Data Mining Techniques in Stock Market Prediction

Sen JiaoEECS 435, Data MiningApr. 14, 2015Case Western Reserve University

OutlineTechnical & Fundamental

AnalysisBayesian ProbabilityDynamic Time SeriesArtificial Neural Network Training

Technical & Fundamental AnalysisNo black swan eventsTechnical Indicators

Bayesian ProbabilityUpdate 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 daysCandidate 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: 1070% training data, 15% validation, 15% testing

Preliminary ResultsStock: Apple (AAPL)Data from Apr. 11, 2013 to Apr.

11, 2015505 trading days450 days training, 55 days

predictionMatlab Neural Network Toolbox

Preliminary Results

Preliminary Results – Dynamic DT

Preliminary Results – ANN

Future WorkModel ImplementationInvestigation on more stocksStatistical analysis