Due to the inherent non-linearity and non-stationary characteristics of financial stock market price time series, conventional modeling techniques such as the Box–Jenkins autoregressive integrated moving average (ARIMA) are not adequate for stock market price forecasting. In this paper, a forecasting model based on chaotic mapping, firefly algorithm, and support vector regression (SVR) is propo...

Stock prices have the characteristics of nonlinearity, randomicity and uncertainty, so It is difficult to accurately depict the change rules of stock prices using traditional linear forecasting methods, which lead to low stock price prediction accuracy. In order to improve the stock price prediction precision , this paper proposed a stock price predicting model using SVM optimized by particle s...

Since the establishment of the Shanghai Stock Exchange (SHSE) in 1990 and the Shenzhen Stock Exchange (SZSE) in 1991, China’s stock markets have expanded rapidly. Although this rapid growth has attracted considerable academic interest, few studies have examined the ability of conventional financial models to predict the share price movements of Chinese stock. This gap in the literature is signi...

this paper investigates the forecasting gold coin futures contract price in iran mercantile exchange. this research has presented a hybrid model based on genetic fuzzy systems (gfs) and artificial neural network (ann) to forecast the gold futures contract, at first, we use stepwise regression analysis (sra) to determine factors which have most influence on stock prices. at the next stage we div...

This paper presents an integration prediction method which is called a hybrid forecasting system based on multiple scales. In this method, the original data are decomposed into multiple layers by the wavelet transform and the multiple layers are divided into low-frequency, intermediate-frequency and high-frequency signal layers. Then autoregressive moving average models, Kalman filters and Back...

One of the most important duties of financial economy is modeling and forecasting the volatilities of price of risky assets. From analysts and policy makers’ view, price volatility is a key variable contributing to perception of market volatilities. Therefore, analysts need to have an appropriate of forecast of price volatility as a necessary input to perform duties such as risk management, por...

Price forecasting is an integral part of economic decision making. Forecasts may be used in numerous ways; specifically, individuals may use forecasts to try to earn income from speculative activities, to determine optimal government policies or to make business decisions. The importance of this topic is caused by instability in the world economy and stock markets; there is a growing interest i...

Prediction of market prices is an important and well-researched problem. While traditional techniques have yielded good results, rooms for improvement still exists, especially in the ability to explain sudden changes in behavior, as a response to shocks. Nonlinear systems have been successfully used to describe phase transitions in deterministic chaotic systems, so the combination of the expres...