Name
Abstract | ||
|---|---|---|
Equity control is one of the key areas of research in international business. This study employs artificial neural networks (ANNs) to model foreign equity control. Comparisons are made with traditional statistical modeling approaches. It was found that ANNs produce a more parsimonious set of independent variables that yield higher classification rates than logistic regression. Thus, it can be concluded that ANNs, with their complex, nonlinear structure, are able to model the relationship between transaction cost factors and majority/minority ownership; and percent equity ownership more accurately than the linear statistical approaches. |
Year | DOI | Venue |
|---|---|---|
2004 | 10.1016/j.ejor.2003.06.002 | European Journal of Operational Research |
Keywords | Field | DocType |
Neural networks,Joint ventures,Foreign equity control,Variable selection,Parsimony | Econometrics,Mathematical optimization,Transaction cost,Economics,Actuarial science,Feature selection,Variables,Equity (finance),International business,Statistical model,Artificial neural network,Logistic regression | Journal |
Volume | Issue | ISSN |
159 | 3 | 0377-2217 |
Citations | PageRank | References |
1 | 0.35 | 7 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Michael Y. Hu | 1 | 426 | 55.74 |
| G. Peter Zhang | 2 | 802 | 51.61 |
| Hai-Yang Chen | 3 | 3 | 2.18 |