Corporate Financial Distress And Bankruptcy: Pr...

Corporate Financial Distress And Bankruptcy: Pr...

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Professor Shaked has authored several books and numerous articles, and appears frequently on television and in the press commenting on contemporary financial and business issues. His academic and professional research covers areas including, but not limited to, investment analysts, valuation, financial distress, solvency, preferences, fraudulent conveyance, bankruptcy, LBOs, international business, mergers and acquisitions, economics, corporate structure analysis, corporate financial decisions, and capital markets.

For the most part, research purporting to address the issue of financial distress has actually studied samples of bankrupt companies. Financial distress and bankruptcy are different. In contrast, this paper starts with a sample of companies that are financially distressed but not yet bankrupt. The sample was obtained by screening the Compustat industry database with a three-tiered identification system. The screen bifurcated companies into financially and non-financially distressed groups. A multi-tiered screen reduces the incidence of mistakenly identifying a non-distressed company as financially distressed. The paper then compares factors indicating the likelihood of future bankruptcies to those indicating future financial distress. To do this, an early warning financial-distress model was developed and compared to a methodologically similar existent model of bankruptcy. The final financial distress model included only one variable present in the bankruptcy model and four new variables. The limited overlap of explanatory factors between the models questions the similarity of financial distress and bankruptcy. Statistical tests lend support to the notion that the bankruptcy process is not just a continuation of a downward spiraling cycle of financial distress. Our hypothesis is that financial distress is something that happens to companies as a consequence of operating decisions or external forces while bankruptcy is something that companies choose to do to protect their assets from creditors.

Using a large dataset that includes nearly 31,000 Greek private firms we examine the determinants of the probability of corporate financial distress. Using a multi-period logit model, we find that profitability, leverage, the ratio of retained earnings-to-total assets, size, the liquidity ratio, an export dummy variable, the tendency to pay out dividends and the growth rate in real GDP are strong predictors of the probability of financial distress for Greek private firms. A model including these variables exhibits the highest in-sample and out-of-sample performance in terms of correctly classifying firms that went bankrupt as more likely to go bankrupt. The predictive ability of the model remains when we increase the forecast horizon, suggesting that the model works well over short and longer time horizons.

This paper contributes to the literature by examining the determinants of the probability of financial distress for private firms in a developing economy, that of Greece. There are several reasons why examining the determinants of the probability of financial distress for private firms is important. First, private firms, especially small- and medium-sized enterprises (SMEs) are of key importance for economic activity, employment and innovation in many countries. With respect to Greece, for example, 99.9% of Greek firms are defined as SMEs, they account for 84.9% of the total workforce and 69% of the value added in the economy (OECD 2015). Similarly, SMEs are the backbone of the economy in the Eurozone. According to Wymenga et al. (2012), across the Eurozone SMEs account for around 98% of all Euro area firms, around 75% of total employment and generate around 60% of gross value added while over 99% of US and UK firms are privately owned and are responsible for more than half of the GDP of the US and the UK, respectively. Second, the evidence shows that private firms are different from public ones. Private firms are smaller, more leveraged, more dependent on trade credit and bank loans, invest more, and face higher borrowing costs.Footnote 4 These differences between private and public firms raise the issue of whether the (non-stock-market) factors that predict the probability of bankruptcy for public firms are (a) the same for private firms and (b) have the same sign as those for public firms and perhaps similar orders of magnitude as well. Third, providing information on the probability of bankruptcy for private firms is quite challenging as these firms do not have their shares traded on a stock exchange and hence there is a lack of market data. As a result, the information we can use to predict bankruptcy is mainly derived from financial statements. Therefore, examining whether it is possible to successfully predict the probability of financial distress for private firms is essential, not least because it will help in formulating policy relating to the supply of credit by banks and the cost of credit for this type of firm. Evidence on the performance of bankruptcy forecast models for private firms will also provide insights into the usefulness of information contained in financial statements for private firms.

The rest of the paper is organized as follows. Section 2 provides a review of the literature and some methodological background to modelling the probability of financial distress using the discrete hazard/multi-period logit approach. Section 3 describes the dataset. Section 4 presents the results for the various models we estimate, the results from forecast accuracy tests and robustness checks. Section 5 concludes.

Several econometric techniques have been used to predict financial distress for publicly traded firms. Beaver (1966) uses univariate analysis to investigate the ability of accounting data to predict bankruptcy. Based on this method a financial ratio for the firm of interest is compared to a benchmark ratio to discriminate a failed firm from a non-failed firm. Altman (1968) employs multiple discriminant analysis to determine the Z-score, a widely used measure for predicting bankruptcy. The objective of discriminant analysis is to generate a linear combination of variables that best separate the bankrupt firms from the non-bankrupt ones. Although popular, this method is subject to some limitations. First, it fails to provide a test of the significance of the individual variables. Second, it assumes that the predictors are distributed as multivariate normal. Third, it prevents the use of dummy variables that can enhance the predictive ability of the bankruptcy forecast models.

Etheridge and Sriram (1997) examine the performance of neural networks with respect to corporate financial distress prediction. They find that the neural network outperforms multivariate discriminant analysis and logistic models. Nittayagasetwat (1996) applies a neural network to forecast bankruptcy for US firms. He finds that his neural network model exhibits higher predictive ability than a logit model. While neural network techniques can provide higher classification rates, they cannot provide any information on the significance of the predictor variables. It is therefore more difficult to assess the contribution of the predictor variables to the prediction of financial distress.

Shumway (2001) argues and shows that the models used by Altman (1968), Ohlson (1980) and Zmijewski (1984) are misspecified as they do not properly address the length of time that a healthy firm has survived, thereby inducing bias. He overcomes this problem by employing a discrete hazard model which is econometrically equivalent to a multi-period logit model. This model has two main advantages. First, it allows researchers to take advantage of all the available firm-year observations. Second, it is a dynamic model in the sense that it enables the probability of bankruptcy to change over time as a function of a vector of explanatory variables that also vary with time. In his empirical work, Shumway (2001) finds that a discrete hazard model delivers the best performance in terms of out-of-sample predictive ability. Since the seminal work of Shumway (2001), Hillegeist et al. (2004) and Agarwal and Taffler (2008) have used discrete hazard models to compare the performance of accounting-information-based and stock-market-information-based bankruptcy prediction models for US and UK firms, respectively. Chava and Jarrow (2004) extend the model of Shumway (2001), highlighting the importance of including industry effects in the discrete hazard model. They also provide evidence that the predictive power of accounting variables weakens when stock-market-based variables are included in the model. Bharath and Shumway (2008) use a discrete hazard model to examine the contribution of the probability of default derived from the Merton (1974) model to predicting financial distress. They find that the probability of default based on the Merton model is not a sufficient statistic for default. Using US data, Campbell et al. (2008), building on the work of Shumway (2001), find that the default probability based on the Merton model has relatively little forecasting power in a discrete hazard model when conditioning on accounting and stock-market-based variables. Tinoco and Wilson (2013) compare hazard models with neural networks and Z-score models using UK data. They find that a panel logistic model with time-varying covariates, which is equivalent to a hazard model, that combines accounting, stock market and macroeconomic variables outperforms the neural network approach and Z-score approach. Similarly, Bauer and Agarwal (2014) show that for the UK, the hazard model developed by Shumway (2001) outperforms the Z-score model developed by Taffler (1983) and the contingent claims model of Bharath and Shumway (2008).

While there is a wealth of evidence on the prediction of financial distress for publicly-listed firms in developed markets, evidence is a little harder to come by for developing markets and for private firms. Kwak et al. (2012) predict bankruptcy for Korean firms after the 1997 financial crisis. Using the accounting variables that Altman (1968) and Ohlson (1980) use, Kwak et al. (2012) estimate their bankruptcy prediction model using multiple criteria linear programming (MLCP). They find that their model works as well as traditional multiple discriminant analysis and traditional logit analysis. However, other than noting where there is a significant difference in the means of the variables for bankrupt and non-bankrupt firms, Kwak et al. (2012) do not focus on the sign and significance of the variables in the prediction of bankruptcy as they are concerned with a comparison of MLCP with traditional multiple discriminant analysis and traditional logit analysis. In examining the predictors of financial distress in the trading and services sectors in Malaysia, Alifiah (2014) finds that the likelihood of distress is negatively related to the debt ratio, the working capital ratio and net income to total assets while it is positively related to the total asset turnover ratio and the base lending rate. Charalambakis and Garrett (2016) evaluate the contribution of accounting and stock-market-based information to the prediction of bankruptcy across developed and emerging markets using discrete hazard models. While they find that book leverage combined with three stock-market-based variables best predict the probability of financial distress for UK firms, they find that stock market information has no significant role to play in predicting the probability of distress for Indian firms. Rather, they find the probability of financial distress in India is negatively related to an accounting-based measure of profitability and positively related to the ratio of current liabilities to total assets. 59ce067264

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