A Different Power of Attorney: Lawyers and the Bankruptcy Prediction of Private Firms with Questionable Accounting

Bankruptcy prediction stands as a crucial instrument for financial institutions in managing credit risk. But these kinds of organizations are not the only ones that are (or, at least, should be) interested in the topic.

Legal scholars must discern the signs and drivers of distress and analyze whether the bankruptcy framework is effectively facilitating the restructuring of economically feasible enterprises while eliminating inefficient ones. Similarly, policymakers striving to maintain the stability and productivity of the economic system should comprehend the inner workings of corporate restructurings and liquidations to enact informed and effective policies.

Despite its relevance, existing bankruptcy prediction literature has predominantly drawn upon data from large, public, audited companies, overlooking the characteristics of the majority of firms worldwide: they are private, unaudited, and possess significantly less reliable financial statements. This discrepancy underscores the need for research that accounts for the distinct economic and financial landscape of private enterprises.

In a recent paper, published in the latest issue of Expert Systems with Applications, Professor Dennis Shasta (NYU) and I have tried to address this research gap. We sought to ascertain the value creditors, scholars, and policymakers could attribute to the less reliable financial information of private debtors and whether prediction models remained viable in such contexts.

Drawing from a sample of over 500 private, already distressed firms, web-scraped directly from corporate restructuring lawsuits in Brazil, our focus was on identifying factors contributing to successful emergence from the crisis. This approach sidesteps the need for subjective definitions of distress or predetermined time frames for predicting financial difficulties, potentially mitigating sampling biases prevalent in prior studies.

In terms of modeling, we expanded the repertoire of traditionally used variables to encompass the trajectory of the company prior to filing, not solely its present condition. Moreover, we employed a diverse array of estimation methods, encompassing traditional statistical techniques alongside machine learning algorithms.

Additionally, we incorporated institutional variables to address nuances inherent to the legal proceedings. These variables pertain to aspects surrounding the companies or the bankruptcy proceedings themselves, rather than the firms' (financial) fundamental attributes. For instance, we introduced a dummy variable to capture the impact of a judge's specialization on case outcomes—designating a judge as specialized if they exclusively presided over bankruptcy cases, and as a generalist otherwise. Similarly, we incorporated a dummy variable indicating attorney specialization, sourced from the website and LinkedIn profiles of the attorneys associated with the initial filings. Additionally, proxies were included to assess accounting reporting quality, such as the presence of an external auditor or the adequacy of financial statements in preceding years.

Our findings have shown that, in the context of low-quality accounting information, financial ratios wielded a lesser impact on explaining default and bankruptcy than suggested by previous research, corroborating evidence that financial features have become less informative with time. Furthermore, lenders exhibited a preference for variables less susceptible to manipulation, such as collateralizable assets and the perceived credibility of financial reports. Moreover, our analysis revealed that, in general, the trajectory of the company leading up to bankruptcy held greater significance in its ability to reverse its fortunes than its current state at the time of the filing.

Alarmingly, and justifying the title of this post, in all but one model the most significant feature that impacted the chances of a successful restructuring was the hiring of specialized attorneys to handle the case. Although this effect is rationalizable and has also been found in other areas of the law (eg, in tax or healthcare), that is a concerning piece of evidence. It can mean that the fate of a distressed company may rely more on the expertise and strategic maneuvering of legal and financial professionals than on the economic and operational foundations of the firm itself.

However disconcerting, this evidence serves as a catalyst for broader discussions. It (i) underscores the limitations of traditional credit risk metrics for a large number of firms worldwide, (ii) advocates for enhanced firm accountability in financial reporting, and (iii) prompts scrutiny of insolvency frameworks that may inadvertently reward legal professional acumen over firm operational merit.

The author’s full article can be found here.

Eduardo da Silva Mattos is a Partner at Øx Analytics and a Professor of Economics at FAE Business School (Brazil).