Securities Investment Risk Management Based On Support Vector Machine

Risk is the basic factor that affects all financial activities. With the development of China's financial markets, not only credit risk, but also market risk and other risks will gradually increase. Thus, financial risk management methods are very important to the present and future innovation and financial institutions investment decisions.Support Vector Machine (Support Vector Machine, SVM) is based on statistical learning theory, which is a new way for machine learning. Because of its sound theoretical foundation, excellent learning performance and projected performance, SVM has been widely used. In this paper, risk management method based on support vector machine is investigated. The main work and the results achieved are:Systematically review of stock market investment risk measurement methods; introduced support vector machine theory and methods based on the principle of structural risk minimization; introduced SVM application in the economics. SVM-based prediction method of securities price is studied. Empirical studies for the Shanghai Composite Index have shown that SVM model can model the stock market volatility very well. Empirical studies for the Huaxia foundation have shown that SVM-based prediction of chaotic time series can capture the market trends and identify market fluctuations well and it is an excellent risk prediction and management tools. According to the defects of the traditional VaR computation methods in the statistics framework, a new VaR model based on weighted support vector machine (W-SVM) was investigated. The Shanghai composite index from the year 2001 to 2009 was modeled and the simulation results indicated that the new VAR method based W-SVM is better than traditional methods. Even for small sample, abnormal fluctuations and heavy tails in nonlinear market, W-SVM model can obtain good performance at different confidence intervals. And it is suitable for different investor.