Using Contingency Approach to Improve Firms’ Financial Performance Forecasts

Document Type : Research Paper


1 Department of Financial Management, Ilam Branch, Islamic Azad University, Ilam, Iran

2 Department of Management, Faculty of Literature and Human Science, Ilam University, Ilam, Iran

3 Department of Accounting, Faculty of Literature and Human Science, Ilam University, Ilam, Iran

4 Department of Accounting, Ilam Branch, Islamic Azad University, Ilam, Iran



One of the challenging issues for investors and professionals is appropriate models to evaluate financial situation of the firms. In this regard, many models have been extracted by researchers using different financial ratios to resolve these issues. However, choosing a model based on the conditions and users’ needs is complex. The main objective of this study is to identify the effect of contingency variables on the firms’ financial performance forecasting models. The statistical population of the research includes all firms listed in Tehran Stock Exchange during the period 2011-2018, among which 154 firms were selected. The research data were collected from firm's financial statements and other source. Multiple Discriminant Analysis and Logit Regression model were used to test the research hypotheses. According to the results of discriminant analysis, environmental uncertainty and firm size positively improve the predictive power of the firm's financial performance, and business strategy and business competition don’t improve the predictive power of the firm's financial performance. Also, the results of logit regression indicated that environmental uncertainty, business strategy, and firm size improve predictive power of the firm's financial performance; but, business competition don’t improve predictive power of the model. The results of comparing the two methods showed that the Discriminant analysis method outperformed the logistic regression method.


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Volume 6, Issue 2
Corrected Proof
Spring 2021
Pages 357-376
  • Receive Date: 22 December 2019
  • Revise Date: 26 August 2020
  • Accept Date: 28 August 2020