1. Data Mining Techniques in Stock Market Prediction
Sen Jiao
EECS 435, Data Mining
Apr. 14, 2015
Case Western Reserve University

2. Outline Technical & Fundamental Analysis Bayesian Probability
Dynamic Time Series
Artificial Neural Network Training

3. Technical & Fundamental Analysis
No black swan events
Technical Indicators

4. Bayesian Probability
Update the probability estimates for a hypothesis once additional evidence is learned
Stand for performance accuracy of individual stock over a certain period of time
Provide standard of optimal decision-making for selecting significant technical indicators

5. Bayesian Probability 300 trading days
Candidate Indicators: MA, Bias, ADX

6. Dynamic Time Series

7. Dynamic Time Series

8. Artificial Neural Network (ANN)
Training algorithm iteratively adjusts the connection weights
Generalize relevant output when network is adequately trained
Training automatically stops when generalization stops improving

9. Artificial Neural Network (ANN)
ANN is expected to yield better prediction
results than dynamic time series in most cases
# of hidden neurons: 10
70% training data, 15% validation, 15% testing

10. Preliminary Results Stock: Apple (AAPL)
Data from Apr. 11, 2013 to Apr. 11, 2015
505 trading days
450 days training, 55 days prediction
Matlab Neural Network Toolbox

11. Preliminary Results

12. Preliminary Results – Dynamic DT

13. Preliminary Results – ANN

14. Future Work Model Implementation Investigation on more stocks
Statistical analysis