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Portfolio Selection with Support Vector Regression Henrique, Pedro Alexandre University of Brasilia, Brazil.

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Presentation on theme: "Portfolio Selection with Support Vector Regression Henrique, Pedro Alexandre University of Brasilia, Brazil."— Presentation transcript:

1 Portfolio Selection with Support Vector Regression Henrique, Pedro Alexandre University of Brasilia, Brazil

2 Machine Learning SVM & SVR Stocks selection WHY SVM? Multiple dimensions Expand the information from the variables  The importance of choosing Kernel From Dr.Sead Sayad web site -Support Vector Machine - Regression (SVR) Chih-Chung Chang and Chih-Jen Lin, LIBSVM : a library for support vector machines, 2001.

3 SVR – Support Vector Regression APPLICATION Test different 15 Kernels for portfolio selection to beat the market The dual function: Kernel ( Multi dimensional mapping) Predict function. Gaussian Radial Basis Kernel:

4 Fundamentalist analysis Feature Selection From 127 down to 24 features S&P 100 – from 06/30/2014 Fundamental data from 06/29/1990 to 06/30/2014. Training 52,5% Validation 22,5% Test 25% Cross Validation Random Selection WORKFLOW

5 Forecasting the quarterly return of the stocks for the Portfolio Selections. 15 portfolios - weighted by the forecast return Benchmark for the portfolios: Equal weighted portfolios with the 100 stocks. STRATEGY

6 RESULTS

7 374,40% 192,65%

8

9 Machine per sector Other inputs Kernel combination SVM with risk management tools RESEARCHERS IN PROGRESS

10 THANK YOU! Packages: Robustbase PerformanceAnalytics Ggplot2 robustbase Dplyr Scales Kernlab Fselector Mlbench Foreach doParallel doSNOW rgl Fan, A., & Palaniswami, M. (2001). Stock selection using support vector machines. Paper presented at the Neural Networks, 2001. Proceedings. IJCNN'01. International Joint Conference on. Marcelino, S., Henrique, P. A., & Albuquerque, P. H. M. (2015). Portfolio selection with support vector machines in low economic perspectives in emerging markets. Economic Computation & Economic Cybernetics Studies & Research, 49(4). Huerta, Ramon, Fernando Corbacho, and Charles Elkan. "Nonlinear support vector machines can systematically identify stocks with high and low future returns." Algorithmic Finance 2.1 (2013): 45-58. Emir, S., Dinçer, H., & Timor, M. (2012). A Stock Selection Model Based on Fundamental and Technical Analysis Variables by Using Artificial Neural Networks and Support Vector Machines. Review of Economics & Finance, 106-122. Pedro Alexandre M.B. Henrique. pedroalexandre.df@gmail.com

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