Measuring firm performance using financial ratios: A decision tree approach

Dursun Delen, Cemil Kuzey, Ali Uyar

Research output: Contribution to journalArticle

96 Citations (Scopus)

Abstract

Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e.; financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.

Original languageEnglish
Pages (from-to)3970-3983
Number of pages14
JournalExpert Systems with Applications
Volume40
Issue number10
DOIs
StatePublished - 1 Aug 2013
Externally publishedYes

Fingerprint

Decision trees
Information fusion
Factor analysis
Taxation
Sensitivity analysis
Profitability

Keywords

  • Decision trees
  • Exploratory factor analysis
  • Financial ratios
  • Firm performance
  • Sensitivity analysis

Cite this

@article{6887922e24f6445387b109047402109c,
title = "Measuring firm performance using financial ratios: A decision tree approach",
abstract = "Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e.; financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.",
keywords = "Decision trees, Exploratory factor analysis, Financial ratios, Firm performance, Sensitivity analysis",
author = "Dursun Delen and Cemil Kuzey and Ali Uyar",
year = "2013",
month = "8",
day = "1",
doi = "10.1016/j.eswa.2013.01.012",
language = "English",
volume = "40",
pages = "3970--3983",
journal = "Expert Systems with Applications",
issn = "0957-4174",
publisher = "Elsevier Ltd",
number = "10",

}

Measuring firm performance using financial ratios : A decision tree approach. / Delen, Dursun; Kuzey, Cemil; Uyar, Ali.

In: Expert Systems with Applications, Vol. 40, No. 10, 01.08.2013, p. 3970-3983.

Research output: Contribution to journalArticle

TY - JOUR

T1 - Measuring firm performance using financial ratios

T2 - A decision tree approach

AU - Delen, Dursun

AU - Kuzey, Cemil

AU - Uyar, Ali

PY - 2013/8/1

Y1 - 2013/8/1

N2 - Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e.; financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.

AB - Determining the firm performance using a set of financial measures/ratios has been an interesting and challenging problem for many researchers and practitioners. Identification of factors (i.e.; financial measures/ratios) that can accurately predict the firm performance is of great interest to any decision maker. In this study, we employed a two-step analysis methodology: first, using exploratory factor analysis (EFA) we identified (and validated) underlying dimensions of the financial ratios, followed by using predictive modeling methods to discover the potential relationships between the firm performance and financial ratios. Four popular decision tree algorithms (CHAID, C5.0, QUEST and C&RT) were used to investigate the impact of financial ratios on firm performance. After developing prediction models, information fusion-based sensitivity analyses were performed to measure the relative importance of independent variables. The results showed the CHAID and C5.0 decision tree algorithms produced the best prediction accuracy. Sensitivity analysis results indicated that Earnings Before Tax-to-Equity Ratio and Net Profit Margin are the two most important variables.

KW - Decision trees

KW - Exploratory factor analysis

KW - Financial ratios

KW - Firm performance

KW - Sensitivity analysis

UR - http://www.scopus.com/inward/record.url?scp=84875369177&partnerID=8YFLogxK

U2 - 10.1016/j.eswa.2013.01.012

DO - 10.1016/j.eswa.2013.01.012

M3 - Article

AN - SCOPUS:84875369177

VL - 40

SP - 3970

EP - 3983

JO - Expert Systems with Applications

JF - Expert Systems with Applications

SN - 0957-4174

IS - 10

ER -