Forecasting Systematic Risk by Least Angel Regression, AdaBoost and Kernel Ridge Regression

In according to importance of risk in financial decision and investment is one of issue that helps to investors is existing tools and appropriate models in order to predict systematic risk. Aim of this research was forecasting systematic risk of companies admitted at Tehran stock Exchange by Least Angel Regression (LARS), AdaBoost and Kernel Ridge Regression (KRR) and comparing ability of the algorithms in order to find the best methods of the test. In this study the financial data of (1159 observations) during 2005-2014. We used MATLAB software vision (R2013b). Results indicated that Kernel Ridge Regression (KRR) with 9.65% error (90.35% confidence) in comparison with Least Angel Regression (LARS) with 12.15% error (87.85% confidence) and AdaBoost with 28.91% error (71.09 confidence) has more ability for forecasting systematic risk. Moreover, ability of forecasting systematic risk in Least Angel Regression (LARS) is more than AdaBoost.

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