Financial Data Mining Talk

Can We Upgrade a Trading System with a Neural Net? Dr Michael Bowles http://www.linkedin.com/in/mikebowles [email_address]

Program
Produce a Simple Trading System
Try 2 neural nets for culling trades
One works the other doesn't
Test whether model selection techniques would have chosen correctly.

Simple Trading System
Buy when price crosses above last high
Sell when price crosses below last low
*Charts are Drawn with NeuroShell Trading Software Package

Simple System Performance
Optimize over 6 weeks. Trade for 2 weeks

Q1 Performance *In this table a “Period” is 2 weeks Per 2 Weeks Avg Rtn: 2.50% Rtn s.d.: 6.07% Yearly Sharpe's: 2.06

Can Statistical Learning Discard Some Losing Trades?
Give a NN some traditional indicators
Train it to reject losing trades
1 st try:
2 input (traditional momentum indicators)‏
1 to 3 hidden neurons
Compare >0 or <0 for accept reject

How does it work?

Q1 Performance (Basically the Same)‏ Per 2 Weeks Avg Rtn: 1.77% Rtn s.d.: 4.25% Yearly Sharpe's: 2.08 *Period = 2 weeks

Try Something Else
4 inputs (traditional momentum indicators)‏
2 hidden neurons
Selectable tanh or Gaussian activation functions for hiddens
GA for weights and activation function selection

Q1 Performance (Good News / Bad News)‏ Per 2 Weeks Avg Rtn: 4.09% Rtn s.d.: 4.64% Yearly Sharpe's: 4.40 *Period = 2 weeks

Predict the Better Performing Model?
How to judge models BEFORE looking at the OOS data
Have a look at linear regression

2-Input Model Results (Linear Terms)‏ Estimate Std.Error t value Pr(>|t|)‏ (Intercept) -0.78735 1.54228 -0.511 0.610 x1 -0.14016 1.86259 -0.075 0.940 x2 0.03526 0.10061 0.350 0.726 Multiple R-squared: 0.0004033 AIC = 2910.813

2-Input Model Results (Polynomial Terms)‏ Estimate Std. Error t value Pr(>|t|)‏ (Intercept) -0.787343 1.549424 -0.508 0.612 x1 -0.160997 2.283883 -0.070 0.944 x2 0.035802 0.103914 0.345 0.731 x3 0.132438 0.850780 0.156 0.876 x4 0.042197 0.157572 0.268 0.789 x5 0.001034 0.004287 0.241 0.810 Multiple R-squared: 0.001049 AIC = 2916.614

4-Input Model Results (Linear Terms)‏ Estimate StdError t value Pr(>|t|) (Intercept) -0.7929 1.5289 -0.519 0.60438 x1BB.MACD -3.1617 2.4588 -1.286 0.19948 x1BB.Price 0.7592 0.7233 1.050 0.29472 x1TT.MACD 6.8499 2.5562 2.680 0.00777 ** x1TT.Price -0.9122 0.5645 -1.616 0.10712 Multiple R-squared: 0.02418 AIC = 2907.399

4-Input Model Results (Polynomial Terms)‏ Estimate StdError t value Pr(>|t|) (Intercept) -0.7896 1.5283 -0.517 0.60581 x1BB.MACD -2.8592 2.8054 -1.019 0.30896 x1BB.Price 0.4084 0.7980 0.512 0.60920 x1TT.MACD 6.8554 2.8108 2.439 0.01532 * x1TT.Price -1.0843 0.7179 -1.511 0.13199 x2X1x1 -3.3184 2.1810 -1.521 0.12921 x2X1x2 1.5070 1.1713 1.287 0.19927 x2X1x3 11.1495 4.1630 2.678 0.00782 ** x2X1x4 -2.0146 0.8542 -2.358 0.01901 * x2X2x2 -0.1230 0.1511 -0.814 0.41623 x2X2x3 -2.6644 1.1330 -2.352 0.01935 * x2X2x4 0.4220 0.2220 1.901 0.05825 . x2X3x3 -7.5425 2.6674 -2.828 0.00501 ** x2X3x4 2.9110 1.0361 2.810 0.00529 ** x2X4x4 -0.2644 0.1136 -2.327 0.02066 * Multiple R-squared: 0.05708 AIC = 2916.835

Conclusion
Demonstrated NN Successfully Upgrades Trade System Statistics
Demonstrated Criteria for Model / Input Selection

Some References Dunis C., Laws J., Naim P, Applied Quantitative methods for trading and investment John Wiley & Sons, 2003 Ch 4. Forecasting and Trading Currency Volatility:An Application of Recurrent Neural Regression Ch5. Implementing Neural Networks, Classification Trees, and Rule Induction Classification Techniques: An Application to Credit Risk Franses P.H., van Dijk D, Non-linear time series models in empirical finance , Cambridge University Press, 2000 Ch 5. Artificial neural networks for returns

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Financial Data Mining Talk

by Mike Bowles on Aug 13, 2009

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