Portfolio Selection with Support Vector RegressionHenrique, Pedro Alexandre University of Brasilia, Brazil

• Machine Learning

• SVM & SVR• Stocks selection

WHY SVM?

• Multiple dimensions

Expand the information from the variables The importance of choosing KernelFrom 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.

• 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:

• 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

• 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

RESULTS

374,40%

192,65%

• Machine per sector

• Other inputs

• Kernel combination

• SVM with risk management tools

RESEARCHERS IN PROGRESS

THANK YOU!

Packages:RobustbasePerformanceAnalyticsGgplot2robustbaseDplyrScalesKernlab

FselectorMlbenchForeachdoParalleldoSNOWrgl

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