1. The GDB Cup: Applying “Real World” Financial Data Mining in an Academic Setting Gary D. Boetticher University of Houston - Clear Lake Houston, Texas, USA

2. What is the GDB Cup? Modeled after the KDD Cup Start with $100,000 + Financial Data + Data Mining Techniques = Make As Much Money as Possible

3. Motivation Availability of Data Gain Experience with DM Process Synthesize ML + Domain Knowledge Pragmatic implications

4. Availability of Data Different Time Series Perspectives –1 minute to monthly Different Financial Instruments –Stocks, Futures, Options, Mutual Funds Large Sample Size –400 - 700 Stocks (Daily, 2.5 Years) –EMini Future (5 Minute, 2 Years) Inexpensive or Free Sources –www.anfutures.com –www.ashkon.com –Screen Scraping (finance.yahoo.com)

5. DM Process: Data Cleansing Low = 0 Volume = 0 Missing Data (e.g. no Open) Missing Time Periods

6. Build Models (Synthesize ML & Domain Knowledge) www.equis.com/Education/TAAZ Tech. Analysis Moving Averages, RSI, MACD, Stochastics, PNF, etc. Machine Learners Supervised NN, GP, SVM, Neuro Fuzzy, SOM, ILP, etc.

7. Validating Models Statistical Valid.  Financial Valid. Ignore Market Conditions (Buy & Hold) Start Date Value  End Date Value Unrealistic Conditions (e.g. Drawdown) Standardize portfolio management Validate with EXCEL models

8. Results - 1 Fall 2002 12/31/99 - 5/31/02 452 stocks Spring 2003 12/31/99 - 5/31/02 712 stocks Fall 2003 6/14/02 - 6/12/03 S&P EMini (5 Min.) Annual ROI = 270% Annual ROI = 310% Annual ROI = 852%

9. Results - 2 Spring 2004 (Train) 10/12/01 - 12/26/03 S&P EMini (5 Min.) Annual ROI = 23,300% Spring 2004 (Test) 12/29/03 - 04/16/04 S&P EMini (5 Min.) Annual ROI = 2,172%

10. Demo

11. Conclusions Effective way to understand DM Process –Data Cleansing –Data Validation Very Good Results –ROI > 250% in all four cases Pragmatic implications