This study offers an empirical examination of the causal relationship between financial risks and firm performance among industrial firms listed on the Amman Stock Exchange credit risks and financial risk ratios are used to gauge overall financial risk, while the performance measure employed is stock market return.
Secondary data were collected from the annual financial statements of Jordanian industrial firms for the period 2009–2022. The generalized method of moments (GMM) was used for coefficient estimation to account for endogeneity and unobserved heterogeneity. Additionally, the panel causality test proposed by Dumitrescu and Hurlin (2012) was applied to determine the direction of causality.
The results reveal a homogeneous causal relationship between firm performance and financial risks. Specifically, credit risk, total loans, and short-term debt ratios positively influence stock returns. In contrast, long-term debt and firm size are found to negatively affect market performance. Finally, the quick ratio and liquidity ratio exhibit insignificant effects on stock market return.
This study provides valuable insights for the management and stakeholders of industrial firms in decision-making processes. Credit risk should be closely monitored, and short-term debt may represent a more effective financial strategy than long-term debt.
While most prior studies have relied on OLS and fixed effects models to analyze the impact of financial indicators on firm performance, this study is among the first to use a dynamic GMM approach. The findings offer important implications and recommendations for owners and managers of industrial firms, enabling them to assess the risks and opportunities associated with debt financing and to leverage the potential benefits of credit risk management.
1. Introduction
Understanding the volatility of stock returns remains a central concern in financial research, particularly regarding how financial risks affect firm performance. For industrial firms, which are key contributors to national economic growth, managing financial risk is critical for maintaining profitability and ensuring long-term sustainability. In recent decades, especially since the global financial crisis of 2007–2009, attention has intensified on how different forms of financial risk, such as credit and liquidity risk, impact corporate outcomes. This increased focus stems from growing market uncertainty and the challenges of forecasting firm performance in an increasingly volatile environment.
Financial risk refers to the uncertainty associated with financial returns, often captured through return variance. This uncertainty can significantly affect investor expectations and managerial decision-making, especially when risks are not fully observable or predictable. As a result, firms must strike a balance between risk-taking and performance, not only to enhance shareholder value but also to ensure operational resilience.
The literature on risk and performance suggests that these variables are interlinked, with a bidirectional relationship that is vital for understanding a firm's operational sustainability. Theoretical perspectives propose a trade-off between performance and risk. Nevertheless, the empirical literature presents mixed findings regarding the nature and direction of this relationship.
Although extensive research has examined the relationship between financial risk and firm performance in the banking sector, relatively little attention has been devoted to non-financial firms, especially those in emerging markets. In Jordan, for example, the industrial sector plays a pivotal role in the economy, yet empirical studies investigating how financial risks influence its performance remain limited. Industrial firms differ significantly from financial institutions in terms of capital structure, access to external finance, corporate governance systems, and risk exposure (Ananzeh, 2024). These distinctions underscore the need for targeted research to understand how financial risks manifest in industrial settings and how they affect firm-level performance metrics.
This study aims to fill this gap by examining the impact of credit and liquidity risks on the performance of Jordanian industrial firms. In doing so, it contributes to the broader literature on risk–performance dynamics in non-financial sectors and provides insights that can inform more effective risk management practices and policy decisions.
The findings of this study carry significant implications for executives, investors, and policymakers. By applying advanced statistical techniques that account for endogeneity, we examine the causal relationships between firm performance and key financial indicators. The results indicate that credit risk, total loans, and short-term debt ratios are positively associated with firm performance, suggesting their potential to enhance profitability. In contrast, long-term debt exerts a negative impact, implying that excessive reliance on long-term financing may undermine firm performance. This highlights the importance for practitioners to consider debt structure and agency costs in financial decision-making. Short-term financing appears to be a more favourable external financing option, associated with lower risk compared to long-term debt. Accordingly, both executives and investors should closely monitor financial indicators to support informed decision-making and achieve an optimal balance between risk and return.
2. Literature review
2.1 Theoretical perspectives and general evidence on the Risk–Performance relationship
The relationship between financial risk and firm performance is a central theme in the finance literature, though theoretical interpretations often diverge. Classical financial theory posits a positive relationship, whereby greater risk is compensated with higher returns (Hawley, 1893). From this standpoint, risk-taking is viewed as an essential driver of profitability and growth. In contrast, agency theory cautions that excessive risk-taking can erode firm value due to managerial opportunism and heightened bankruptcy risk (Jensen and Meckling, 2019; Bowman, 1979). Jensen (1986) further explains the link between financial strategy and firm performance within the agency framework. When ownership and management are separated, financial decisions and debt financing intensify pressure on managerial performance and reduce moral hazard. Thus, when internal funding is insufficient to pursue investment opportunities, owners and managers may prefer short-term debt to mitigate risk.
Empirical evidence is equally nuanced. Some researchers suggest that prudent risk-taking enhances innovation, operational efficiency, and competitiveness (Ali and Islam, 2022). Others report that unmanaged or excessive risk impairs firm performance, particularly under volatile economic conditions or in firms with weak internal controls. This divergence highlights the context-specific nature of the risk–performance nexus, influenced by firm characteristics, regulatory environments, industry sectors, and macroeconomic dynamics.
2.2 Empirical evidence: financial vs. non-financial firms
Substantial research has examined financial institutions, especially banks. In developed economies, Li et al. (2019) found that SMEs in the EU with lower credit risk exhibited stronger performance, attributed to better transparency and governance. Barbua-Miu et al. (2019) reported a positive relationship between liquidity risk and performance in European non-financial firms. Within the banking sector, Saeed and Zahid (2016) found that effective credit risk controls positively affected the profitability of UK banks.
In the United States, Samad (2012) emphasized that credit risk metrics, such as loan loss provisions and non-performing loans, significantly predicted bank performance. Tailab (2014) analyzed the impact of debt on 30 US energy firms between 2005 and 2013. The results showed that total debt and short-term debt enhanced firm performance, while long-term debt had an insignificant negative association. He also found a significant negative effect of firm size on performance. In contrast, Arhinful and Radmehr (2023) concluded that debt levels negatively affect the performance of non-financial firms in Japan.
Studies from emerging markets reveal more heterogeneous outcomes. Shetty and Yadav (2019) showed that interest rate risk negatively affected Indian bank profitability, while foreign exchange risk had no significant effect. In Bangladesh, Majumder and Li (2018) identified a negative link between risk-taking and performance in commercial banks. Evidence from South Africa by Munangi and Sibindi (2020) confirmed that higher credit risk, particularly non-performing loans, undermined bank profitability. Similarly, Siddique et al. (2022), using the GMM model, found that credit and liquidity risk negatively impact the financial performance of South Asian commercial banks, while lending rates have a significant positive effect. Isedu and Erhabor (2021) found no association between credit risk and bank performance, but highlighted liquidity risk as the most important determinant of bank performance in Nigeria. They provided evidence of a positive and significant relationship between liquidity risk indicators and financial performance. Conversely, Michael and Enang (2022) demonstrated that credit risk positively influences bank performance in Nigeria, as measured by return on equity (ROE).
In non-financial sectors, Offiong et al. (2019) found that exchange rate, liquidity, interest rate, and inflation risks all negatively affected Nigerian SMEs. Mukanzi et al. (2016) reported that business and credit risks reduced stock returns for non-financial firms listed on the Nairobi Securities Exchange, while liquidity risk had a positive effect. Saleem and Rehman (2011) revealed that the liquidity ratio exerts a strong impact on firm performance, as measured by return on assets. They also found that current, quick, and liquidity ratios contribute to improved performance among non-financial firms listed on the Karachi Stock Exchange (KSE). Rahmah and Peter (2024) showed that when Indonesian manufacturing firms balance risk with return on investment, they become more financially resilient and better positioned to meet future financial obligations. More recently, Amugada and Mwangi (2025) found a significant negative impact of both short-term and long-term debt ratios on the performance of manufacturing firms in Kenya.
Recent studies also underscore the role of environmental and regulatory contexts in shaping firm performance. Further evidence suggests that accounting- and market-based performance measures may respond differently to credit and liquidity risks (Harb et al., 2023).
2.3 Research gaps and the Jordanian industrial context
In the Jordanian context, the majority of existing research has focused primarily on the banking sector. Al-Eitan and Bani-Khalid (2019) found a negative and significant effect of credit risk on the performance of commercial banks. Alkhazali et al. (2021) reported an inverse relationship between leverage risk and performance but observed no significant impact of liquidity risk. Despite the growing importance of the industrial sector in Jordan's economic development, empirical investigations into risk–performance relationships in this sector remain limited. Matar and Eneizan (2018) examined the determinants of financial performance in Jordanian industrial firms, noting that liquidity and revenue positively affected performance, whereas leverage had a detrimental effect. However, their study did not specifically analyse financial risk indicators such as credit or liquidity risk.
Recent literature on sector-specific risk management in the MENA region is scarce, especially for the industrial sector, despite its growing economic importance. This omission is particularly noteworthy given the sector's contribution to employment and GDP. Understanding how financial risks affect performance in industrial firms is critical for policy formulation, investment decisions, and managerial strategy.
2.4 Contribution of the present study
This study aims to fill the existing gap by exploring the influence of financial risks—specifically credit and liquidity risk—on the performance of industrial firms listed on the Amman Stock Exchange. Unlike prior research that has primarily focused on banks, this study shifts the focus to the understudied industrial sector.
Both theoretical and empirical gaps justify the application of advanced econometric techniques, such as the two-step Generalized Method of Moments (GMM) estimator and causality tests, to uncover causal links between financial risks and industrial firm performance. Understanding these dynamics is essential for guiding policy actions aimed at improving financial resilience and sustainable performance in emerging economies. Managers may adopt financial strategies to enhance their benefits rather than maximize shareholders' value, which can hinder firm performance. Following this agency conflict, the current study investigates the impact of financial indicators on firm performance.
This study contributes to the existing literature on financial risk and firm performance in several ways. First, it uniquely analyses the impact of financial risk indicators on industrial firm performance using the GMM model in Jordan, thereby addressing endogeneity concerns. Second, the findings reveal that not all financial risk indicators have a causal impact on firm performance. In particular, long-term debt and short-term debt have markedly different effects: short-term debt is associated with improved firm performance. Third, this study uses stock return as the key performance indicator. According to Oxenfeldt (1959), stock return is a valid and suitable proxy for firm performance in stable markets. The Amman stock exchange, in general, and the Jordanian industry sector, in particular, are regarded as among the most stable in the region (Alkhatib and Marji, 2012). To the best of the authors' knowledge, this is the first study to investigate the impact of financial risks on performance in the context of Jordanian industrial firms using simultaneous-equation modelling alongside GMM, as proposed by Arellano and Bover (1995). GMM is employed to address the endogeneity of the data by using lagged values of variables as instruments and controlling for unobserved heterogeneity, thereby ensuring the persistence of the response variable. This model provides consistent parameter estimates (Mokni and Rachdi, 2014; Athanasoglou et al., 2008; Sanga and Aziakpono, 2023; Saleh et al., 2024).
The findings are expected to offer valuable implications for practitioners, investors, and policymakers concerned with risk management and firm valuation in emerging markets.
3. Methodology
3.1 Study method
The existing literature has examined the relationship between financial risk and firm performance using various econometric models across different industries and economies. To the best of the authors' knowledge, however, this is the first study to examine the causal link between financial risks and firm performance among Jordanian industrial firms, using a pairwise Dumitrescu-Hurlin panel causality test. This study is thus expected to make a substantial contribution to the literature on financial risk and firm performance.
A two-step System GMM technique, proposed by Arellano and Bond (1991), is employed to estimate the potential impacts. According to Roberts and Whited (2013), heterogeneity is a common issue in empirical financial studies. Therefore, the first lag value of the dependent variable was included alongside its previous levels. The GMM approach uses a series of instrumental variables generated from the lagged values of explanatory variables to address unobserved heterogeneity and dynamic relationships, as explained in Roodman (2009).
Several advantages of using the GMM estimator have been highlighted in the literature. According to Sheikh et al. (2018) and Nguyen et al. (2015), this approach facilitates meaningful empirical analysis and enhances estimation efficiency. Blundell and Bond (1998) further suggested that GMM is more efficient as it controls for the persistence of unobserved firm-specific effects. According to Ullah et al. (2018), the GMM model also handles endogeneity issues effectively, addressing unobserved heterogeneity, simultaneity, and dynamic endogeneity. To mitigate instrument proliferation in the GMM estimation, we collapsed the instrument matrix, restricted the maximum lag depth to two, and tested robustness using alternative lag structures (Roodman, 2009).
Bandyopadhyay and Barua (2016) emphasized the importance of recognizing the predetermined nature of endogenous variables and ensuring that they are not correlated with other regressors. In this study, model diagnostics included the Arellano Bond autocorrelation test and the Hansen J statistic. The lack of serial correlation in the error terms was verified through preliminary tests, while the Hansen J test assessed the validity and orthogonality of instruments. The estimated model is as follows:
where denotes stock return, the dependent variable for firm i at time t, and denotes its lagged value. The explanatory variables include current ratio (CR), quick ratio (QR), liquid assets to total assets (LA), total loans to total assets (TL), short-term to total assets (ST), and long-term to total assets (LT), for i at time t. Additionally, denotes firm size, which serves a control variable, while α is a constant; β1, β2, β3, β4, β5, β6, β7, and β8 are the coefficients, while is the error term.
To determine the direction of causality among the study variables, we employed the Dumitrescu and Hurlin (2012) panel causality test, which accounts for cross-sectional heterogeneity. The null hypothesis assumes homogenous non-causality across all cross-sections, while the alternative allows for heterogeneous causality in some. The test was conducted on first-differenced data to capture short-term dynamics among variables, as recommended by Hoffmann et al. (2005) and Lopez and Weber (2017). The Schwarz Information Criterion (SIC) was used to select the optimal lag length (K = 2), and the causality regression takes the form:
Where K denotes the lag length, in the balanced panel, denotes the autoregressive parameter, and denotes the regression coefficient, which varies across cross-sections.
3.2 Study variables
The selection of credit and liquidity ratios as financial risk indicators is both theoretically and contextually justified, particularly in emerging economies such as Jordan. These indicators were chosen for their direct relevance to firm solvency and operational continuity, which are central to performance under conditions of capital constraints, high borrowing costs, and limited market liquidity (Al-Eitan and Bani-Khalid, 2019; Harb et al., 2023). Credit risk, often measured by the ratio of non-performing loans or debt ratios, reflects a firm's ability to meet its financial obligations, a crucial factor in capital-intensive industrial sectors. Liquidity risk, assessed using current and quick ratios, captures a firm's ability to manage short-term liabilities—an essential aspect for sectors vulnerable to fluctuations in cash flows and input costs (Tan and Floros, 2018). The variables used in the current study, their abbrivation, and measurement are shown in Table 1.
Variable descriptions
| Variable | Abb | Measurement | Calculation |
|---|---|---|---|
| Stock return | SR | Financial performance | SR=((P1-Pt-1)/Pt-1))*100% |
| Current ratio | CR | Financial risk | Current assets/current liabilities |
| Quick ratio | QR | Financial risk | Quick assets/current liabilities |
| Liquidity ratio | LA | Financial risk | Liquid assets/total assets |
| Credit risk ratios | TL | Credit risk | Total loans/total assets |
| ST | Credit risk | Short-term debt/total assets | |
| LT | Credit risk | Long-term debt/total assets | |
| Total assets | S | Firm size | Natural log of total assets |
In Jordan, industrial firms face structural challenges in financial markets—including limited access to diversified financing and underdeveloped capital markets. As such, these indicators provide a realistic and policy-relevant framework for assessing financial risk (Alabdullah, 2018). To strengthen the robustness of our methodological framework, we acknowledge certain limitations. Although the two-step GMM estimator is suitable for dynamic panel data and for controlling endogeneity and unobserved heterogeneity (Arellano and Bover, 1995), it is sensitive to instrument proliferation and can be biased in small samples (Roodman, 2009). To address this, we implement robustness checks by limiting the number of instruments, exploring alternative model specifications, and employing the Hansen and Arellano-Bond tests. This multi-pronged approach ensures that our empirical strategy remains methodologically sound while adequately addressing the complexities of the research context.
3.3 Data collection and sampling
This study's multivariate structure encapsulates liquidity risk, credit risk, stock returns, and control variables. All industrial firms listed on the Amman Stock Exchange between 2009 and 2022 were included in the initial data sample, with certain firms excluded as necessary. In particular, some companies did not provide year-end financial statements during the sample period and were thus removed from the dataset. Consequently, the final sample comprised 37 firms with balanced panel data covering more than a decade. We focus on the performance of Jordanian industrial firms for several reasons. First, this sector represents one of the main contributors to employment and overall economic development (Matar and Eneizan, 2018; Shibia, 2023; Al-Hazaima et al., 2025). Second, this sector plays a significant role in economic growth by adding significant value to the national income in Jordan. According to the Department of Statistics (2025) GDP grew by 2.7% in the fourth quarter of 2024 compared to the same period in 2023. In terms of sectoral contributions, the industrial sector recorded the highest share (18.7%) of this growth among key sectors of the Jordanian economy. Third, Jordan plays a strategic role as a gateway to major Middle Eastern markets, and the Amman Stock Exchange (ASE) is considered one of the largest and most active stock exchanges in the region (Alabdullah, 2018). We selected a sample period exceeding ten years to minimize any downward bias in the estimation of the two-step GMM analysis that can occur when using small samples (Blundell and Bond, 1998).
4. Results and discussions
A summary of the descriptive statistics for the variables under investigation is presented in Table 2. Stock market return (SR), used as the firm's performance indicator, captures both monetary and market-based dimensions. The maximum observed SR value was 14.54. The mean values of the financial risk indicators are as follows: CR (2.70), QR (1.79), and LA (0.23). These figures indicate that the industrial firms studied maintain sufficient liquidity to cover current liabilities. For credit risk indicators, the mean values are: TL (0.06), ST (0.27), and LT (0.05), suggesting that the firms finance part of their assets through debt, with manageable repayment obligations. These firms exhibit relatively low credit risk levels, although there is some variation in their asset management practices.
Descriptive statistics
| Variable | Mean | Maximum | Minimum | Std. Dev | Observations |
|---|---|---|---|---|---|
| SR | −0.14771 | 14.54643 | −16.6667 | 3.63668 | 370 |
| CR | 2.705,845 | 20.13765 | 0.008244 | 2.298991 | 370 |
| QR | 1.797686 | 13.02683 | 0.008244 | 1.736485 | 370 |
| LA | 0.232953 | 0.818729 | −0.53577 | 0.229197 | 370 |
| TL | 0.060317 | 0.540042 | 0 | 0.101674 | 370 |
| ST | 0.272585 | 0.998157 | 0.014199 | 0.149087 | 370 |
| LT | 0.058633 | 0.730458 | 0 | 0.114172 | 370 |
| S | 7.460384 | 9.087522 | 6.563347 | 0.546754 | 370 |
The Pearson correlation matrix (Table 3) reveals no multicollinearity concerns among the explanatory variables, with the highest correlation observed between CR and ST (−0.67), followed by CR and LA (0.59). The SR variable exhibits only a weak correlation with other explanatory variables, such as 0.047 with CR.
Correlation coefficients
| SR | CR | QR | LA | TL | ST | LT | S | |
|---|---|---|---|---|---|---|---|---|
| SR | 1 | |||||||
| CR | 0.046911 | 1 | ||||||
| QR | 0.043841 | 0.654276 | 1 | |||||
| LA | 0.082943 | 0.597518 | 0.539212 | 1 | ||||
| TL | −0.01973 | −0.28643 | −0.2606 | −0.30361 | 1 | |||
| ST | −0.02446 | −0.67882 | −0.64749 | −0.54403 | 0.383676 | 1 | ||
| LT | 0.012786 | −0.21096 | −0.18197 | −0.23039 | 0.401651 | 0.048844 | 1 | |
| S | −0.03479 | −0.00703 | 0.062794 | −0.10536 | 0.017999 | −0.05953 | 0.238032 | 1 |
The results of unit root tests (Table 4) confirm that all variables are non-stationary at levels but become stationary after first differencing. All series exhibit first-order integration, with all p-values below the 5% significance level, leading to the rejection of the null hypothesis of non-stationarity. These results suggest long-run stability in the tested relationships.
Unit root tests
| Variable | ADF | PP | Result | |
|---|---|---|---|---|
| SR | Level | 134.624*** | 252.199*** | Non-stationary |
| First difference | 189.660*** | 434.925*** | Stationary | |
| CR | Level | 119.858*** | 150.994*** | Non-stationary |
| First difference | 170.324*** | 281.414*** | Stationary | |
| QR | Level | 115.138** | 166.955*** | Non-stationary |
| First difference | 168.861*** | 395.425*** | Stationary | |
| LA | Level | 100.568* | 93.6533* | Non-stationary |
| First difference | 131.359*** | 200.319*** | Stationary | |
| TL | Level | 73.5732* | 73.2694 | Non-stationary |
| First difference | 136.763*** | 183.032*** | Stationary | |
| ST | Level | 90.6325* | 108.615** | Non-stationary |
| First difference | 141.870*** | 257.450*** | Stationary | |
| LT | Level | 75.5635** | 63.6597 | Non-stationary |
| First difference | 84.5650*** | 169.642*** | Stationary | |
| S | Level | 81.0790 | 96.5835* | Non-stationary |
| First difference | 115.863** | 213.287*** | Stationary |
The correlation matrix, based on the Pearson correlation coefficients, illustrates the linear associations between the financial risk indicators and firm performance variables. The results show moderate correlations and provide no evidence of multicollinearity, supporting the inclusion of these variables in the subsequent regression models. This preliminary analysis confirms the theoretical expectations regarding the direction of relationships, which are further examined through the two-step GMM estimation.
To justify model selection, chi-square-based diagnostics, including the Hansen J test for overidentifying restrictions, were applied. The Hansen J statistic yielded a p-value of 0.47, indicating no evidence against the validity of the instruments, and confirming the model's appropriateness. These findings, along with the Arellano-Bond autocorrelation tests, confirm the robustness of the selected dynamic panel model for analyzing the causal effects between financial risk and firm performance.
The two-step GMM estimates, resulting from the investigation into the relationships between firm performance and financial risk indicators, are presented in Table 5. The results show that the CR coefficient is statistically significant and has a positive effect on financial performance among industrial firms, as measured by stock market return. These findings further suggest that a 1% increase in CR for the prior fiscal year is likely to result in a 3.40% increase in SR, with all figures significant at the 1% level. This finding is broadly consistent with previous empirical studies (e.g. Saleem and Rehman, 2011; Samo and Murad, 2019; Michael and Enang, 2022; Mushafiq et al., 2023).
GMM estimator results
| Variable | Coefficient | t-Statistic | Prob |
|---|---|---|---|
| SR | −0.049311 | −1.543006 | 0.1241 |
| CR | 3.406176 | 4.914898 | 0.0001 |
| QR | −1.886009 | −1.571112 | 0.1174 |
| LA | −1.358434 | −0.262688 | 0.793 |
| TL | 28.82881 | 3.466117 | 0.0006 |
| ST | 14.21755 | 3.978749 | 0.0001 |
| LT | −25.97691 | −3.146872 | 0.0018 |
| S | −16.95755 | −3.289846 | 0.0011 |
| Hansen J p-value 0.474412 | |||
| Arellano-Bond Serial Correlation Test | |||
| Test order | m-Statistic | rho | SE(rho) |
|---|---|---|---|
| AR(1) | 0.09718 | 3936.39 | 40,504.78 |
| AR(2) | 0.001341 | 172.926 | 128,926.4 |
TL has a positive and statistically significant impact on a company's market performance. A 1% increase in TL during the previous fiscal year has a marginal effect on the current share price, resulting in an estimated 28.82% increase. This finding is consistent with previous studies showing that a higher lending rate enhances firm performance (e.g. Siddique et al., 2022; Tailab, 2014). Additionally, the ST variable has a positive impact on a company's market performance, with each percentage increase in ST in the prior fiscal year associated with a 14.21% rise in the current share price. This result supports the findings of Yazdanfar and Öhman (2015), who argue that firms tend to rely more on short-term debt than on long-term debt due to its lower cost and greater flexibility.
With respect to long-term debt, LT exhibits a negative and statistically significant effect on market performance. Specifically, a 1% increase in LT in the previous fiscal year is associated with a 25.97% decline in the current share price. This finding aligns with several previous studies reporting a negative and significant relationship between long-term debt and firm performance, suggesting that excessive long-term debt can hinder firms' profitability and growth (e.g. Arhinful and Radmehr, 2023; Yazdanfar and Öhman, 2015; Amugada and Mwangi, 2025).
Moreover, the regression results for the control variable effects reveal that the firm size coefficient is significant at the 1% level, indicating that company size negatively affects stock market returns. This finding is consistent with Taliab's (2014) results, which reported a significant negative relationship between firm size and firm performance. Similarly, Arhinful and Radmehr (2023) also found a negative relationship between firm size and performance when measured using ROE.
Table 5 presents the results of the two diagnostic tests along with the GMM estimators. The p-values obtained from the Arellano-Bond tests are 0.097 for AR (1) and 0.0012 for AR (2), both of which exceed the 0.10 threshold. These results indicate that the null hypothesis of no serial correlation in the residuals cannot be rejected, thereby supporting the validity of the model. Similarly, the Hansen J test for overidentifying restrictions yielded a p-value of 0.47, which is well above the 0.10 significance level. This result confirms the validity of the instruments used and reinforces the appropriateness of the GMM estimation.
To assess causality among the study variables, the Dumitrescu and Hurlin (DH) (2012) panel causality test was employed, accounting for cross-sectional heterogeneity. The null hypothesis (H0) posits the absence of homogeneous causation across cross-sections. This test, aimed at capturing short-term dynamics, was applied using the first-differenced series of lagged data, as recommended by Blundell and Bond (1998). The Schwarz Information Criterion (SIC) was used to determine the optimal lag length, which was set at two. Both the W-statistic and Zbar-statistic were interpreted in accordance with Dumitrescu and Hurlin's methodology (2012).
Table 6 presents the DH panel causality results, which reveal a bidirectional causal relationship between the performance of industrial firms and financial risk indicators. Specifically, a homogeneous causal relationship exists between CR and SR, with p-values of 0.0018 and 0.029, respectively, both below the 5% threshold. These results confirm a statistically significant bidirectional causal link between CR and SR. In contrast, the results indicate that QR does not cause SR, nor does SR influence QR, as the corresponding p-values exceed 0.10. Therefore, the null hypothesis of no homogeneous causality between these variables cannot be rejected. Similarly, LA neither causes nor is caused by SR, given that their p-values also exceed the 10% level, supporting the absence of a causal relationship. However, the analysis reveals significant bi-directional causality between SR and the following variables: ST, LT, and firm size.
Pairwise dumitrescu Hurlin panel causality tests
| Null hypothesis: Direction of causality | W-Stat | Zbar-Stat | Prob |
|---|---|---|---|
| CR does not homogeneously cause SR | 2.53 | 6.43476 | 0.0018 |
| SR does not homogeneously cause CR 3.70 | 3.57989 | 0.0291 | |
| QR does not homogeneously cause SR | 2.89 | 0.11311 | 0.8931 |
| SR does not homogeneously cause QR 2.50 | 0.44519 | 0.6411 | |
| LA does not homogeneously cause SR | 3.31 | 0.70776 | 0.4936 |
| SR does not homogeneously cause LA 4.68 | 0.04267 | 0.9582 | |
| TL does not homogeneously cause SR | 2.60 | 6.82219 | 0.0013 |
| SR does not homogeneously cause TL 4.10 | 4.36236 | 0.0136 | |
| ST does not homogeneously cause SR | 3.80 | 8.0521 | 0.0004 |
| SR does not homogeneously cause ST 2.73 | 3.08171 | 0.0474 | |
| TL does not homogeneously cause SR | 3.02 | 6.62009 | 0.0015 |
| SR does not homogeneously cause TL 2.86 | 3.77472 | 0.0241 | |
| SIZE does not homogeneously cause SR | 2.68 | 4.36236 | 0.0136 |
| SR does not homogeneously cause SIZE 3.27 | 2.9395 | 0.0545 | |
These findings carry important implications for policymakers and industrial firms in Jordan. Policymakers can enhance financial oversight by developing early warning systems to monitor excessive exposure to long-term debt and by promoting short-term financing strategies that support improved market performance, such as liquidity-oriented credit facilities. Regulatory reforms should also mandate stricter financial disclosure requirements to enhance transparency regarding firms' risk exposures. For industrial firms, the observed positive effects of credit risk and short-term debt suggest prioritizing effective working capital management and maintaining an optimal capital structure that avoids excessive reliance on long-term debt. These practices can help strengthen investor confidence and enhance firm valuations. Theoretically, this study contributes to the financial literature by grounding its analysis in agency theory and trade-off theory. Further attention should be given to how credit risk levels and debt maturity structures influence corporate financing decisions. Establishing the causal linkage between financial risk indicators and firm performance, particularly in emerging markets, provides valuable insights for executives, investors, and regulators. Notably, the identified negative impact of long-term debt on firm performance highlights the importance of strategic financial decision-making and proactive risk assessment.
5. Conclusion
This empirical study investigated the bidirectional causal relationship between financial risk and the performance of industrial firms listed on the Amman Stock Exchange. Firm performance was measured using stock market returns, while firm size was included as a control variable. Theoretical arguments and empirical evidence were integrated to establish grounded expectations, and a two-step System GMM estimation was employed to assess the direct impact of financial risk on firm performance. In addition, the pairwise Dumitrescu Hurlin panel causality test was used to examine reverse and reciprocal causality among the variables for industrial firms on the ASE during the period 2009 to 2019.
According to the empirical results, a positive relationship exists between CR and financial performance for firms listed on the ASE, while LT and S negatively affect SR. Conversely, TL and ST positively impact SR. Overall, the findings support the presence of homogeneous causality between financial risk and stock returns for industrial firms in Jordan.
This study, therefore, recommends that future studies extend this analysis by including moderating variables, such as macroeconomic factors, within the SR-performance relationship, based on the premise that a country's economic conditions influence firm-level factors (e.g. productivity) and corporate decision-making. The present study contributes to the ongoing debate on the relationship between financial indicators and firm performance. It provides valuable insights by demonstrating that higher financial risk and credit risk can be associated with better performance among manufacturing firms. While the use of short-term debt enhances performance, higher levels of long-term debt exert a negative and statistically significant effect on firm performance.
Future research should further develop this framework by incorporating macroeconomic variables such as interest rates, inflation, and exchange rate volatility to capture broader economic implications. Furthermore, comparative studies across sectors or regions can help generalize the findings and provide deeper insights into how financial risks affect firm performance in different contexts. Future empirical work could also differentiate between large and small manufacturing firms or compare financial and nonfinancial sectors. If notable differences in outcomes and behaviours emerge, they would carry important policy and regulatory implications. One limitation of this study is that several firms did not provide sufficient disclosures to calculate the required independent variables; consequently, these firms were excluded, reducing the effective sample size. Future studies should consider a longer time horizon and conduct cross-country comparative analyses to examine the impact of financial indicators on firm performance in greater depth.
