Solvency Risk and Corporate Performance: A Case Study on European Retailers

Alexandra Horobet,Lucian Belascu,Cosmin-Alin Botoroga,Stefania Cristina Curea,Alexandra Smedoiu Popoviciu,Dan Gabriel Dumitrescu

doi:10.3390/jrfm14110536

Alexandra Horobet, Lucian Belascu + Show 4 more

Open Access

https://doi.org/10.3390/jrfm14110536

Copy DOI

Abstract

This paper proposes a new approach toward understanding the financial performance dynamics in the EU retail sector (pre-pandemic); we focus on the connection between indebtedness and solvency risk and other areas of corporate performance (e.g., liquidity, assets efficiency, and profitability). Its contribution resides in identifying the drivers behind solvency risk in a sector that went through significant transformations in recent decades, as well as the links between the various areas of performance of retailers, and their impacts on solvency risk, using the machine-learning random forest methodology. The results indicate a declining trend for solvency risk of EU food retailers after the global financial crisis and up until the beginning of the pandemic, which may reflect their maturity on the market, but also an adjustment to legal changes in the EU, meant to equalize the tax advantages of debt versus equity financing. Solvency risk accompanied by liquidity risk is a mark of the retail sector, and our results indicate that the most critical trade that EU retailers face is between solvency risk and liquidity, but is fading over time. The volatility of liquidity levels is an important predictor of solvency risk; hence, sustaining a stable and good level of liquidity supports lower risks of financial distress, and may mitigate the shock impacts for EU retailers. A higher solvency risk was accompanied by increased efficiency of asset use, but reduced profitability levels, which led to higher returns available to shareholders for high solvency risk retailers. Overall, retailers should focus on operational performance evidenced by financial indicator levels than on the volatility of these indicators as predictors of solvency risk.

Highlights

The choice that businesses face, between equity and debt, when financing needs related to current activities and/or investments, has never been an easy one
We propose a methodology based on machine-learning random forest, which is shown to be one of the best ways to predict financial distress and solvency risk and was applied to the European Union retail sector in the period between the two major crises of the twenty-first century: the global financial crisis of 2007–2009 and the COVID-19 pandemic
Before presenting the results of the machine-learning random forest methodology, an understanding of the solvency risk attributes and of the other indicators of financial performance for the sample of companies included in the analysis, is useful, as it allows one to better grasp the financial performance on European retailers

Summary

Introduction

The choice that businesses face, between equity and debt, when financing needs related to current activities and/or investments, has never been an easy one. Both types of financing provide benefits and disadvantages and, until now, optimal capital structure is still a contentious issue in corporate finance (Titman and Tsyplakov 2007; Brusov et al.2014; Dufour et al 2018). While the main advantage of equity financing resides in the lack of a financial burden on the company, since there are no regular payments to financers, using it means giving up ownership over the business and sharing the decisions with the other equity holders. Increased debt financing has advantages, as it reduces the overall tax burden through tax-deductible interest, but in times of economic downturn, it amplifies firms’ solvency risks and exposure to changes in market conditions (Abraham et al 2020).

Methods

Results

Conclusion