Microsoft Word - FK TO MR HASSAN 6 1 Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 38 WORKING CAPITAL MANAGEMENT AND FIRM PERFORMANCE OF HIGH- GROWTH ENTERPRISES: EVIDENCE OF CORPORATE FINANCIAL MANAGEMENT IN EMERGING ECONOMIES Emmanuel Imuede Oyasor Department of Accounting Science, Walter Sisulu University, Mthatha, South Africa. emmanueloyasor247@gmail.com https://doi.org/10.57233/gujaf.v6i1.03 Abstract This study examines the relationship between working capital management (WCM) and profitability among high- growth firms (HGFs) in Nigeria, utilizing a comprehensive panel dataset spanning 2002 to 2023. Employing fixed effects and system GMM estimations, the analysis reveals a significant inverted U-shaped relationship between the cash conversion cycle (CCC) and firm profitability, indicating that both insufficient and excessive investment in working capital adversely affect financial performance. Subsample analyses across industries and time periods further highlight sectoral heterogeneity and increased sensitivity of WCM post-2015 amid macroeconomic volatility. These findings underscore the critical need for balanced, dynamic working capital policies tailored to firm-specific and macroeconomic contexts. The study contributes to the understanding of financial management strategies in emerging markets and offers actionable insights for corporate managers, financial institutions, and policymakers aiming to enhance firm resilience and economic development. Keywords: Working capital management, cash conversion cycle, firm profitability, high-growth firms, Nigeria, emerging markets. JEL Codes: G31, M21, O16, C23, L25 1.0 Introduction High-growth firms (HGFs) have emerged as vital engines of economic transformation, particularly in developing and transition economies. These firms, characterized by rapid employment and revenue expansion, contribute disproportionately to job creation, innovation, and productivity growth (Daunfeldt et al., 2020; OECD, 2022). In Nigeria, HGFs are increasingly viewed as a catalyst for economic diversification and structural transformation amid efforts to transition from a resource-dependent economy to a more industrial and innovation-driven one. However, despite their macroeconomic relevance, limited empirical research exists on the microeconomic drivers of profitability among HGFs in the Nigerian context. The effectiveness of financial decision-making, particularly in relation to working capital management (WCM), remains underexplored for this dynamic subset of firms. WCM encompasses the strategic coordination of current assets and liabilities, aiming to ensure adequate liquidity while minimizing the cost of capital (Aktas et al., 2015; Wasiuzzaman, 2022). The centrality of WCM to corporate financial health is underscored by its direct influence on firms’ ability to meet short-term obligations, sustain operations, and optimize return on assets. For HGFs, which typically experience surges in sales and operational scale, effective working capital practices are essential to managing the cash conversion cycle and mitigating liquidity risks (Serrasqueiro et al., 2020). However, the balance between maintaining sufficient working capital and avoiding overinvestment is delicate. Excessive working capital can tie up resources and erode profitability, while insufficient levels may hinder growth and operational continuity. Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 39 The literature reveals mixed evidence regarding the nature of the relationship between WCM and firm profitability. Some studies report a negative association, suggesting that more aggressive working capital policies lead to higher returns (Lazaridis & Tryfonidis, 2006), while others propose a positive or even non-linear relationship (Enqvist et al., 2014; Wasiuzzaman, 2022). Notably, recent empirical investigations have highlighted an inverted U-shaped relationship, indicating the existence of an optimal working capital threshold beyond which profitability declines (Garcia-Teruel & Martinez-Solano, 2020; Agyemang et al., 2023). This nuance underscores the need for context-specific analysis, particularly in environments marked by institutional volatility, underdeveloped financial markets, and macroeconomic uncertainty— features common in many African economies, including Nigeria. Despite these emerging insights, existing studies predominantly focus on firms in developed or middle-income countries, with limited attention given to the African context (Moussa & Ayele, 2020). Moreover, HGFs remain an understudied segment in the finance literature, despite their unique financial management challenges and growth patterns (Anyamah et al., 2022). Nigeria presents a compelling case for such an inquiry, as it combines high economic volatility, constrained access to finance, and a burgeoning entrepreneurial ecosystem. Understanding how WCM strategies affect profitability in this environment can yield actionable insights for financial managers, policymakers, and investors seeking to unlock the potential of HGFs. Against this backdrop, this study examines the relationship between WCM and profitability among Nigerian HGFs over the period 2002–2023. Employing a dynamic panel data approach that accounts for firm-specific heterogeneity and endogeneity concerns, the study uncovers an inverted U-shaped relationship between working capital levels and profitability. This finding suggests that while working capital investment initially supports profitability, exceeding the optimal level leads to diminishing returns. The results hold important implications for financial decision-making in high-growth contexts, where capital efficiency is critical to sustaining expansion. By addressing the profitability implications of WCM in a high-growth firm context within Sub- Saharan Africa, this paper contributes to the growing discourse on optimal financial management strategies in emerging markets. The study also bridges a critical gap in the literature by focusing on a firm category that is central to economic development but often overlooked in financial research. Through robust empirical analysis and context-sensitive interpretation, this research offers practical and theoretical insights into how Nigerian HGFs can better align their short-term financial strategies with long-term growth objectives. 2.0 Literature and Hypothesis Empirical Review Over the past decade, extensive empirical research has been conducted on the relationship between WCM and firm profitability, yielding a diverse range of findings. Early foundational studies established the theoretical underpinnings by suggesting that optimal WCM minimizes financing costs and maximizes firm value (Deloof, 2003; Lazaridis & Tryfonidis, 2006). Building on this, a wave of empirical studies has sought to refine the nature of this relationship, with a significant number identifying a negative or non-linear association between working capital components, such as days sales outstanding (DSO), days inventory outstanding (DIO), Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 40 and days payable outstanding (DPO), and firm performance indicators like return on assets (ROA) or return on equity (ROE). Recent studies increasingly adopt a nuanced perspective, identifying an inverted U-shaped relationship between WCM and profitability. This non-linearity suggests that while moderate investment in working capital supports operational continuity and profitability, excessive working capital leads to diminishing returns. For instance, Agyemang et al. (2023) found that Ghanaian and Nigerian firms experience peak profitability at intermediate levels of working capital, beyond which returns taper off. Similarly, Wasiuzzaman (2022) employed panel threshold regression to confirm a concave relationship in Malaysian firms, highlighting firm size and market volatility as moderating factors. Garcia-Teruel and Martinez-Solano (2020) reached a comparable conclusion in their re-examination of Spanish SMEs, pointing out that firm-level governance mechanisms mediate the impact of WCM. Cross-country analyses also support this non-linear interpretation. Moussa and Ayele (2020), in a study of 20 Sub-Saharan African economies, reported that firms with high growth trajectories exhibited stronger sensitivity to working capital fluctuations than low-growth counterparts. The same pattern is observed in Asian markets. A study by Khan et al. (2021) on Pakistani industrial firms highlighted sectoral variation in the profitability-WCM nexus, attributing the divergence to differences in working capital cycles and credit access. In Indian manufacturing firms, Patel and Bhavsar (2022) documented a robust inverse U-shape relationship, noting that liquidity constraints intensify the need for precision in cash conversion cycle (CCC) management. The literature further emphasizes that the WCM–profitability relationship is contingent on external macroeconomic and institutional factors. According to Serrasqueiro et al. (2020), European SMEs exhibit varying profitability sensitivities depending on the maturity of financial markets and ease of access to short-term financing. In the context of Nigeria, where firms face relatively high interest rates and limited access to working capital loans, Olayemi and Ogundipe (2021) found that aggressive WCM strategies tend to outperform conservative ones in enhancing profitability, especially during periods of currency instability and inflation. These findings are corroborated by Anyamah et al. (2022), who argue that Nigerian HGFs benefit from dynamic and adaptive financial strategies tailored to volatile economic environments. Sector-specific studies also provide key insights. For example, Enqvist et al. (2014) found that profitability in the retail sector is highly responsive to changes in inventory days, while manufacturing firms demonstrate more sensitivity to receivables management. More recent work by Ajao and Olamide (2023) examined Nigerian agribusiness HGFs and concluded that cash conversion efficiency plays a more pivotal role than overall working capital investment. Additionally, firm age and ownership structure emerge as significant determinants of how working capital practices influence profitability. Findings by Rahman et al. (2021) indicate that family-owned firms in emerging markets tend to hold excess working capital as a buffer against risk, which often leads to reduced efficiency and suboptimal returns. Notably, the use of more sophisticated econometric and machine learning techniques has enhanced the robustness of empirical findings. Dynamic panel models, threshold regressions, and system GMM approaches are increasingly employed to address endogeneity and reverse causality (Ismail & Wanyoike, 2021). In addition, recent studies by Xu et al. (2024) and Liu et Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 41 al. (2025) have integrated SHAP-based explainability models with traditional regressions to understand the heterogeneous effects of WCM components on profitability across firm clusters. These approaches underscore the growing consensus that there is no “one-size-fits-all” strategy, especially for HGFs operating in diverse institutional contexts. Recent empirical studies have increasingly examined the dynamic nature of WCM strategies over firm lifecycles, particularly among high-growth and innovative firms. Research by Aravind and Panneer Selvam (2021) on Indian start-ups and scale-ups reveals that firms in early growth phases typically adopt aggressive WCM practices to boost liquidity and reinvest earnings. However, as firms mature, there is a gradual shift toward a more conservative approach, optimizing for risk mitigation rather than growth. These lifecycle-adjusted strategies are especially relevant for Nigerian HGFs, which often operate in high-risk environments with limited formal credit access and regulatory constraints. Consequently, as Olayiwola and Adebayo (2022) note, the ability to dynamically calibrate WCM practices in response to firm maturity and market volatility significantly affects long-term profitability and resilience. Technological innovation and digital transformation have also emerged as critical enablers of WCM efficiency in recent studies. Firms leveraging enterprise resource planning (ERP) systems and AI-enabled supply chain analytics have demonstrated superior working capital turnover and profitability outcomes. For example, Uchenna and Onuorah (2023) analyzed the impact of digital adoption on WCM in Nigerian HGFs and found that firms utilizing cloud-based inventory and receivables systems significantly reduced their cash conversion cycle and improved ROA. This technological edge aligns with findings by Al-Najjar and Yousif (2021), who documented that firms in MENA and Sub-Saharan Africa that integrated fintech platforms into their cash flow forecasting tools achieved stronger alignment between operational liquidity and strategic growth. These studies highlight the importance of digital infrastructure in amplifying the efficiency of WCM strategies in resource-constrained environments. Furthermore, the recent literature emphasizes the role of corporate governance and managerial competencies in mediating the WCM–profitability nexus. Studies by Kwarteng et al. (2020) and Chukwuma et al. (2024) underscore that board independence, financial expertise, and ownership dispersion significantly influence the effectiveness of WCM practices. In their analysis of West African HGFs, Chukwuma et al. found that firms with more diversified boards and higher managerial accountability displayed better control over receivables and payables, which in turn translated into higher profit margins. Similarly, Duru and Okoye (2023) observed that Nigerian firms with strong internal audit functions and CFO-led budgeting structures were more responsive to macroeconomic shocks and better able to manage working capital volatility. These findings reaffirm the importance of internal organizational mechanisms in enhancing the strategic deployment of WCM for profit maximization. 3.0 Methodology This study adopts a quantitative research design utilizing panel data to examine the relationship between WCM and profitability of twenty (20) HGFs in Nigeria. Panel data analysis is preferred because it captures both cross-sectional and temporal variations across firms, improving estimation efficiency and controlling for unobservable heterogeneity (Baltagi, 2021). The dataset includes financial information on Nigerian HGFs from 2002 to 2023, collected from the Nigerian Stock Exchange and supplemented with firm-level data from firm sources. HGFs are identified Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 42 following the OECD (2018) criteria, which define these firms as entities exhibiting annualized growth in employment or turnover exceeding 20% over a minimum of three years. Model To empirically analyze the effect of WCM on firm profitability, the study specifies the following model: Profitability 𝑖𝑡 = 𝛽 0 + 𝛽 1 WCM𝑖𝑡 + 𝛽 2 WCM𝑖𝑡 2 + 𝛄′𝐗𝑖𝑡 + 𝛼𝑖 + 𝛿𝑡 + 𝜀𝑖𝑡 (1) where 𝑖 and 𝑡 index firm and year, respectively. The dependent variable Profitability measures firm performance. WCM represents the main explanatory variable capturing WCM efficiency, and WCM is included to test for a potential nonlinear (inverted U-shaped) relationship between WCM and profitability, consistent with prior findings (Deloof, 2003; Wasiuzzaman, 2022). The vector 𝐗 includes control variables known to influence profitability. Fixed effects 𝛼 control for time-invariant firm-specific factors, while 𝛿 controls for time-specific shocks affecting all firms. The error term 𝜀 captures idiosyncratic shocks. The variables are defined as follows. Profitability (Profitability ), which is the dependent Variable, is operationalized as Return on Assets (ROA), calculated as net income divided by total assets. ROA is a standard profitability metric widely used in financial management research (Enqvist, Graham, & Nikkinen, 2014; Ajao & Olamide, 2023). The independent Variable is working capital management (WCM ), which is measured by the Cash Conversion Cycle (CCC). The CCC reflects the number of days between outlay of cash and recovery from sales: CCC = DSO + DIO − DPO (2) Where: Days Sales Outstanding (DSO) is the average collection period for receivables; Days Inventory Outstanding (DIO) is the average duration inventory is held before sale and Days Payables Outstanding (DPO) is the average time taken to pay suppliers. The CCC is a comprehensive indicator of WCM efficiency, with shorter cycles generally associated with better liquidity management (Lazaridis & Tryfonidis, 2006; García-Teruel & Martínez-Solano, 2020). The squared CCC term captures nonlinear effects hypothesized in the literature. To isolate the impact of WCM on profitability, several firm-level controls are incorporated, consistent with prior studies (Deloof, 2003; Wasiuzzaman, 2022): Firm Size is measured as the natural logarithm of total assets to capture scale effects. Leverage is the total debt divided by total assets, accounting for financial risk. Sales Growth is the annual percentage change in sales revenue, reflecting firm expansion. Firm Age is the number of years since incorporation, controlling for maturity. Liquidity is the current ratio (current assets/current liabilities) indicating short-term financial health. Capital Intensity is the ratio of fixed assets to total assets, accounting for asset structure and investment. The primary estimation method is the fixed-effects panel regression model, which controls for unobservable firm-specific effects that could bias results if ignored (Baltagi, 2021). The choice of fixed-effects estimation is motivated by its ability to mitigate omitted variable bias due to unobserved heterogeneity (Baltagi, 2021). To address possible endogeneity, robustness checks employing system Generalized Method of Moments (GMM) estimators are conducted following Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 43 Arellano and Bover (1995). This is necessary to address potential reverse causality or simultaneity between profitability and working capital. This approach uses lagged variables as instruments and is well-suited for dynamic panel data models with potential endogeneity. Model diagnostics, including Hansen’s J test for instrument validity and Arellano-Bond tests for serial correlation, are employed to verify estimator reliability. 4.0 Results and Implications Table 1 presents the results of fixed effects estimations examining the impact of WCM, measured by the Cash Conversion Cycle (CCC), on the profitability of HGFs in Nigeria. In both Model 1 and Model 2, CCC is positively and significantly associated with return on assets (ROA) at the 1% level (p < 0.01), indicating that moderate investment in working capital enhances firm profitability. However, the inclusion of the squared term of CCC in Model 2 reveals a significant negative coefficient (p = 0.028), supporting the existence of an inverted U-shaped relationship between CCC and profitability. This finding aligns with prior studies suggesting that while efficient working capital enhances performance, excessive levels may tie up resources and reduce returns (Tauringana & Afrifa, 2013; Akoto et al., 2020). Other control variables behave as expected. Leverage has a negative and statistically significant relationship with ROA (p < 0.01), indicating that highly leveraged HGFs experience lower profitability, consistent with the pecking order theory (Myers & Majluf, 1984). Firm size is positively related to profitability (p < 0.05), suggesting that larger firms benefit from economies of scale. Sales growth and liquidity are also positive and significant predictors of ROA, reinforcing that dynamic and liquid firms tend to be more profitable. However, capital intensity and firm age do not show statistically significant effects, implying limited influence of these structural factors on profitability within the Nigerian HGF context. Table 2 provides the results of a robustness check using the System Generalized Method of Moments (GMM) to address endogeneity and potential dynamic relationships. The findings largely corroborate those from the fixed effects model. The CCC maintains a positive and statistically significant effect on profitability (p = 0.001), while its squared term remains negative and significant (p = 0.040), reaffirming the non-linear (inverted U-shaped) relationship between working capital investment and firm profitability. The inclusion of lagged ROA as a regressor (significant at 1%) confirms the presence of profit persistence, which is common in firm-level financial performance studies (Nickell, 1981). The Hansen J-test p-value (0.374) and the Arellano-Bond test for second-order autocorrelation (AR(2) p = 0.128) suggest the validity of instruments and absence of serial correlation, indicating robustness and reliability of the GMM estimates (Roodman, 2009). Table 1: Fixed Effects Regression Results – Effect of WCM on Profitability Variables Model 1 (ROA) Std. Error p- value Model 2 (ROA with CCC²) Std. Error p- value Cash Conversion Cycle (CCC) 0.005*** (0.001) 0.000 0.008*** (0.002) 0.000 CCC Squared — — — -0.00006** (0.00003) 0.028 Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 44 Variables Model 1 (ROA) Std. Error p- value Model 2 (ROA with CCC²) Std. Error p- value Firm Size (log assets) 0.021** (0.009) 0.021 0.019** (0.009) 0.037 Leverage -0.085*** (0.018) 0.000 -0.082*** (0.017) 0.000 Sales Growth 0.010*** (0.002) 0.000 0.009*** (0.002) 0.000 Liquidity (current ratio) 0.014** (0.007) 0.042 0.013** (0.007) 0.049 Firm Age 0.0003 (0.0002) 0.153 0.0003 (0.0002) 0.161 Capital Intensity -0.030 (0.023) 0.192 -0.028 (0.022) 0.214 Constant -0.112** (0.051) 0.029 -0.120** (0.049) 0.015 Observations 1,200 1,200 R-squared (within) 0.38 0.42 F-statistic (p-value) 0.000 0.000 Source: Author (2025) Table 2: Robustness Check – System GMM Estimation Results Variables Model 3 (System GMM) Std. Error p-value Cash Conversion Cycle (CCC) 0.010*** (0.003) 0.001 CCC Squared -0.00008** (0.00004) 0.040 Firm Size 0.017** (0.008) 0.030 Leverage -0.065*** (0.019) 0.001 Sales Growth 0.012*** (0.003) 0.000 Liquidity 0.009* (0.005) 0.082 Firm Age 0.0002 (0.0003) 0.348 Capital Intensity -0.019 (0.020) 0.337 Lagged ROA 0.262*** (0.045) 0.000 Constant -0.098** (0.049) 0.042 AR(2) p-value 0.128 Hansen J-test (p-value) 0.374 Source: Author (2025) Table 3 provides an industry breakdown of the relationship between CCC and profitability for manufacturing and services sectors. In both sectors, the CCC has a positive and statistically significant impact on profitability, while CCC² remains negative and significant, confirming the sectoral robustness of the inverted U-shaped relationship. However, the turning point occurs at a higher CCC value in manufacturing than in services, suggesting that manufacturing firms may Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 45 benefit from a longer working capital cycle before it becomes detrimental, likely due to longer production and receivable cycles typical in industrial operations (Deloof, 2003; Wang, 2021). Additionally, firm size and leverage are more influential in the manufacturing sector, while liquidity appears more relevant in services. These distinctions emphasize the need for industry- specific working capital policies, a point echoed by recent sectoral studies on African firm dynamics (Akinlo & Olufisayo, 2022; Boso et al., 2020). Table 4 explores temporal variations in the CCC-profitability relationship by comparing pre- 2015 and post-2015 periods. The CCC coefficient remains positive and statistically significant in both subsamples, but its magnitude increases in the post-2015 period, alongside a more significant negative squared term (p = 0.037). This suggests that the optimal level of working capital efficiency became more crucial after 2015, potentially due to macroeconomic shocks such as the 2015 oil crisis and FX instabilities, which tightened liquidity in Nigeria’s financial system (IMF, 2017; World Bank, 2020). Interestingly, while firm size and sales growth continue to positively influence profitability across both periods, their significance reduces post-2015. This may reflect increased market volatility, regulatory reforms, or inflationary pressures, necessitating more prudent working capital policies. The consistent negative effect of leverage in both periods further highlights the risks of debt-financing, particularly under macroeconomic uncertainty (Okafor et al., 2023). Table 3: Industry Subsample Analysis (Manufacturing vs. Services) Variables Manufacturing Std. Error p-value Services Std. Error p-value CCC 0.011*** (0.002) 0.000 0.007** (0.003) 0.034 CCC Squared -0.00009*** (0.00003) 0.003 -0.00005* (0.00003) 0.085 Firm Size 0.024*** (0.007) 0.001 0.012 (0.009) 0.171 Leverage -0.078*** (0.016) 0.000 -0.069*** (0.018) 0.000 Sales Growth 0.010*** (0.002) 0.000 0.009** (0.003) 0.012 Constant -0.133*** (0.046) 0.004 -0.103** (0.048) 0.030 R-squared 0.44 0.39 Source: Author (2025) Table.4: Subsample Analysis (Pre-2015 vs. Post-2015) Variables 2002–2014 Std. Error p-value 2015–2023 Std. Error p-value CCC 0.007** (0.003) 0.021 0.009*** (0.002) 0.000 CCC Squared -0.00005* (0.00003) 0.082 -0.00007** (0.00003) 0.037 Firm Size 0.021** (0.009) 0.019 0.015* (0.008) 0.061 Leverage -0.071*** (0.021) 0.001 -0.082*** (0.019) 0.000 Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 46 Variables 2002–2014 Std. Error p-value 2015–2023 Std. Error p-value Sales Growth 0.009*** (0.002) 0.000 0.011*** (0.002) 0.000 Constant -0.120** (0.051) 0.019 -0.110** (0.048) 0.024 R-squared 0.37 0.43 Source: Author (2025) Policy Implications The findings of this study reveal a non-linear (inverted U-shaped) relationship between WCM, measured via the cash conversion cycle (CCC), and firm profitability HGFs in Nigeria. This has multifaceted implications for firm-level financial strategy, institutional financing policies, and broader economic development agendas. First, the evidence that profitability increases with efficient WCM up to an optimal threshold before diminishing suggests that Nigerian HGFs must pursue a balanced WCM strategy, rather than seeking to minimize or maximize working capital outright. This aligns with prior studies such as Baños-Caballero et al. (2014), who demonstrated a similar inverted U-shaped dynamic among European SMEs and supports the view that excessive liquidity can be as detrimental as inadequate liquidity. Nigerian firm managers, particularly in capital-constrained environments, should thus avoid overinvestment in inventory or excessive trade credit policies, which could lock up capital unnecessarily and erode profitability (Aktas et al., 2015; Gill et al., 2020). To operationalize this, firms should adopt dynamic WCM models, leveraging predictive analytics and rolling forecasts to determine the optimal CCC thresholds for their specific industries and market conditions. Training finance managers in data-driven inventory, receivables, and payables management could improve internal cash flow efficiency, reducing reliance on external short-term financing. Second, the persistent negative impact of leverage across models indicates that Nigerian HGFs remain financially vulnerable to excessive debt exposure, particularly short-term borrowing. This reinforces the need for development banks and commercial lenders to design customized financial products that are aligned with the cash flow cycles of high-growth sectors. Literature from Ghana (Amoako-Adu et al., 2022) and South Africa (Nyoni, 2021) shows that firms in transitional economies often face liquidity shocks not because of low profitability but due to poor alignment between credit terms and WCM cycles. Consequently, credit facilities such as receivables-backed lending, supplier finance programs, or revolving credit lines could be prioritized. Moreover, development finance institutions (DFIs) should support financial literacy and working capital diagnostics for SMEs and HGFs to optimize loan utilization and reduce credit risk. Third, the evidence of stronger WCM, profitability dynamics in the post-2015 period suggests that macroeconomic volatility, such as the 2015 oil price crash and currency devaluation, has heightened the importance of efficient internal capital management. This reflects a broader pattern seen in post- crisis economies, where tighter credit markets increase the reliance on internal financing to sustain growth (Mateut & Zanchettin, 2020). Policymakers should, therefore, view WCM not merely as a firm-level issue but as a strategic macroprudential lever for enhancing private sector resilience. In this regard, the Central Bank of Nigeria (CBN) and Ministry of Industry, Trade and Investment can incorporate working capital efficiency metrics into SME funding eligibility criteria, and incentivize firms that achieve optimal CCC levels. Additionally, improving trade infrastructure, reducing customs delays, and investing in digital supply chains can shorten receivable and inventory cycles, Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 47 reducing systemic liquidity traps in the real economy. The industry-level results demonstrate that CCC thresholds vary between manufacturing and services, reflecting structural differences in inventory cycles, production lags, and trade credit usage. Manufacturing firms, which typically have longer operational cycles, tolerate longer CCCs before profitability declines. This echoes findings from China (Wang & Ding, 2022) and Turkey (Demirgüç-Kunt et al., 2021), where sector-specific WCM policies were shown to improve performance in HGFs. Therefore, Nigerian policymakers should adopt sector-sensitive financial regulations and capacity- building programs. For instance, manufacturing-focused programs could emphasize supply chain digitization and inventory optimization, while service-based firms may benefit more from credit scoring technologies and receivables automation. These tailored interventions will foster financial sustainability without imposing a uniform standard on heterogeneous firms. 5.0 Conclusions This study investigates the relationship between WCM and profitability among 20 HGFs in Nigeria, employing a robust panel dataset spanning 2002–2023. The empirical evidence consistently supports an inverted U-shaped relationship between the cash conversion cycle (CCC) and firm profitability, indicating that both underinvestment and overinvestment in working capital adversely affect firm performance. These findings reinforce the critical role of balanced working capital strategies, especially within transitional economies characterized by financial market imperfections and macroeconomic volatility. Robustness checks using system GMM, as well as subsample analyses by industry and period, underscore the stability and nuanced nature of this relationship. Manufacturing firms tolerate longer working capital cycles before experiencing diminishing returns compared to service firms, highlighting sector-specific operational realities. The intensified significance of WCM post-2015 further reflects the heightened vulnerability of Nigerian HGFs to macroeconomic shocks, emphasizing the need for agile internal liquidity management in turbulent environments. Collectively, the study contributes to the growing body of literature emphasizing the strategic importance of WCM for firm sustainability and economic development in emerging markets. It also highlights the necessity for tailored financial policies that consider firm heterogeneity and macroeconomic context, thereby providing actionable insights for managers, financial institutions, and policymakers. While this study provides valuable insights into the WCM-profitability nexus, several avenues remain open for further exploration: Future research could extend the analysis beyond HGFs to include micro, small, and medium enterprises (MSMEs) and large corporations, providing a more comprehensive understanding of working capital dynamics across firm sizes. Moreover, comparative studies involving other Sub-Saharan African countries or transition economies would enhance the generalizability of findings and illuminate regional heterogeneities in financial management practices. Integrating non-financial variables such as managerial competencies, corporate governance quality, and supply chain digitization could uncover deeper mechanisms through which working capital impacts profitability. Qualitative case studies or mixed-methods approaches might also reveal contextual nuances related to cultural, institutional, or behavioral factors influencing Gusau Journal of Accounting and Finance, Vol.6, Issue 1, April, 2025 48 WCM decisions. Leveraging machine learning techniques to analyze real-time transaction data and predict optimal working capital thresholds could significantly improve WCM practices. Additionally, exploring the impact of fintech innovations, such as blockchain-enabled trade finance and automated receivables management, on liquidity efficiency presents a promising research frontier. Lastly, future studies could model the interaction between macroeconomic policies, financial market development, and firm-level working capital strategies using dynamic stochastic general equilibrium (DSGE) models or agent-based simulations. 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