1. Data Mining BS/MS Project Decision Trees for Stock Market Forecasting Presentation by Mike Calder

2. Decision Trees Used for stock market forecasting –Classification trees –Regression trees Analysts attempt to predict the value of a given stock at some point in the future The methods can also be used to predict trends in the stock market as a whole 2

3. Motivation Accurate predictions in the stock market allow investing companies to thrive Identifying attributes that correlate with success in the stock market may lead to finding causation –If causes of success can be controlled, the economy can be pushed in a good direction 3

4. Stock Market Challenges Training set can be very large –All stock data over a period of time Predicting attributes tend to be binarized –like we saw when using the ID3 algorithm The target attribute (increase/decrease in stock value) can be numeric or nominal 4

5. Sample Decision Tree 5 VAR1 – if a stock’s average true range (average max-min) is greater than the stock’s moving average (average of end-of-day prices) VAR3 – if a stock’s end-of-day price is greater than the 50-day moving average VAR4 – if a stock’s end-of-day price divided by the daily range (max-min) is > 0.5 VAR5 – if a stock’s auto-correlation (degree of correlation with respect to its previous values) return has been > 0 for 5 days Taken from (Trader, 2014)

6. Processing Used Correlation based feature selection Numeric attribute binarization Target regression calculation N-fold cross-validation (usually n=10) Decision tree pruning 6

7. Sample Results 7 The red lines show real data for the 4 stocks, black lines show the decision tree’s predictions (y-axis represents the increase/decrease in stock value by percentage) Taken from (Trader, 2014)

8. Additional Complexity Advanced techniques can be combined with decision tree construction –Hierarchical hidden Markov model (HHMM) has been combined with decision trees for stock trend prediction –Other machine learning algorithms have been used with decision tree methods to forecast stock market changes as well 8

9. References R. Trader. “Using CART for Stock Market Forecasting”. Data Science and Trading Strategies. 2014.R. Trader. “Using CART for Stock Market Forecasting”. Data Science and Trading Strategies. 2014. S. Tiwara. “Predicting future trends in stock market by decision tree rough-set based hybrid system with HHMM”. International Journal of Electronics and Computer Science Engineering. 2012.S. Tiwara. “Predicting future trends in stock market by decision tree rough-set based hybrid system with HHMM”. International Journal of Electronics and Computer Science Engineering. 2012. T. Zhang. “Stock Market Forecasting Using Machine Learning Algorithms”. Stanford University. 2012.T. Zhang. “Stock Market Forecasting Using Machine Learning Algorithms”. Stanford University. 2012. 9