Stock price forecasting has been mostly realized using quantitative information. However, recent studies have demonstrated that sentiment information hidden in corporate annual reports can be successfully used to predict short-run stock price returns. Soft computing methods, like neural networks and support vector regression, have shown promising results in the forecasting of stock price due to...

This paper surveys recent literature in the area of Neural Network, Data Mining, Hidden Markov Model and Neuro-Fuzzy system used to predict the stock market fluctuation. Neural Networks and Neuro-Fuzzy systems are identified to be the leading machine learning techniques in stock market index prediction area. The Traditional techniques are not cover all the possible relation of the stock price f...

STOCK market price behavior has been studied extensively. It is influenced by a myriad of factors, including political and economic events, among others, and is a complex nonlinear time-series problem. Traditionally, stock price forecasting is performed based on technical analysis, which focuses on price action, which is the process of finding patterns in price history. More recently, research ...

Palladium is an element of PGM group that has significant physical properties. This leads to more attention to this metal. Due to vast applications of palladium in industry and its usage in jewelry, its price plays an important role in economic. Therefore, forecasting its price is crucial subject in economic and engineering design. This paper proposes the model GM(1,1) to predict the Palladium ...

Forecasting the prices of metals is important in many aspects of economics. Metal prices are also vital variables in financial models for revenue evaluation, which forms the basis of an effective payment regime using resource policymakers. According to the severe changes of the metal prices in the recent years, the classic estimation methods cannot correctly estimate the volatility. In order to...

The classical cobweb theorem is extended to include production lags and price forecasts. Price forecasting based on a longer period has a stabilizing effect on prices. Longer production lags do not necessarily lead to unstable prices; very long lags lead to cycles of constant amplitude. The classical cobweb requires elasticity of demand to be greater than that of supply; this is not necessarily...

With the characteristics of nonlinearity and randomness, stock prices change with a strong feature of disorder, and its mathematical model is often complex which makes it difficult to accurately determine the price or contain chaos. One single forecast method can only describe the stock price information partially, but fails to reflect the overall picture. In this paper, a method of Radial Basi...

This study analyzes the accuracy of forecasted target prices which are disclosed by leading investment banks within their analysts’ reports on German stocks for the period from 2002 to 2004. We compute a measure for target price forecast accuracy that evaluates the ability of analysts to exactly forecast the ex-ante (unknown) 12-months stock price. Overall, the target price forecasting accuracy...

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a wellknown and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from th...

It is very important to forecast electricity price in a deregulated electricity market for choosing the bidding strategy, and it is the most important signal for other players. It engulfs information for both customers and producers in order to maximize their profit. Thus, choosing the best method of price forecasting is a crucial task to have the most accurate forecast. In this paper the price...