Financial Data Mining Talk
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I presented these slides at a meeting of ACM data mining group. I discuss using data mining to improve performance of an existing trading system. The presentation was video taped. You can see the video at: http://fora.tv/2009/05/13/Michael_Bowles_Neural_Nets_and_Rule-Based_Trading_Systems if you have any questions or comments contact me: mike@mbowles.com or http://www.linkedin.com/in/mikebowles
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Financial Data Mining Talk
- 1. Can We Upgrade a Trading System with a Neural Net? Dr Michael Bowles http://www.linkedin.com/in/mikebowles [email_address]
- 5. 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
- 7. How does it work?
- 8. 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
- 10. 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
- 12. 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
- 13. 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
- 14. 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
- 15. 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
- 17. 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