Date of Award
2015
Publisher
North Dakota State University
Document Type
Thesis
Degree Awarded
Master of Science (MS)
Program
Statistics
Department
Statistics
College
Science and Mathematics
Faculty Advisor
Miljkovic, Tatjana
Description
Financial advisors working in a stock exchange market are often faced with a situation to convince a client of merits of investing in a company that just entered the market. To predict company's return based on its revenue, a simple linear regression may be used. This thesis finds that a model based on a mixture regressions is superior over a simple linear regression. The error term in each regression component is assumed to follow standard Gaussian distribution. The data is tested on 116 companies that entered the market as Initial Public Offering (IPO). A 2-component mixture regressions is found to provide the best fit for the data. A simulation study is conducted to verify the performance of this model. Optimum number of components is found using Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC) as well as the parametric bootstrapping of the likelihood ratio test statistics.
Recommended Citation
Liu, Xiyuan, "Estimating Return on Initial Public Offering Using Mixtures of Regressions" (2015). Statistics. 91.
https://digitalcommons.ndsu.edu/stats/91
Rights
NDSU Policy 190.6.2
Rights Link
https://www.ndsu.edu/fileadmin/policy/190.pdf
Handle Identifier
https://hdl.handle.net/10365/27902