Name
Abstract | ||
|---|---|---|
Mergers and acquisitions (M&A) happens frequently between corporations to combine and/or transfer their ownerships, operating units and assets. The purpose of the study is to develop a service that is able to recommend a feasible M&A deal. We integrate the support vector machine model with the kernel tricks to automatically determine M&A deals. In the end of the study, our proposed technique is empirically validated, and the results show the effectiveness of the recommendation service. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1007/978-3-030-22338-0_12 | HCI IN BUSINESS, GOVERNMENT AND ORGANIZATIONS: INFORMATION SYSTEMS AND ANALYTICS |
Keywords | Field | DocType |
Mergers and acquisitions, Machine learning, Support vector machine, Financial kernel, Recommendation service | Kernel (linear algebra),Data science,Computer science,Support vector machine,Human–computer interaction,Mergers and acquisitions | Conference |
Volume | ISSN | Citations |
11589 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 0 | 5 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yu-Chen Yang | 1 | 0 | 1.35 |
| Yi-Syuan Ke | 2 | 0 | 0.34 |
| Weiwei Wu | 3 | 21 | 9.53 |
| Keng-Pei Lin | 4 | 117 | 11.61 |
| Yong Jimmy Jin | 5 | 0 | 0.34 |