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Closing prices of the financial stock market change daily at the end of each session. These changes happen because of many factors that affect the prices of the stocks. This study attempts to accurately predict closing prices by applying a data mining approach and investigate and identify the most influential factors of Dubai Financial Stock Market prices. The main objective of this study is to help...
Nonparametric Linear Regression and Artificial Neural Network models have been developed based on different perspectives and assumptions. In this paper a survey is made to compare the predictive performances of the nonparametric models of closing prices of Stock Index data, where the data is non normal. Comparative studies with the existing statistical prediction models indicate that the proposed...
Stock market forecasting has attracted a lot of research interests in previous literature, and recent studies have shown that artificial neural networks (ANN) method achieved better performance than traditional statistical ones. ANN approaches have, however, suffered from difficulties with generalization, producing models that can overfit the data. This paper employs a relatively new machine learning...
This paper proposes an intelligent trading system using support vector regression optimized by genetic algorithms (SVR-GA) and multilayer perceptron optimized with GA (MLP-GA). Experimental results show that both approaches outperform conventional trading systems without prediction and a recent fuzzy trading system in terms of final equity and maximum drawdown for Hong Kong Hang Seng stock index.
Stock market predictions comprise challenging applications of modern time series forecasting and are essential to the success of many businesses and financial institutions. In this paper, a novel nonlinear combination model is presented for stock market forecasting, which based on Support Vector Machine (SVM) regression combining the linear regression of traditional statistical model with the nonlinear...
Neural Networks are models of biological neural structure, so the scientist, engineers & mathematicians etc. try to make an intellectual abstraction with the help of neural network which would enable a computer work in a similar fashion in which the human brain works. Here we use a specific type of neural network called ??Holographic Neural Network?? (HNN), for stock price prediction. HNN takes...
We propose a hybrid approach of support vector regression, genetic algorithm, and seasonal moving window to explore seasonality effect for the stock indexes in three developed and one emerging markets using daily prices from 1996 to 2005. First, we utilize genetic algorithm to locate the approximate optimal combination of technical indicators. Then the property of nonlinearity and high dimensionality...
Stock index prediction seems to be a challenging task of the financial time series prediction process especially in emerging markets with their complex and inefficient structures. Multivariate adaptive regression splines (MARS) is a nonlinear and non-parametric regression methodology and has been successfully used in classification tasks. However, there are few applications using MARS in stock index...
In our economic society, future stock price trend is very hot focus that the investors concern about. Challenges still exist in stock price prediction model regarding significant time-effectiveness of prediction, the complexity of methods and selection of feature index variables. In this paper, we present a new approach based on Logistic Regression to predict stock price trend of next month according...
Stock market analysis and prediction has been one of the widely studied and most interesting time series analysis problems till date. Many researchers have employed many different models, some of them are linear statistic based while some non linear regression, rule, ANN, GA and fuzzy logic based. In this paper we have proposed a novel model that tries to predict short term price fluctuation, using...
Stock market forecasting has attracted a lot of research interests in previous literature. Traditionally, the autoregressive moving average (ARMA) model has been one of the most widely used linear models in time series forecasting. However, the ARMA model cannot easily capture the nonlinear patterns. And recent studies have shown that artificial neural networks (ANN) method achieved better performance...
In this paper, we study the theory of generalized regression neural networks, a kind of radial basis network that is often used for function approximation, and apply it for the forecasting of the Shanghai composite index of the Chinese stock market. The raw data consists of 4245 observations of daily closing values of the Shanghai Composite Index spanning the trading dates December 19, 1990 to April...
This study combines wavelet-based feature extractions with kernel partial least square (PLS) regression for international stock index forecasting. Wavelet analysis is utilized as a preprocessing step to decompose and extract most important time scale features from high dimensional input data. Owing to the high dimensionality and heavy multi-collinearity of the input data, a kernel PLS regression model...
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