The Usefulness of Operating Cash Flow for Predicting Business Bankruptcy in Medium-Sized Firms.
| Autor | Rodriguez-Masero, Natividad |
1 Introduction
This paper examines a model based on accounting information derived from financial statements and that is mainly focused on one variable, operating cash flow (OCF), which is not frequently used in these studies. Furthermore, the study seeks to contribute to the literature on predicting failure in medium-sized firms. As Tascon, Castano and Castro (2018) pointed out, although recent studies generally recognise that SMEs require specific tools for risk management in accordance with their particular characteristics, these kinds of businesses have received less attention than large and/or listed firms. The current accounting regulations in Spain require medium-sized firms to provide such information in their cash flow statement. However, cash flow statements are not compulsory for small firms; therefore, small businesses have not been included in the sample.
The obtained model achieves its highest success rate, of 95.24% accurate classifications, at three years, which is a high success rate compared to previous models and provides longer notice until the time of failure. According to the review carried out by Jardin (2015), the accuracy of traditional models decreases beyond one year. According to the same review, the model with the highest accuracy at three years (Gepp & Kumar, 2008) achieved 90.5% correct classifications and a year in advance only three models exceeded 95% (Gepp & Kumar, 2008; Korol, 2013; Sun, Jia, & Li, 2011). According to Jardin (2015), the accuracy of this estimation over a mid-term horizon is relevant to value the risk of financial institutions, which exists until the maturity of their loans. Moreover, from the perspective of the debtor firm, the possibility of foreseeing business failure with a margin of three years increases the capacity to take measures to correct the situation before it happens.
As the literature (Mari-Vidal, Marin-Sanchez, Segui-Mas, & Michael-Zamorano, 2014; Tascon & Castano, 2012) has pointed out, there is no commonly accepted framework regarding business failure or its determining factors, and most of the research is geared toward testing the informative content of financial statements as a predictor, looking for a relationship between accounting data and future solvency. The use of non-accounting data in these models has been scarce, even though, according to Tascon and Castano (2012), the results tend to improve when some of these non-financial variables are included. Authors such as Campillo, Serer, and Ferrer (2013) have shown that the accounting information provided by firms is useful when conducting this kind of research, yielding consistent results. In the same line, Altman, Iwanicz-Drozdowska, Laitinen, and Suvas (2017) cite several recent reviews of the efficacy of these models that conclude that the difference in the predictive accuracy of accounting-based and market-based models is not significant; however, the use of accounting-based models allows for a higher level of risk-adjusted return on credit activity (Agarwal & Taffler, 2008).
Finally, as Serer, Campillo, and Feres, (2009) pointed out, the timeframe must be considered a key variable in business failure predictions. Taking this idea into account, the present study follows the proposal made by Pina (1998) and cited by Enguidanos (2009), which involves using values of ratios calculated for several years prior to failure.
The rest of the paper is as follows. A literature review on business failure prediction is presented in section 2. Section 3 describes the data and methodological aspects, including a description of the sample, the definition of variables, and the methodology employed in the empirical research. Next, section 4 sets out the results obtained, and finally, section 5 presents the main conclusions of the research.
2 Background
2.1 The concept of failure
First of all, it is important to clarify what we understand by failure in this paper, since there are different definitions for this concept in the literature. As indicated by Mari-Vidal et al. (2014), we can basically group these definitions into two blocks: those that opt for an economic approach and those that apply a legal approach. The economic perspective gives rise to a wide variety of options. Tascon and Castano (2012) cite Graveline and Kokalari (2008), who mention three groups of concepts: ceasing to repay a debt; meeting the conditions set forth in current bankruptcy regulations; or having a patrimonial situation that is a precursor to future failure. One representative of this third option is Altman (1981), who defines failure as technical insolvency or in the sense of capital with a lack of liquidity. In turn, another group of authors (Gilbert, Menon, & Schwartx, 1990; Hill, Perry, & Andes, 1996) refers to the prolongation of continued losses. Gazengel and Thomas (1992) consider a failed firm to be one that cyclically generates more financial burden than income. And more recently, Davydenko (2007) argues that when the equity situation reflects a reduced value in assets or a shortage of cash, this can trigger business failure. Misas (2008) and Rodriguez, Molina, and Perez (2003) consider an entity to be unsuccessful when it incurs technical bankruptcy, understanding this to mean negative net equity (Tascon & Castano, 2012).
The fact that in the legal approach an objective criterion can be used to classify firms between failed and unsuccessful has been decisive in its greater use, as indicated by Mari-Vidalet al. (2014) and Somoza-Lopez and Vallverdu-Calafell (2003). It has used done in the case of Spain, for example, by Campillo et al. (2013) and Garcia-Mari, Sanchez-Vidal, and Tomaseti-Solano (2016). The first authors defended the option, indicating that its drawbacks (basically the considerable reduction of sample sizes) were overcome "by the advantages of objectivity and setting the date of failure in the selection process" (Campillo et al., 2013, p. 31). Consequently, this work understands failure to refer to a firm's initiation of legal insolvency proceedings, or "concurso de acreedores" in the Spanish legislation, and establishes the date of failure to be the moment the judge issues a ruling in this regard.
2.2 Financial ratios and bankruptcy prediction
The main line of research into business failure has so far focused on estimating a reliable prediction model, aiming to build a useful tool to prevent and correct business failure before it occurs (Garcia-Mariet al., 2016). Most of the research aims to test the informative content of financial statements as a predictive element, seeking a relationship between accounting data and future solvency (Mari-Vidal et al., 2014).
As mentioned, the literature cites Beaver (1966) as a pioneering author in studying the usefulness of accounting information to predict business failure, although his main objective was not to predict business failure, but rather to show the informative potential of accounting data. His contributions represented a great qualitative leap in the research, incorporating univariate discriminant analysis and evaluating the predictive capacity of the ratios separately. Subsequently, Altman (1968) included multivariate analysis, being the first to apply this technique to predict business failure.
These models were followed, with notable improvements, by those proposed by Altman, Haldeman and Narayanan (1977), Deakin (1972), Edmister (1972), Sinkey (1975), and Taffler (1983). This body of research achieved good results with small classification errors, although the statistical restrictions to which this methodology is subject (independence and normality of the variables and equality of the variance-covariance matrices) greatly distorted the results, lowering their degree of reliability (Campillo et al., 2013). Later works have used logistic regressions with logit or probit models (Martin, 1977), artificial intelligence techniques (Bell, Ribar, & Verchio, 1990; Serrano-Cinca, 1996; Shin & Lee, 2002), or DEA (Paradi, Asmild, & Simak, 2004). In the case of Spanish firms, a pioneering model was developed by Professor Amat in his doctoral thesis of 1990 and reformulated in 2008, obtaining a score where positive values indicate that the firm has a high probability of enjoying good economic-financial health. Both for Spanish and international cases, the abovementioned literature reviews by Campillo et al. (2013) and Tascon and Castano (2012) show an increasing group of techniques that use financial ratios to predict business failure, such as multiple discriminant analysis (MDA), logistic regression (LR), artificial neural network (ANN), support vector machines (SVM), rough sets (RS), case-based reasoning (CBR), decision tree (DT) and genetic algorithm (GA), which are mentioned in the review developed by Alaka et al. (2018). Of all these techniques, Tascon et al. (2018) indicate that the most used techniques for predicting business failure in SMEs are linear discriminant analysis (LDA), quadratic discriminant analysis (QDA), logit and probit. These authors (Tascon et al., 2018) add a remarkable recent innovation, based on the use of differences in percentiles to calculate the distance to failure in a specific group of firms. This work tested its model on a sample of small Spanish firms from the construction industry.
Amat, Manini and Renart (2017) refer to the literature reviews developed by Abdou and Pointon (2011), concluding, among other things, that there is not yet one technique that dominates over the others. So, according to these authors, the traditional statistical techniques are often used rather than techniques such as neural networks, decision trees and genetic programming because the former have been proven to perform very well. The predicting capabilities of both groups of approaches were sufficiently similar to make it difficult to distinguish between them (Abdou & Pointon, 2011), as mentioned by Amat et al. (2017).
2.3 Use...
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