Dr. Zagdbazar "Zag" Davaador

Dr. ZagDr. Zagdbazar "Zag" Davaador is an assistant professor of finance at the University of Wisconsin-Parkside. He earned his bachelor's degree from the Mongolian University of Science and Technology and master's degree in data science from the University of Michigan-Ann Arbor. He also holds a Ph.D. in finance from Texas A&M International University. He is a Certified Financial Planner™. Prior to joining the University of Wisconsin, Dr. Davaador taught finance courses at Western Michigan University and Wayne State University. He has 5+ years of experience in the high-tech industry.

  • Session information

    Special Research Presentation

    Fintech Research Track
    Friday, November 4
    10:10 to 11:30 a.m.
    Hyflex Room, Schneider Hall

    A growing number of lending platforms such as Prosper, Funding Circle and Lending Club enable
    borrowers to obtain microloans in just a few clicks on their mobile phones without much hassle. At
    the same time, peers who have extra cash may become investors or lenders in this market to earn
    profits. Computer algorithms replace bank due diligence on these loans and peer lenders evaluate
    credit risks based on borrowers’ basic information. It is interesting how such unsophisticated lenders
    process borrowers' information to decide whether or not to invest in a particular loan. In his research,
    Dr. Zagdbazar Davaadorj examines whether a borrower's job title could signal credit worthiness, which
    might impact loan performance and interest rate. Davaadorj defines borrowers as skilled if their job
    belongs to the fifth zone in the U.S. Department of Labor’s occupations lists. For unsophisticated
    lenders in a peer-to-peer setting, simple information such as borrowers' job titles could signal
    borrowers' credit worthiness. Furthermore, the initial listing status, whole or fractional, depends on the
    skilled job title. Additional analyses suggest the impact is strengthened for borrowers whose income
    is verified. Results are stronger after addressing the endogeneity problem and running analyses on the
    propensity-matched sample.