By Professor John Knopf (University of Connecticut School of Business) and Professor Kristina Lalova (Michigan State University – Eli Broad College of Business)
The purpose of our paper is to test the predictive performance of established bankruptcy prediction models in the finance literature against a new model inclusive of employee information. Our basic hypothesis is that employee satisfaction shows up as a predictor of financial distress prior to financial statement data. Well before bankruptcy or even negative financial performance, managers and employees may be aware of significant problems within their companies. Some of these significant problems include poor leadership, price competition, excessive borrowing, poor supplier relationships, and risky investment strategy. Although managers may be reluctant to disclose this information, workers may reveal problems through dissatisfaction with their jobs and the firm. However, we empirically test our model for predictability, not causation. Whether employees are less satisfied because of an impending bankruptcy or whether employee satisfaction impacts the chances of bankruptcy is an interesting topic for further studies.
We document that employee satisfaction is a strong predictor of bankruptcy. Specifically, we find that the employee satisfaction model predicts bankruptcy more accurately than any of the existing financial information-based models in years leading up to bankruptcy filings other than the year immediately prior to a bankruptcy filing. We additionally find that close to the bankruptcy filing date, models with inclusion of both financial statement and employee satisfaction data outperform models with inclusion of financial data only. Separately, we hypothesize that employees with a positive outlook on their companies around the bankruptcy filing are more likely to participate in a reorganization filing than in a liquidation filing. We document that employee satisfaction predicts bankruptcy emergence and that companies with higher employee satisfaction are more likely to emerge from bankruptcy.
We test four key bankruptcy models from the finance literature using a dataset from 2008 to 2020 to show that each one contains unique information regarding the probability of bankruptcy filings. We also build a new model to reflect employees’ attitudes and emotions before bankruptcy filings and include key variables from each of the four already established bankruptcy models in the literature in our model. Additionally, we build neural networks that learn from both employees’ textual reviews and ratings. We make four novel findings. First, employee satisfaction shows up as a predictor of bankruptcy prior to financial data-based models. Second, when we add employees’ attitudes in each of the four bankruptcy models, we find improvement to their predictive performance, although it is a small improvement above the models’ results. We conclude that in the year before the bankruptcy filing, financial statement and market information overwhelm any other information about the company. Third, employee satisfaction around the bankruptcy filing predicts whether the company will emerge from bankruptcy. Fourth, our results with the neural networks point to employee satisfaction textual reviews possessing superior information to employee satisfaction ratings one year before bankruptcy filings.
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