Financial Data Mining Talk
•Download as PPT, PDF•
0 likes•1,031 views
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
![Can We Upgrade a Trading System with a Neural Net? Dr Michael Bowles http://www.linkedin.com/in/mikebowles [email_address]](https://image.slidesharecdn.com/financialdataminingtalk-090813134518-phpapp02/85/Financial-Data-Mining-Talk-1-320.jpg)
![Program ,[object Object],[object Object],[object Object],[object Object]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
More Related Content
Viewers also liked
Viewers also liked (20)
Similar to Financial Data Mining Talk
Similar to Financial Data Mining Talk (20)
Recently uploaded
Recently uploaded (20)
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