Etd

 

Factor Analysis for Stock Performance Public

Downloadable Content

open in viewer

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

Creator
Contributors
Degree
Unit
Publisher
Language
  • English
Identifier
  • etd-050405-180040
Keyword
Advisor
Defense date
Year
  • 2005
Date created
  • 2005-05-04
Resource type
Rights statement
License

Relationships

In Collection:

Items

Permanent link to this page: https://digital.wpi.edu/show/xg94hp68r