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

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Presentation transcript:

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

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

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

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

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

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

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

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

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

Demo

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