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Can earnings management information improve bankruptcy prediction models?

  • S.I. : Regression Methods based on OR techniques
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Abstract

This study investigates whether earnings management in its two forms (accruals and real activities manipulation) can improve bankruptcy prediction models. In particular, it examines whether special information extracted from earnings management, including potential manipulations of the reported earnings found in financial statements, might improve the accuracy of bankruptcy prediction models. It applies earnings management–based models, based on financial ratios and earnings management variables, to a sample of 6,000 French small and medium-size enterprises, then compares the classification rates obtained by these models with a model based solely on financial ratios. This study thus makes several contributions by (1) investigating novel predictors, accruals, and real activities manipulation variables, in the context of bankruptcy prediction modeling; (2) enabling analyses of the contribution of earnings management–based variables, in the form of static and dynamic indicators, to model performance; (3) revealing the influence of these variables on the forecasting horizon of bankruptcy prediction models (one- to three-year horizon); and (4) establishing that earnings management information provides a complementary explanatory variable for enhancing model performance.

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Notes

  1. Earnings management occurs when managers use their judgment to develop financial reports and structure transactions, but they also may alter financial reports to mislead some stakeholders about their firms’ underlying economic performance or influence contractual outcomes that depend on reported accounting numbers (Healy and Wahlen, 1999).

  2. Although failed firms are clearly outnumbered by non-failed firms in general, most studies use equal numbers (Yu et al., 2014), because predicting bankruptcy using a representation of the whole population (imbalanced data set, in which failed firms only represent 5%) can produce a suboptimal classification model that provides unfavorable predictions across data classes, concentrated on predicting the majority class (non-failed firms) and ignoring the minority class (failed firms). We use a popular paired-sample technique, such that we pair data about firms that have failed with firms that have not, according to their industry sector (Gordini, 2014). In turn, we can evaluate the impact of earnings management in the bankruptcy prediction model without the distortion that imbalanced data sets can cause.

  3. Small- to medium-sized enterprises employ between 10 and 250 people, earn turnover between €2 and €50 million, and have total assets between €2 and €43 million (http://ec.europa.eu). They represent 99.6% of firms in Europe, employ 69.5% of workers, and contribute to the 60.1% of the gross valued added generated in the EU-28 in 2017 (Source: Annual Report on European SMEs 2017/2018).

  4. Firms in financial intermediation or insurance, real estate firms, and foreign activities firms are excluded because their financial accounts have different characteristics than those of other firms, which would make comparisons based on earnings manipulation and financial information problematic.

  5. When the correlation between two variables is greater than 0.6, we remove one of them. That is, to balance the financial dimensions included in the initial set of variables, we remove one of the two variables that gives too much weight to a given financial dimension among those represented in the initial set of variables. Although neither variable is likely to overweight a given dimension, we rely on a factor analysis to remove the least relevant variable. Following this procedure, we ensure diversity among financial dimensions and avoid concentration of the most representative ones, such as profitability, liquidity, and financial structure (Kirkos, 2015).

  6. When we estimate ROA, we use net income rather than net income plus net-of-tax interest expense (a traditional measure), to avoid potential problems associated with estimating the tax rate.

  7. The reduction of discretionary expenditures model implies reducing expenditures in certain items, such as research and development, which can be found among listed firms, whereas our data set features by small and medium-sized firms. Thus, we have excluded it from the aggregated real activities measure.

  8. We developed and tested all models on the same test set, to ensure comparisons among them are valid. To achieve conciseness, and noting that 60 results for each evaluation metric (3 prediction horizons 4 models 5 prediction methods) would not be readable, we present the results using average values.

  9. We processed different segments of the sample in the training sets, according to size, to test whether model performance might depend on the training set size. Across training sets of different sizes (6000, 8000, and 12,000), the test generates results that match those from the main study, including significant differences between a model based solely on financial ratios and models based on earnings management variables (static and dynamic). As a further check, we consider the proposed models’ prediction capacity when the training set features a realistic proportion. Therefore, we built a training set with 2,000 failed firms and 38,000 non-failed firms (5/95 proportion). Although the earnings management–based models perform well in this scenario, prediction performance with regard to the failed class suffers. The results are available from the authors upon request.

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Acknowledgements

We are very grateful to the anonymous reviewers for their substantial contributions to improving this article.

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  1. IAE LILLE, Université de Lille, ULR 4999 LUMEN, Lille, France

    Eric Séverin

  2. INSEEC U Research Center, ESCE International Business School, Paris, France

    David Veganzones

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  1. Eric Séverin
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Séverin, E., Veganzones, D. Can earnings management information improve bankruptcy prediction models?. Ann Oper Res 306, 247–272 (2021). https://doi.org/10.1007/s10479-021-04183-0

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