Maintaining consistent profitability in turbulent economic conditions requires continuous adaptation, highlighting the importance of financial resilience (FR) for business success. FR encompasses financial robustness, foresight, flexibility, and recovery capabilities, all crucial for navigating economic shocks. While FR is a dynamic concept, existing measures often focus on households and lack an objective, enterprise-centric perspective. This research addresses this gap by measuring FR using financial flexibility (FF) and the cash conversion cycle (CCC), offering a combined stock and flow perspective. The relationship between CFP and FR remains unclear, with prior studies presenting conflicting findings regarding the FF-CFP and CCC-CFP nexuses. Some research suggests a positive linear relationship, others a negative one, and still others a non-linear (concave or U-shaped) relationship, with results varying depending on the period (pandemic vs. non-pandemic) and the methodology. This study focuses on Taiwanese listed manufacturing firms—a significant sector of Taiwan's economy—to investigate the FR-CFP relationship during the COVID-19 pandemic. The research aims to determine the nature of the FR-CFP relationship and whether this relationship differs for environmentally sensitive and non-sensitive industries. The quantile regression (QR) approach is employed to analyze data and overcome limitations of the ordinary least squares (OLS) method, providing a more robust analysis of the relationship across different quantiles of corporate financial performance, which is proxied by Tobin's Q.

The concept of FR is multifaceted, encompassing the ability to access internal and external resources during financial adversity and the capacity to recover quickly from shocks. While various indicators of FR have been proposed, many are subjective measures from household or individual perspectives. This study uses a more objective approach for businesses by combining FF and CCC. Existing literature on the FF-CFP nexus presents conflicting views, with some studies reporting a positive impact based on free cash flow theory, while others suggest a negative impact due to agency costs or over-investment. Recent studies increasingly highlight the potential for nonlinear (concave or U-shaped) relationships. Similarly, studies on the CCC-CFP relationship show conflicting results, some finding a positive correlation and others a negative correlation, with a few proposing a nonlinear (concave) relationship. The lack of studies synthesizing stock and flow perspectives to examine the comprehensive FR-CFP nexus motivated this research.

This study uses data from 6051 firm-quarters of Taiwan Stock Exchange (TSE) listed manufacturing firms from Q1 2020 to Q3 2021, covering the early phase of the COVID-19 pandemic. The dependent variable is Tobin's Q, a widely used measure of CFP. The explanatory variable, FR, is a composite measure derived from FF and CCC. FF is calculated as the sum of cash flexibility ((cash + cash equivalent)/total assets) and debt flexibility (1 - (total liabilities/total assets)). CCC is calculated as the sum of the average number of days of inventory, accounts receivable, less the average number of days of accounts payable. Control variables include revenue growth (REVG), net profit growth before taxes (BNIG), growth rate of owner’s equity (OEG), average collection days (ARD), research and development intensity (RDG), firm size (SIZE), and financial leverage (LEV). The quantile regression (QR) approach is used to investigate the relationship between FR and CFP across different Tobin's Q quantiles. This approach offers several advantages over OLS, providing more robust results and allowing for analysis of the relationship across different parts of the CFP distribution. The model examines the curvilinear relationship by including both FR and FR squared (FR2) as explanatory variables. The study also conducts inter-quantile regressions to assess whether the differences across quantiles are statistically significant. Robustness checks were performed using price-to-book ratio (PBR) as an alternative measure of CFP and by including an industry-adjusted FR variable. Finally, the sample was divided into environmentally sensitive (ES) and non-ES firms to explore industry heterogeneity.

The quantile regression results reveal a concave-convex relationship between FR and CFP. Specifically, a concave (inverted U-shaped) relationship is found for firms in the lower and median Tobin's Q quantiles, indicating that beyond a certain point, increasing FR leads to diminishing returns in CFP. In contrast, a convex (U-shaped) relationship is observed in the highest (90th) Tobin's Q quantile. This suggests that for high-performing firms, there is an initial decrease in CFP with increasing FR, followed by an increase, potentially reflecting the need for a significant level of FR to support sustained high performance. Inter-quantile tests confirm significant differences in the FR-CFP relationship across quantiles. The robustness checks using PBR and industry-adjusted FR largely confirm the primary findings. Regarding industry heterogeneity, the concave relationship is prevalent among ES firms across most quantiles. However, non-ES firms exhibit a concave relationship in lower quantiles and a convex relationship in higher quantiles. The inflection points and confidence intervals were calculated to validate the U-shaped and inverted U-shaped relationships.

The findings challenge the traditional linear view of the FR-CFP relationship. The concave-convex relationship supports both the 'too much of a good thing (TMGT)' and 'too little of a good thing (TLGT)' effects. The TMGT effect explains the diminishing marginal returns to FR beyond a certain threshold for lower and median performing firms, while the TLGT effect captures the initial negative relationship between FR and CFP before an increase in higher-performing firms. The differences observed between ES and non-ES firms highlight the importance of considering industry-specific factors when assessing the impact of FR on CFP. The results contribute to a more nuanced understanding of the FR-CFP nexus, particularly during periods of economic uncertainty like the COVID-19 pandemic. The study’s findings offer important insights into the optimal level of FR for firms with varying levels of market-based performance.

This study makes several key contributions. It examines the FR-CFP relationship during the COVID-19 pandemic, using a novel composite FR measure and the QR approach to capture the nonlinearity. It finds a concave-convex relationship that varies based on firm performance and environmental sensitivity. This research offers guidance for managers to optimize FR levels, investors to assess firm performance, and policymakers to develop supportive policies.

The primary limitation is the relatively short study period, focusing only on the early phase of the COVID-19 pandemic. Future research should examine a longer time frame, including data from other countries and industries, to increase generalizability. Furthermore, additional factors influencing CFP during the pandemic, such as government support, could be included in future models. Finally, the mechanisms through which FR impacts CFP could be investigated in more detail (e.g., through the capital, labor, or technology channels).