Stock trend forecasting is a one of the main factors in choosing the best investment, hence prediction and comparison of different firms’ stock trend is one method for improving investment process. Stockholders need information for forecasting firm’s stock trend in order to make decision about firms’ stock trading. In this study stock trend, forecasting performs by data mining algorithm. It sho...

The use of intelligent systems for stock market predictions has been widely established. This paper deals with the application of hybridized soft computing techniques for automated stock market forecasting and trend analysis. We make use of a neural network for one day ahead stock forecasting and a neuro-fuzzy system for analyzing the trend of the predicted stock values. To demonstrate the prop...

Currently, stock price forecasting is carried out using either time series prediction methods or trend classifiers. The trend classifiers are designed to predict the behaviour of stock price’s movement. Recently, soft computing methods, like support vector machines, have shown promising results in the realization of this particular problem. In this paper, we apply several prototype generation c...

To counter strong features of disorder and randomness of stock market fluctuation in China, we introduce a Markov process model for the stock market trend forecasting, which is a useful complement for an existing technical analysis. Meanwhile, we expound on the related properties of Markov process and establish Markov chain mathematical model of the stock market trend forecasting, furthermore, ...

today, stock investment has become an important mean of national finance. apparently, it is significant for investors to estimate the stock price and select the trading chance accurately in advance, which will bring high return to stockholders. in the past, long-term trading processes and many technical analysis methods for stock market were put forward. however, stock market is a nonlinear sys...

study of the changes in the stock price in tehran stock exchange is of great importance. this is because of its application in forecasting the stock price in the stock exchange. the aim of this article is to investigate the forces and mechanisms that cause the dramatic changes in stock price and the formation of chaotic trend. to test whether the chaotic trend in the tehran stock exchange exist...

This paper uses an empirical connection between real stock market indices of Germany and the USA for forecasting corresponding returns. We are starting from the random walk as the traditional forecasting model in stock market applications, extending it by co-integration. Since the cointegrating relation considers information about a systematic link between the stock market indices, containing a...

Most of existing fuzzy forecasting models partition historical training time series into fuzzy time series and build fuzzy-trend logical relationship groups to generate forecasting rules. The determination process of intervals is complex and uncertainty. In this paper, we present a novel fuzzy forecasting model based on high-order fuzzy-fluctuation trends and the fuzzy-fluctuation logical relat...

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...