Title page for ETD etd-050405-180040

Document Type	thesis
Author Name	Cheng, Wei
URN	etd-050405-180040
Title	Factor Analysis for Stock Performance
Degree	MS
Department	Mathematical Sciences
Advisors	Dalin Tang, Advisor Eugene Yablonski, Co-Advisor Bogdan Vernescu, Department Head
Keywords	Factor Analysis Principal Factor Maximum-likelihood Stock Performance
Date of Presentation/Defense	2005-05-04
Availability	unrestricted
Abstract Factor models are very useful and popular models in finance. In this project, factor models are used to examine hidden patterns of relationships for a set of stocks. We calculate the weekly rates of return and analyze the correlation among those variables. We propose to use Principal Factor Analysis (PFA) and Maximum-likelihood Factor Analysis (MLFA) as a data mining tool to recover the hidden factors and the corresponding sensitivities. Prior to applying PFA and MLFA, we use the Scree Test and the Proportion of Variance Method for determining the optimal number of common factors. Then, rotation for PFA and MLFA were performed to improve the first order approximations. PFA and MLFA were used to extract three underlying factors. It was determined that the MLFA provided a more accurate estimation for weekly rates of return
Files	Wei_Cheng.pdf

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