Default prediction for small businesses
Abstract/Contents
- Abstract
- Small business loan defaults have been an active research area. Two major families of default prediction models are reduced form models and structural form models. Reduced form models focus on finding the statistical relations between default events and various economic variables, while structural form models focus on describing default event as a stopping time of some stochastic process. First we propose a reduced form model that uses massive amounts of data mined from the internet to supplement traditional economic variables in the data set. We compared the performance of reduced form models using internet covariates versus those using traditional covariates, and explored the possibility of a hybrid model using both types of covariates. Secondly, we propose a structural form model where we integrate the impact of relationship lending, a practice that has achieved success in recent years, into an incomplete information model. Numerical results are provided for both models.
Description
| Type of resource | text |
|---|---|
| Form | electronic; electronic resource; remote |
| Extent | 1 online resource. |
| Publication date | 2015 |
| Issuance | monographic |
| Language | English |
Creators/Contributors
| Associated with | Zhang, Lingren |
|---|---|
| Associated with | Stanford University, Department of Management Science and Engineering. |
| Primary advisor | Giesecke, Kay |
| Thesis advisor | Giesecke, Kay |
| Thesis advisor | Infanger, Gerd |
| Thesis advisor | Lai, T. L |
| Advisor | Infanger, Gerd |
| Advisor | Lai, T. L |
Subjects
| Genre | Theses |
|---|
Bibliographic information
| Statement of responsibility | Lingren Zhang. |
|---|---|
| Note | Submitted to the Department of Management Science and Engineering. |
| Thesis | Thesis (Ph.D.)--Stanford University, 2015. |
| Location | electronic resource |
Access conditions
- Copyright
- © 2015 by Lingren Zhang
- License
- This work is licensed under a Creative Commons Attribution Non Commercial 3.0 Unported license (CC BY-NC).
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