The nature of statistical learning theory
The nature of statistical learning theory
MR functional cardiac imaging: segmentation, measurement and WWW based visualisation of 4D data
Future Generation Computer Systems - Special issue on ITIS—an international telemedical information society
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond
Time Series Analysis, Forecasting and Control
Time Series Analysis, Forecasting and Control
Computers and Operations Research - Special issue: Emerging economics
Forecasting stock market movement direction with support vector machine
Computers and Operations Research
Expert Systems with Applications: An International Journal
Expert Systems with Applications: An International Journal
Frequent Patterns Minning of Stock Data Using Hybrid Clustering Association Algorithm
ICIME '09 Proceedings of the 2009 International Conference on Information Management and Engineering
Using self-organizing maps to visualize high-dimensional data
Computers & Geosciences
Comparison of GARCH, Neural Network and Support Vector Machine in Financial Time Series Prediction
PReMI '09 Proceedings of the 3rd International Conference on Pattern Recognition and Machine Intelligence
Clustering Indian stock market data for portfolio management
Expert Systems with Applications: An International Journal
A multiple-kernel support vector regression approach for stock market price forecasting
Expert Systems with Applications: An International Journal
Clustering of the self-organizing map
IEEE Transactions on Neural Networks
Financial volatility trading using recurrent neural networks
IEEE Transactions on Neural Networks
Support vector machine with adaptive parameters in financial time series forecasting
IEEE Transactions on Neural Networks
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Financial return on investments and movement of market indicators are fraught with uncertainties and a highly volatile environment that exists in the global market. Equity markets are heavily affected by market unpredictability and maintaining a healthy diversified portfolio with minimum risk is undoubtedly crucial for any investment made in such assets. Effective price and volatility prediction can highly influence the course of the investment strategy with regard to such a portfolio of equity instruments. In this paper a novel SOM based hybrid clustering technique is integrated with support vector regression for portfolio selection and accurate price and volatility predictions which becomes the basis for the particular trading strategy adopted for the portfolio. The research considers the top 102 stocks of the NSE stock market (India) to identify set of best portfolios that an investor can maintain for risk reduction and high profitability. Short term stock trading strategy and performance indicators are developed to assess the validity of the predictions with regard to actual scenarios.