This paper surveys recent literature in the area of stock market forecasting using advanced engineering based methods like Neural Network, fractal theory, Data Mining, Hidden Markov Model and Neuro-Fuzzy system. Neural Networks and Neuro-Fuzzy systems are emerging as an effective tool to be used in the forecasting of stock market especially in machine learning techniques. Due to chaotic behavio...

Original scientific paper As the stock market volatility is highly nonlinear, coupling and time varying, it is difficult to predict by the traditional forecasting methods. For explaining the existing problems of the current volatility forecasting method, we use the model based on the weighted least squares support vector regression (WLSSVR) method to predict the stock index volatility in this p...

After reviewing the vast body of literature on using FTS in stock market forecasting, certain deficiencies are distinguished in the hybridization of findings. In addition, the lack of constructive systematic framework, which can be helpful to indicate direction of growth in entire FTS forecasting systems, is outstanding. In this study, we propose a multilayer model for stock market forecasting ...

The effective role of capital in every country flows through giving guidelines for capital and resources, generalizing companies and sharing development projects with public, and also adding accredited companies stock market requires appropriate decision making for shareholders and investors who are willing to buy shares based on price mechanism. Forecasting stock price has always been a challe...

A convolutional neural network architecture is presented for financial time series forecasting. Historical data of daily open, close, high and low prices as well as traded volume from 20 stocks is used to forecast the trend of a single stock for the next day. The out-of-sample performance averages at 50.9%, which is highly significant for financial data. Very high accuracies for specific stocks...

After more than 30 years of development, China’s stock exchange market has already had a considerable scale. Modeling and forecasting prices is always problem. Based on the data three stocks given in title, this paper analyzes characteristics development trend each stock. foothold chaos theory [1] studies nonlinear dynamic predictability market, finally tests it.

Classification and patterns extraction from customer data is very important factors for business support and decision making. Identification of newly emerging trends is needed in business process. Sales patterns from inventory data indicate market trends and can be used in forecasting which has great potential for decision making, strategic planning and market competition. The system consists o...

Modeling and analysis of future prices has been hot topic for economic analysts in recent years. Traditionally, the complex movements in the prices are usually taken as random or stochastic process. However, they may be produced by a deterministic nonlinear process. Accuracy and efficiency of economic models in the short period forecasting is strategic and crucial for business world. Nonlinear ...

The so-ealled "golden-cross" and "dead-cross" are said to be useful signals to forecast market trends. In this paper, we focus on the Japanese stock market where gold-crosses and dead-crosses are empirically considered as useful investment signals. First, we examined the usefulness of these signals by using historical Japanese stock price data. The results confirmed that these crosses were usef...

This paper adopts the concept of dynamic feedback systems to model the behavior of financial markets, or more specifically, the stock market from a dynamic system point of view. Based on a feedback adaptation scheme, the authors model the movement of a stock market index within a framework that is composed of an internal dynamic model and an adaptive filter. The output-error model is adopted as...