Bankruptcy prediction with low-quality financial information

Abstract

The corporate bankruptcy prediction literature has traditionally relied on data from public, audited companies. However, the vast majority of firms worldwide are privately-held and lack the same level of scrutiny over their financial statements. As a result, these businesses usually produce less accurate and transparent accounting reports. Our research problem is to address this gap: how stakeholders deal with these less reliable information? Using a novel dataset of 503 private firms that filed for reorganization in Brazil between 2007 and 2020, we found that financial ratios had a significantly lesser effect on explaining default and bankruptcy than what previous research suggested, due in part to the lower information content in the accounting statements within our database. Instead, lenders seem to focus on harder-to-conceal variables, such as collateralizable assets, as well as on institutional factors, like proxies of financial statement quality. There is also concerning evidence that specialized attorneys can ”work the system” in favor of distressed companies regardless of their financial fundamentals. Additionally, we found that machine learning models outperformed traditional statistical ones in different sorts of metrics, corroborating the literature on the superior performance of non-linear approaches on datasets having synergistic causality among its features.