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Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data

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Abstract

Recent studies of the prediction of corporate financial failure have taken into account many factors, mostly corresponding to financial ratios derived from firms’ annual accounts. Nevertheless, the current crisis and the consequent exponential increase in rates of insolvency have made it clear that the phenomenon of bankruptcy cannot be explained without reference to macroeconomic variables; thus, the overall condition of the economy, and not just the internal financial ratios of firms, must be addressed. In this paper, focusing on the Spanish construction sector from 1995 to 2011, we analyse selected econometric models for predicting bankruptcy, in which both macroeconomic variables and financial ratios are employed. In view of the large number of variables with these characteristics, which are frequently correlated with each other, and the consequent enormous number of models that would be obtained, we decided to focus on just five optimal econometric models for predicting the financial failure of firms, at 1, 2, 3, 4 and 5 years in advance, with a limited number of explanatory factors, to be selected by an automatic statistical procedure, guided solely by the data and based on a genetic algorithm. The empirical results obtained show that these econometric models are capable of achieving high rates of predictive success, both for in-sample and for out-of-sample predictions. In the latter case, failure and non-failure firms were classified with success rates of 98.5 and 82.5%, respectively, 1 year in advance. This predictive quality is maintained at 2, 3 and even 4 years in advance.

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Notes

  1. A genetic algorithm is a metaheuristic procedure, introduced by Holland (1975), which generates solutions to optimization problems using techniques inspired by natural evolution, such as inheritance, mutation, selection and crossover. See Acosta-González and Fernández-Rodríguez (2007, 2014), and the references therein, for a simple, but comprehensive introduction to GA.

  2. Sistema de Análisis de Balances Ibéricos (Iberian Balance Sheet Analysis System).

  3. This matching is frequent in the studies of financial distress because the whole data set for healthy firms would unbalance the prediction problem because the number of healthy firms is overwhelmingly higher than the failed firms (more than 99% in the SABI database). In this sense, as Maddala (1983, p. 91) points out, in the general context of LOGIT models which we have used, such sampling procedures only affect the constant term in the estimations.

  4. Instituto Nacional de Estadística (Spanish Institute of Statistics).

  5. The variables selected by GASIC in the five models are expressed in different units. Therefore, in order to determine the variables that exert most influence in bankruptcy, standardized coefficients are obtained for each model. To that end, we follow the methodology proposed by Menard (2001) with respect to logistic regression.

  6. To draw the following figures, the variables in question are ordered from lowest to highest, and the value of the corresponding median is assigned to the remaining variables. The probability of failure is then calculated using the Logit function.

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Correspondence to Fernando Fernández-Rodríguez.

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Acosta-González, E., Fernández-Rodríguez, F. & Ganga, H. Predicting Corporate Financial Failure Using Macroeconomic Variables and Accounting Data. Comput Econ 53, 227–257 (2019). https://doi.org/10.1007/s10614-017-9737-x

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