A real time clustering and SVM based price-volatility prediction for optimal trading strategy

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• 作者：Subhabrata Choudhury ; Subhajyoti Ghosh ; Arnab Bhattacharya ; Kiran Jude Fern ; es ; Manoj Kumar Tiwari
• 关键词：Stock market ; Clustering ; Self-Organizing Maps ; Trading strategy ; Support vector machine
• 刊名：Neurocomputing
• 出版年：5 May, 2014
• 年：2014
• 卷：131
• 期：Complete
• 页码：419-426
• 全文大小：951 K

文摘

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.