The impact of multinationality on firm value: A comparative analysis of machine learning techniques

Cemil Kuzey, Ali Uyar, Dursun Delen

Research output: Contribution to journalArticle

14 Scopus citations

Abstract

In this study, the impact of multinationality (as measured by foreign sales ratio) and fourteen other financial indicators on firm value (characterized by market capitalization and market-to-book ratio) for the period of 1997-2011 was investigated using two popular machine learning techniques: decision trees and artificial neural networks. We divided the time period of 1997-2011 into two periods; 1997-2004 and 2005-2011 to investigate the robustness of results pre- and post-IFRS implementation. To determine the relative importance of factors as the predictors of firm value, first, a number of classification models are developed; then, the information fusion based sensitivity analysis is applied to these classification models to identify the ranked order of the independent variables. Among the independent variables, multinationality was found to determine firm value only moderately. In addition to multinationality, other financial characteristics such as firm size (as measured by natural logarithm of assets), leverage, liquidity, and profitability were consistently found to be affecting firm value.

Original languageEnglish
Pages (from-to)127-142
Number of pages16
JournalDecision Support Systems
Volume59
Issue number1
DOIs
StatePublished - 1 Mar 2014
Externally publishedYes

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Keywords

  • Artificial neural networks
  • Decision trees
  • Firm value
  • Machine learning
  • Multinationality
  • Predictive analytics
  • Sensitivity analysis

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