlight...

Parametric term structure models have been successfully applied to innumerous problems in fixed income markets, including pricing, hedging, managing risk, as well as studying monetary policy implications. On their turn, dynamic term structure models, equipped with stronger economic structure, have been mainly adopted to price derivatives and explain empirical stylized facts. In this paper, we c...

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...

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the y...

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-...