Worcester Polytechnic Institute Electronic Theses and Dissertations Collection

Title page for ETD etd-050405-180040

Document Typethesis
Author NameCheng, Wei
TitleFactor Analysis for Stock Performance
DepartmentMathematical Sciences
  • Dalin Tang, Advisor
  • Eugene Yablonski, Co-Advisor
  • Bogdan Vernescu, Department Head
  • Keywords
  • Factor Analysis
  • Principal Factor
  • Maximum-likelihood
  • Stock Performance
  • Date of Presentation/Defense2005-05-04
    Availability unrestricted


    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

  • Wei_Cheng.pdf

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