Pairwise acquisition prediction with SHAP value interpretation

Katsuya Futagami, Yusuke Fukazawa, Nakul Kapoor, Tomomi Kito

Research output: Contribution to journalArticlepeer-review

Abstract

Predicting future pairs of the acquirer and acquiree companies is important for acquisition or investment strategy. This prediction is a challenging problem due to the following requirements: to incorporate various non-financial factors and to address the lack of negative samples. Concerning the former, we proposed including a network feature that represented the importance of an acquirer and an acquiree in the investment and category networks, as well as a company relation feature associated with their similarity and closeness. Considering the latter requirement, as negative examples, we set the pairs of acquirers and acquirees with the features that were similar to those of positive examples. This allowed learning minor differences between the companies selected for acquisition and the candidate ones. We evaluated our proposed prediction model using 2000–2018 acquisition logs collected from CrunchBase. Based on the analysis of the high SHapley additive explanation (SHAP) value features, we found that the newly considered network and company relation features had high significance (10 out of 22 top key features). We also clarified how these novel features contributed to the prediction of acquisition occurrence by interpreting the SHAP value.

Original languageEnglish
Pages (from-to)22-44
Number of pages23
JournalJournal of Finance and Data Science
Volume7
DOIs
Publication statusPublished - 2021 Nov

Keywords

  • Acquisition prediction
  • CrunchBase
  • Machine learning
  • SHAP value interpretation

ASJC Scopus subject areas

  • Economics and Econometrics
  • Finance
  • Statistics and Probability
  • Applied Mathematics
  • Computer Science Applications
  • Business, Management and Accounting (miscellaneous)

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