Date of Award

Summer 2004

Degree Type

Thesis - Restricted

Degree Name

Master of Science (MS)

Department

Electrical and Computer Engineering

First Advisor

Povinelli, Richard

Second Advisor

Corliss, George

Third Advisor

Struble, Craig

Abstract

The Time Series Data Mining framework developed by Povinelli is extended to perform weekly multiple time-step prediction and adapted to perform weekly stock selection from a broader market. The stock selections are combined into weekly portfolios, and techniques from Modem Portfolio Theory and the Capital Asset Pricing Model are adapted to optimize the portfolios. The contribution of this work is the combination of stock selection and portfolio optimization to develop a temporal data mining based stock trading strategy. Results show that investors can increase overall wealth, obtain optimal weekly portfolios that maximize return for a given level of portfolio risk, overcome trading costs associated with trading on a weekly basis, and outperform the market over a given time range.

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