Name
Abstract | ||
|---|---|---|
Private equity investors seek to rank potential investment opportunities in growth stage private companies within an industry sector. The sparsity of historical investment transaction data for many growth stage private companies' may present a major obstacle to using statistical methods to discern industry specific features associated with successful and failed companies.This paper describes a Bayesian ranking approach based on i extracting and selecting features; ii training support vector machine classifiers from feature pairs of labeled companies in an industry; iii non-parametric estimation of posterior probabilities of success and failure; and iv ranking unlabeled companies within a cohort based on scores derived from posterior probability estimates. We anticipate that this approach will not only be of interest to statisticians and machine learning specialists with an interest in venture capital and private equity but extend to a broader readership whose interests lie in classification methods where missing data is the primary obstacle. |
Year | DOI | Venue |
|---|---|---|
2014 | 10.2139/ssrn.2096425 | AI Commun. |
Keywords | Field | DocType |
bayesian statistics,machine learning,private equity,bayesian analysis,support vector machines | Ranking,Private equity,Computer science,Posterior probability,Artificial intelligence,Venture capital,Bayesian statistics,Missing data,Transaction data,Machine learning,Bayesian probability | Journal |
Volume | Issue | ISSN |
27 | 2 | 0921-7126 |
Citations | PageRank | References |
0 | 0.34 | 2 |
Authors | ||
2 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Matthew Dixon | 1 | 0 | 0.34 |
| Jike Chong | 2 | 136 | 11.62 |