Date of Award
Thesis - Restricted
Master of Science (MS)
Electrical and Computer Engineering
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.
Diggs, David Hugo, "Multiple Step Financial Time Series Prediction with Portfolio Optimization" (2004). Master's Theses (1922-2009) Access restricted to Marquette Campus. 4440.