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

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

Technical & Fundamental Analysis No black swan events Technical Indicators

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

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

Dynamic Time Series

Dynamic Time Series

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

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

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

Preliminary Results

Preliminary Results – Dynamic DT

Preliminary Results – ANN

Future Work Model Implementation Investigation on more stocks Statistical analysis