The nature of statistical learning theory
The nature of statistical learning theory
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
Neural networks in business: techniques and applications for the operations researcher
Computers and Operations Research - Neural networks in business
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Visualization of large category map for internet browsing
Decision Support Systems - Web retrieval and mining
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Dynamic support vector machines for non-stationary time series forecasting
Intelligent Data Analysis
Adaptive mixtures of local experts
Neural Computation
Self organization of a massive document collection
IEEE Transactions on Neural Networks
Integration of genetic fuzzy systems and artificial neural networks for stock price forecasting
Knowledge-Based Systems
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Computers and Electronics in Agriculture
A case study of applying LRFM model in market segmentation of a children's dental clinic
Expert Systems with Applications: An International Journal
An efficient CMAC neural network for stock index forecasting
Expert Systems with Applications: An International Journal
A Framework For State Transitions On The Self-Organizing Map: Some Temporal Financial Applications
International Journal of Intelligent Systems in Accounting and Finance Management
Computers & Mathematics with Applications
Topological pattern discovery and feature extraction for fraudulent financial reporting
Expert Systems with Applications: An International Journal
Hi-index | 12.06 |
Stock price prediction has attracted much attention from both practitioners and researchers. However, most studies in this area ignored the non-stationary nature of stock price series. That is, stock price series do not exhibit identical statistical properties at each point of time. As a result, the relationships between stock price series and their predictors are quite dynamic. It is challenging for any single artificial technique to effectively address this problematic characteristics in stock price series. One potential solution is to hybridize different artificial techniques. Towards this end, this study employs a two-stage architecture for better stock price prediction. Specifically, the self-organizing map (SOM) is first used to decompose the whole input space into regions where data points with similar statistical distributions are grouped together, so as to contain and capture the non-stationary property of financial series. After decomposing heterogeneous data points into several homogenous regions, support vector regression (SVR) is applied to forecast financial indices. The proposed technique is empirically tested using stock price series from seven major financial markets. The results show that the performance of stock price prediction can be significantly enhanced by using the two-stage architecture in comparison with a single SVR model.