Determinants of Financial Fragility in Jordanian Non-Financial Firms: Empirical Evidence Based on the Financial Instability Hypothesis

Abstract

Financial fragility among non-financial corporations (NFCs) has become a critical concern in developing economies, where both firm-specific and macroeconomic conditions shape corporate financial stability. Understanding these dynamics is essential to enhancing corporate resilience and informing effective regulatory interventions. This study is motivated by Minsky’s Financial Instability Hypothesis (FIH), to empirically investigate the determinants of financial fragility in Jordanian non-financial firms (NFCs) using panel data from 71 companies listed on the Amman Stock Exchange (ASE) between 2015 and 2021. By employing a panel logistic regression analysis, results reveal that Return on Assets (ROA) significantly supports financial stability, while inflation negatively impacts it, underlining the detrimental impact of increasing inflation rates on corporate financial health. The beneficial effects of GDP growth and institutional quality also emphasize how important governance and economic conditions are in promoting financial stability. The study offers an original insight on the dynamics of financial fragility in a developing market, with important ramifications for regulators, business managers, and policymakers looking to boost institutional quality, control inflation, and increase corporate profitability. The findings extend Minsky’s hypothesis to a developing-market context and provide implications for policymakers seeking to strengthen institutional frameworks, contain inflationary pressures, and promote corporate financial stability.

1. Introduction

The Global Financial Crisis (GFC) has severe consequences for companies worldwide, illustrating the catastrophic implications of financial instability. This new era of financial volatility was met with an echoing response to this heightened demand for improved financial management practices (Farag & Mallin, 2017). The terms financial fragility, financial distress, insolvency, default, failure, and bankruptcy are occasionally used synonymously, though with very subtle differences in their meanings (Alaka et al., 2018). Investigating the causes of financial distress has been a priority in finance research dating back to the work of E. I. Altman (1968), when he derived a model using a group of financial ratios for the prediction of bankruptcy of companies, which is thereafter known as the Altman model or the Z-score model.

This was followed by other new models, the most notable of which was the distance for the standard model (1974), which was the most important.

The study will utilize Nishi’s (2019) Financial Fragility Index (FFI), a newly developed measurement of financial crisis based on Minsky’s (1992) Financial Instability Hypothesis (FIH). This leading work from (Minsky, 1992) provides a new process structure to understand economically fragile events, leading to volatility and a receptive state that may be exposed to destabilizing shocks.

According to (Schroeder, 2009), there are two classifications for financial fragility within Minsky’s (1992) framework, which considers financial fragility either as a “process that may result in financial instability” or, more generally, as “a condition where a shock could cause instability”. In turn, Minsky (1992) pointed out that the phenomenon of financial fragility is more likely to appear in capitalist economies, because it pushes towards relatively longer optimistic time periods, which leads to a higher degree of risk, which will lead to the occurrence of financial crises. However, Minsky’s Financial Instability Hypothesis (FIH) has been tested in relatively limited empirical studies, particularly in cases of assessing the financial fragility of non-financial companies, in addition to some studies conducted on private sector companies in the Spanish and Dutch environment, to classify the companies studied based on their financial stability, which leads to obtaining an effective vision of how financial fragility develops amid economic and political changes (Rozmainsky et al., 2022a; Bryleva & Rozmainsky, 2022).

Studies have started to identify the factors that predict financial distress in the Middle East and North Africa (MENA) area, and the results show unique patterns that differ from those seen in industrialized nations. This research demonstrates the necessity for region-specific assessments, highlighting how factors such as firm lifespan, profitability, and external economic and political conditions influence financial distress. This background provides references to the current study, which uses Nishi’s operational FFI, based on Minsky’s FIH, to investigate factors that contribute to financial fragility between NFC in Jordan. Given the remarkable difficulties that the COVID-19 epidemic has brought since 2020, the study is particularly relevant, as it has further emphasized the importance of understanding financial fragility. The study is to check the knowledge body on economic management practices in the region by examining macroeconomic and corporate -specific factors affecting NFC’s economic fragility in Jordan. This will help to highlight the economic stability of NFC.

The study employs logistic regression and Nishi’s Financial Fragility Index (FFI) to more effectively assess financial fragility in Jordan’s non-financial enterprises. Initially, it provides an intensive evaluation of the company’s financial health by classifying it in Minsky’s Ponzi, speculative or hedge states. It also provides an empirical understanding of factors that contribute to financial fragility.

Additionally, E. I. Altman et al. (2017) argue that although a generic international model achieves a respectable level of classification accuracy, country-specific predictions could further enhance this accuracy, particularly by incorporating straightforward additional factors. To strengthen economic resilience and reduce financial vulnerability, the study’s conclusions provide policy implications and recommendations for decision-makers and financial managers.

The remainder of this study is as follows: In Section 2, we conduct a review of previous empirical investigations related to the determinants of financial fragility. Section 3 provides an overview of the research methodology and the data utilized in this study. Section 4 presents a comprehensive discussion of the findings. Section 5 clarifying some policy implications and recommendations. Section 6 provides the research limitations and suggest ideas for related future research. Finally, Section 7 concludes this study.

2. Literature Review

2.1. Background

In recent decades, the multifaceted issue of financial fragility in enterprises has increasingly captured the attention of researchers. Hyman Minsky’s Financial Instability Hypothesis has remained the key explanation here (Michail, 2021). In scientific studies of this subject, the mechanism of the formation of financial vulnerability to bankruptcy no-wonder but as an accumulation of financial instability (J. Davis, 2020; Rozmainsky & Rodionova, 2021). Various studies showing the progressive structure of financial distress have found traction with Minsky’s theory, which considers bankruptcy as the final stage for companies.

Numerous studies highlighting the progressive structure of financial distress have found resonance with Minsky’s theory, which sees bankruptcy as the terminal phase of a continuum of financial distress. According to E. Altman and Hotchkiss (2005), bankruptcy is the final stage of a financial hardship continuum and a point of no return following a string of intricate financial difficulties, as noted by E. Altman and Hotchkiss (2005). This viewpoint is consistent with their argument. Furthermore, Volkov et al. (2017) note that premature identification of financial vulnerability is made more difficult by the reality that the symptoms of financial distress are not apparent until a statutory announcement of bankruptcy. Following up on these theoretical foundation research, significant efforts have been put into building and refining models that can predict and comprehend financial instability using financial ratios and other statistical methods (Yazdanfar & Öhman, 2020).

In recent decades, the issue of business financial fragility has become increasingly a focus of interest for scholars. Hyman Minsky’s Financial Instability Hypothesis (Michail, 2021) remains the most popular theory in this field. In this field of research, the development of financial vulnerability is examined, which results in bankruptcy—not as an acute process, but as a natural outcome of growing financial instability (J. Davis, 2020; Rozmainsky & Rodionova, 2021).

Most analyses highlighting the progressive aspect of financial distress have reflected Minsky’s hypothesis, which views bankruptcy as the final step for companies that remain insolvent. For E. Altman and Hotchkiss (2005), bankruptcy is the final stage of a continuum of financial distress, marking a point of no return from a chain of complex financial woes. This interpretation is consistent with their argument. Additionally, Volkov et al. (2017) note that pre-empting financial fragility is made more challenging by the fact that symptoms of financial distress often remain hidden until the announcement of a formal bankruptcy.

After these theoretical studies, efforts have been intensive in the development and sophistication of models that possess the potential to grasp and predict financial instability by applying financial ratios and other statistical methods (Yazdanfar & Öhman, 2020).

2.2. The Financial Instability Hypothesis

Following the Global Financial Crisis (GFC), Hyman P. Minsky’s Financial Instability Hypothesis (FIH), which is often hailed as prophetic in predicting such economic upheavals, gained prominence. By referencing Keynes’s landmark work, “The General Theory of Employment, Interest, and Money,” Minsky’s FIH departs from neoclassical economic theories and provides a critique specific to the workings of capitalist economies. It was first proposed in 1977. Minsky points out that financial systems are inherently vulnerable, which is exacerbated by debt buildup and further complicated in open economies by external variables such as currency fluctuations and maturity mismatches. This perspective situates financial fragility within a cycle where current profits and asset quality—reflective of past investments—feed into the system’s overall stability (Perepelkina & Rozmainsky, 2023; Arestis & Glickman, 2002; Sinapi, 2013).

The basic idea of Minsky’s concept is that “stability breeds instability,” which causes a vicious cycle of greater leverage and speculative borrowing that accelerates the transition from stability to instability. He maps the path from stability to possible crisis by defining three financial regimes—speculative finance, hedge finance, and Ponzi finance—each based on its capacity to meet debt commitments (L. E. Davis et al., 2019; González & Pérez-Caldentey, 2018).

These conceptual ideas became quantifiable indices, indicating the varying levels of financial fragility as a result of Minsky’s FIH. Academics have specifically looked into debt levels, debt-to-income ratios, and speculative characteristics of financial activity. Several financial fragility indices (FFIs) have been developed to provide measurements of levels of stability or levels of vulnerability throughout firms and economies (Tymoigne, 2014; Vercelli, 2011; Perepelkina & Rozmainsky, 2023).

Several FFIs have been generated using different methods, capturing Minskyan ideas. Mulligan (2013), for example, examined companies using Minsky’s three financial categories, as defined by the Interest Coverage Ratio (ICR), which provides information about the firm’s ability to sustain debt. In the same vein, Torres Filho et al. (2019) employed a cash-flow accounting approach and categorized Brazilian energy distribution firms, which also showed a rise in financial fragility from 2008 through 2013. L. E. Davis et al. (2019) did a study on the cash inflows minus debt commitments of US non-financial firms (NFC’s). Nishi (2019) constructed an FFI that used Minsky’s safety margin principles in his 2019 paper.

He subsequently moved to do a very exhaustive evaluation of the factors at play in creating financial instability affecting Japanese non-financial corporations (NFC’s) with a focus on differences between industries and sizes of businesses. The outcomes of the analysis showed that speculative financing dominated across a wide array of industries and sizes, but that hedge and Ponzi finance patterns and determinants diverged greatly. The FFI is a well-known resource for checking financial stability because of Nishi’s construction method which is highly regarded and tries to stay faithful to Minsky’s theoretical assumptions (Perepelkina & Rozmainsky, 2023; Rozmainsky et al., 2022b).

To validate the theory, Minsky’s FIH empirical study has mainly focused on reviewing past and present instances of financial crises. The Global Financial Crisis was a clear example of Minsky’s predictions that a crisis can be triggered by increasing leverage and speculative banking practices, as evidenced by its labelling as a “Minsky moment”. The predictive potential of Minsky’s framework was demonstrated through the application of FIH in numerous scenarios, specifically, but not exclusively, in the Brazilian energy sector, Japanese firms, and North American sectors. The research demonstrates not only the cyclical nature of financial stability and fragility but also how the various economic actors navigate these cycles, as well as emphasizing the nuances between the interplay of macroeconomic vulnerability and speculative practices (Nikolaidi et al., 2021; Fuller, 2014; Guttmann & Plihon, 2010).

2.3. Empirical Evidence on the Determinants of Financial Fragility

Financial fragility is influenced by a variety of interacting factors, including macroeconomic conditions, industry-specific weaknesses, corporate governance structures and stability measures, as well as political factors. A comprehensive review of the determinants of financial distress conducted by Habib et al. (2020), uses three categories of factors: corporate governance indicators, macroeconomic factors, and firm fundamentals.

Empirical evidence from multiple studies emphasizes that financial fragility is complex and requires jurisdiction-specific, holistic policy approaches that recognize these complexities. Policymakers, regulators, and industry stakeholders need to consider these aspects, and develop actions that provide certainty to the financial system and robustness to potential internal or external shocks in order to enhance financial stability and reduce the chance of financial fragility.

2.3.1. Empirical Evidence from Developed Countries

Minsky’s Financial Instability Hypothesis (FIH) has been investigated in numerous international situations. The theoretical applicability of FIH is explicitly illustrated through several works from Perepelkina and Rozmainsky (2023) in Russia, to Bibi et al. (2023) in Mexico. Other works suggest the applicability of FIH in Asian contexts (e.g., Azad, 2023, India; Nishi, 2019, Japan; Can & Canöz, 2020, Turkey). The hypothesis has also been investigated in European contexts, as seen in studies by Rozmainsky and Rodionova (2021) in Italy, Rozmainsky et al. (2022a) in the Netherlands, Rozmainsky et al. (2022b) in France, and Bryleva and Rozmainsky (2022) in Spain. Taken together, these bodies of research emphasize how ostensibly safe times can promote reckless behaviour that, instead of advancing stability, seeks amounts of illusory security that magnifies risky behaviour and increases the potential for financial instability.

The interest behind these studies has made an extremely good impression of financial fragility and a general understanding of financial instability in various economic structures by assessing vulnerabilities within various economic sectors and how broader macroeconomic factors work alongside those sectors, and ultimately, the financial systems of those businesses which have augmented the merit of these financial fragility studies and the determinants associated with them.

There are several studies completed on different economic sectors in a number of countries supporting Hyman Minsky’s financial instability hypothesis (FIH) including, for an example, Bryleva and Rozmainsky (2022), which addressed private non-financials in Spain, Perepelkina and Rozmainsky (2023) on private non-financial in Russia, and Rozmainsky et al. (2022a) on private non-financial sector in the Netherlands. These analyses reveal the intricate dynamics of financial soundness and financial fragility within each nation based on the financial fragility indexes, and categorization of firms by hedging, speculative, and Ponzi regimes. The results from Spain, Russia, and the Netherlands illustrate how context- and industry-specific vulnerabilities interact with macro-prudential and external economic policy to influence the financial regime in which firms operate. Bryleva and Rozmainsky (2022) examine the financial fragility of Spain non-financial private enterprises from 2011 to 2017 through the lens of Minsky’s Financial Instability Hypothesis (FIH) framework. Their article provides a nuanced and detailed analysis of the transition to financing under higher levels of reliability post-financial crisis. Exploring the cases of 163 different Spanish firms, their article categorizes firms in hedge, speculative, and Ponzi schemes. While financial austerity was prevalent, firms were evidently gravitating toward their lower financial fragility categories; this was attributed to government action in promoting recovery, tourism, and diversifying exports. Their work revealed not just the cross-sectional evidence of firm responses to differing macro-prudential policy and external economic influences, but the confluence of government policy, economic stability, and firm financial strategies.

Likewise, Perepelkina and Rozmainsky’s (2023) study presents a thoughtful empirical examination of Russian firms through the Financial Instability Hypothesis (FIH) across several industries. From their study, macroeconomic determinants such as GDP growth and profitability were found to dramatically alter the financial regimes of firms and can influence whether a firm pivots toward or away from speculative and Ponzi financing.

Bibi et al. (2023) showed how the onset of COVID-19, as described in their case study of Mexican firms, exposed financial vulnerabilities that existed prior to the pandemic. They also demonstrated that financial arrangements in certain sectors made them susceptible to financial fragility and the macroeconomic shocks they faced, causing some industries to have financial arrangements that further increased their susceptibility to financial fragility. Their findings presented sector-specific patterns of financial stability and instability. The studies show significant advances in understanding financial fragility by highlighting the predictive power of FIH for future financial instability as well as future economic policies designed to avert future financial crises.

2.3.2. Empirical Evidence from Developing Countries

Examining the MENA region, ElBannan (2021) focuses on the impact of the company life cycle and the Arab Spring, and investigates predictors of financial distress in 11 MENA countries between 2006 and 2015. He contends that companies that are small, established, and profitable, with slow asset growth and higher market-to-book ratios, are less financially distressed. Other variables that lowered financial distress were strong financial markets and low corruption. Furthermore, the study examines the impact of the company life cycle stages on financial well-being and proposes methods for mitigating risk, such as enhancing cash holdings and earned capital. The findings highlight the importance of robust financial markets and low corruption in reducing financial distress. Moosa and Khatatbeh (2023) argue that policymakers should conduct a more comprehensive review of the challenges and issues associated with economic diversification in the Middle East and North Africa (MENA) region. Diab et al. (2023) examine the associations among political risk, corporate governance, and bank stability in the MENA region, revealing how post-Arab Spring financial stability benefits from political stability and the positive impact of political stability on financial stability.

According to Moosa et al. (2023), banks may manipulate financial statements to temporarily improve their financial outlook. This makes it more difficult to determine a bank’s actual level of financial soundness from end-of-period financial reports. According to the literature, proxies for financial stability are just as pertinent when examining the factors that contribute to banks’ financial fragility. This emphasizes how the dangers facing the banking industry are receiving more attention from academics, especially in the wake of the global financial crisis (Qi et al., 2023; Al-Habashneh et al., 2023). Qi et al. (2023) identified three key topics: the relationships between corporate governance and bank risk, adopting risk management strategies, and assessing bank risk during financial crises. This study aims to enhance our understanding of the factors that contribute to financial fragility in banks by recommending future research directions for risk management strategies that support financial stability and resilience. Khatatbeh et al. (2024) provide direct empirical support for Minsky’s FIH by applying Nishi’s Financial Fragility Index to Jordanian non-financial firms. Their findings reveal a high prevalence of financially vulnerable “Ponzi” firms, with over half the sample consistently classified as such from 2015 to 2021. This fragility intensified during periods of economic pressure, demonstrating a cyclical pattern. Sector-specific analysis further validated the hypothesis, showing non-manufacturing industries like tourism were disproportionately fragile. This study confirms the FIH’s relevance in the Jordanian emerging market, illustrating the dynamic buildup of financial fragility.

In their 2013 study, Alareeni and Branson (2013) examine Altman’s Z-Score models to assess their ability to predict company failures in Jordan, with a specific focus on industries and services, over a sample period spanning from 1968 to 1993. The models were originally developed for the US and other developed economies, and the study examines their effectiveness in the context of Jordan’s particular economic environment. Overall, it was concluded that the original Z-Score was effective with industrial businesses but only moderately predictive with service firms. Al-Ramli and Taj-Addin (2023) analyzed banks listed on the Abu Dhabi and Dubai stock exchanges and found that increased financial fragility, measured by lower Z-scores, was closely associated with deteriorating capital adequacy and profitability—confirming Minsky’s prediction that prolonged stability leads to greater vulnerability. Elhaj (2021) has recently investigated the potential to predict business failures in Jordanian businesses, with a focus on internal causes. In his analysis he uses multi-discriminant analysis (MDA) and qualitative analysis to investigate financial information from 49 companies from 2015 to 2017. In his study, debt, equity capital, retained earnings, and sales emerged as the most significant predictors of business defaults. This finding is important to identify the underlying causes of business failures, and highlights the importance of keeping an eye on financial indicators to avoid possible business distress and failure.

3. Data and Methodology

3.1. Sample and Data

The authors have a complete panel of non-financial corporations (NFCs) that were listed on the Amman Stock Exchange (ASE) between 2015 and 2021, which comprises the research data. Data was sourced from annual reports and the ASE website. The final sample, constrained by data availability, comprises panel data for 71 NFCs representing diverse industries over the period from 2015 to 2021, resulting in a total of 497 observations.

Table 1. The Number of Sample NFCs across Various Industries.

Industry	Number of NFCs
Pharmaceutical and Medical Industries	4
Food and Beverages	7
Technology and Communication	2
Commercial Services	9
Educational Services	6
Health Care Services	2
Mining and Extraction Industries	8
Electrical Industries	2
Chemical Industries	5
Engineering and Construction	5
Utilities and Energy	4
Hotels and Tourism	8
Transportation	8
Textiles, Leathers, and Clothing	1
Total	71

Source: Authors own creation.

shows the sectoral dispersion of the full sample.

A comprehensive panel of non-financial corporations (NFCs) listed on the Amman Stock Exchange (ASE) between 2015 and 2021 is included in the research data. The data was acquired from annual reports on the ASE webpage. The final sample in a panel data set consisted of 71 different NFCs from various sectors, with 497 observations; however, data constraints limited the observation count. Table 1 displays the sectoral distribution of the total sample.

3.2. Variables and Measurement

This study focuses on the causes of financial fragility in Jordanian non-financial firms. The researchers used the Financial Fragility Index (FFI) devised by Nishi (2019), who operationalized it by using a methodological framework based on Minsky’s (1992) financial instability hypothesis. In Minsky’s theory, Nishi’s methodology evaluated financial instability in terms of the firm’s financial statements and the flow of cash, i.e., if we consider our activities as “uses” representing capital expenditures, interest on loans, or dividends, we will recognize that those “uses” also have “sources” that reflect earnings from operations or borrowing. Nishi classified companies into three distinct categories of financial fragility, ranked from the least stable (Ponzi) to the most stable (hedged) financial firms.

This study identifies the causes of financial fragility experienced by Jordanian non-financial firms. In order to do this, a Financial Fragility Index (FFI) will be applied, which refer to the methodological problem that Nishi (2019) developed from Minsky’s (1992) theory of financial instability. Nishi developed a method to measure financial instability by incorporating a balance sheet concept and a cash flow accounting approach, which aligns with Minsky’s theory. The sources of cash from Nishi’s method were earnings and borrowings, and the uses of cash were dividends paid, interest on loans, and capital expenditures. Nishi then defined three types of organizations and groups according to their financial fragility, the most stable of which is the hedge, and the least stable of which is the Ponzi.

In Minsky’s analytical framework, companies are assigned a position along a continuum, ranging from the most stable to the most fragile, based on their degree of reliance on borrowing or, more broadly, financial instability as defined by Minsky. Stable hedge firms have operating profits that exceed the total of their new investment, debt service, and dividends. Their operating profits exceed the amount necessary to meet the company’s financial obligations, including the payment of dividends, without going further into debt. This allows the company to pay down its debt and liabilities, while being in the most secure financial position. A speculative firm is one that has enough operating revenues to pay dividends and debt service, but they do not generate enough revenues to pay for both new investment capital and its other financial obligations; thus, to continue operating, the speculative firm is obliged to borrow more.

Ponzi companies are located at the most vulnerable point on the spectrum; they produce revenues that barely cover debt service and dividends, and they must borrow more money to pay off the initial loans and dividends, as well as to move forward with new businesses. This demonstrates a vulnerable financial position that relies on constant borrowing. Using a certain framework, Nishi (2019) categorizes corporations according to their financial vulnerability using the Financial Fragility Index (FFI). Companies are categorized into three categories—hedge, speculative, and Ponzi.

Financial data including operating profit (r), investments (g), interest payments (i), and cash dividends (D) are used to make these classifications. In addition, the calculated variable (d) for changes in retained profits is also used. A firm is determined to be a Ponzi scheme (FFI = 0) if its operating profit less the cash dividends, interest payments, and investments (r − g − i − D) and the operating profit less interest payments and cash dividends (r − i − D) are both less than zero. A firm is classified as Speculative (FFI = 1) if its operational profit is less than cash dividends, interest payments, and investments but at least it was still either zero or positive. A firm is classified as a hedge (FFI = 2) if their operational profit is non-negative, which is computed from the cash dividends, interest payments, and investments. This framework emphasizes an assessment tool to explore the financial sustainability and/or vulnerability of an organization.

3.3. Methodology

This research takes a quantitative approach to examine the impact of an important firm-specific and macroeconomic factors on the financial fragility on Jordanian Non-Financial Corporations (NFCs) in the period of 2015 to 2021. It utilises a panel dataset of 71 NFCs listed on the Amman Stock Exchange and employs logistic regression to examine the impact of firm-specific and macroeconomic factors on the probability of being classified as financially fragile, based on the Financial Fragility Index (FFI). Panel logistic regression, as outlined by Torres-Reyna (2007), has multiple benefits such as improved precision of estimates, additional degrees of freedom, less multicollinearity, and controlling for the unobserved characteristics that do not change over time. Panel logistic regression is powerful for panel data analysis, as enumerated above when your dependent variable is categorical, due to improved statistical conclusions and more accurate, dependable data analysis.

In turn, FFI uses the following three ordered categorical values: 0 for Ponzi, 1 for speculative, and 2 for hedge. The use of a panel logistic regression model is theoretically justified, as it does not impose restrictive assumptions on the transition probabilities among financial regimes (Moosa, 2017; Maddala, 1983). Moreover, this specification allows for unobserved heterogeneity across firms and over time, which is critical in analysing financial fragility within a heterogeneous corporate environment such as Jordan’s. The choice of the methodology is based upon previous literature (e.g., Rozmainsky et al., 2022a), where the FFI is an ordered categorical variable (Ponzi < Speculative < Hedge), implying that a firm in the “Hedge” category is more stable than one in “Speculative,” which in turn is more stable than “Ponzi.” An Ordered Logit (or Ordered Probit) model is the standard and theoretically appropriate econometric technique for such a variable. The general model can be expressed as follows:

$${\mathrm{F}\mathrm{F}\mathrm{I}}_{\mathrm{i},\mathrm{t}}=\mathsf{\alpha }+\sum _{\mathrm{k}=1}^{\mathrm{n}}{\mathsf{\beta }}_k{\mathrm{X}}_{i,t}+{\mathsf{\epsilon }}_{i,t}$$

(1)

where ${\mathrm{F}\mathrm{F}\mathrm{I}}_{\mathrm{i},\mathrm{t}}$ is the financial fragility index (the dependent variable), and ${\mathrm{X}}_{\mathrm{i},\mathrm{t}}$ is a set of the predictors that are deemed important for financial fragility, and ${\mathsf{\epsilon }}_{i,t}$ is the error term.

Table A1. Study Variables.

Variables		Description
FFI	Financial Fragility Index	Financial Fragility Index calculated using Nishi’s (2019) methodology, which operationalizes Minsky’s financial instability hypothesis to assess the degree of financial fragility.
MBV	Market-to-Book Value Ratio	The market value of equity divided by the book value of equity, reflecting a company’s valuation relative to its balance sheet strength.
ROA	Return on Assets	Net income divided by total assets; this ratio measures a company’s efficiency in generating profits from its assets.
Size	Company Size	Measured by the natural logarithm of total assets, this metric is used to quantify the overall scale of a company.
Leverage	Financial Leverage	The leverage ratio is calculated as total debt divided by total assets, indicating the extent to which a company is financed by debt.
GDPG	GDP per Capita Growth	The rate of change in gross domestic product (GDP) per capita over a specific period, as measured by the World Development Indicators (WDI).
Inflation	Inflation, consumer prices	Measured as the annual percentage change in consumer prices, indicating the rate at which the general level of prices for goods and services is rising.
InstQ	Institutional Quality	A proxy for institutional quality, calculated as an equally weighted composite of the six World Governance Indicators (WGI) from the World Bank: voice and accountability (−2.5 to +2.5), political stability (−2.5 to +2.5), government effectiveness (−2.5 to +2.5), regulatory quality (−2.5 to +2.5), rule of law (−2.5 to +2.5), and control of corruption (−2.5 to +2.5).

Source: Authors own creation.

, in the appendix to this paper, exhibits the study variables and their description.

4. Empirical Results

4.1. Descriptive Analysis

The study commences the empirical analysis with descriptive statistics.

Table 2. Descriptive Statistics.

Variables	Observations	Mean	Std. Dev.	Min	Max
FFI	497	0.8792757	0.9865594	0	2
MBV	497	111.6076	106.0496	−721	685
ROA	497	3.036479	7.814204	−59.95	41.42
Size	497	10.49526	1.467526	6.768493	14.19807
Leverage	497	97.68568	235.649	0	2329.291
GDPG	497	−1.706668	2.494058	−6.531007	1.614222
Inflation	497	1.224547	1.866653	−0.8768514	4.462311
InstQ	497	−0.0916482	0.0107887	−0.1086508	−0.0733225

Source: Authors own creation.

shows that the Financial Fragility Index (FFI) has a mean value of approximately 0.879, with a standard deviation of 0.987. This suggests a moderate level of financial fragility among the sampled Jordanian non-financial companies. The presence of companies across the full spectrum of financial fragility—from those classified as financially instable “Ponzi” (0) to those considered highly stable “Hedge” (2)—highlights the diverse financial health within the sector. The mean value closer to 1 suggests that, on average, companies are closest to being speculative, potentially indicating a leaning towards Ponzi financial conditions according to Minsky’s categorization. This finding highlights the importance of investigating the underlying factors that contribute to financial fragility, as well as the necessity for targeted financial management and policy interventions to mitigate the risks associated with higher levels of fragility.

Furthermore, the descriptive statistics reveal significant variability among Jordanian non-financial firms in terms of market valuation (MBV), profitability (ROA), and financial leverage, indicating diverse financial health and risk profiles. In addition, macroeconomic factors, such as low/negative GDP per capita growth and moderate inflation, reflect challenging economic conditions. The consistent institutional quality scores indicate a stable but potentially improvable governance environment.

Table 3. Correlations.

	FFI	MBV	ROA	Size	Leverage	GDPG	Inflation	InstQ
FFI	1.00
MBV	0.14 *	1.00
ROA	0.44 *	0.32 *	1.00
Size	0.17 *	0.04	0.20 *	1.00
Leverage	0.01	0.35 *	0.01	0.38 *	1.00
GDPG	0.00	0.00	−0.01	0.01	0.01	1.00
Inflation	−0.05	0.00	0.01	0.01	0.01	0.64 *	1.00
InstQ	0.02	−0.04	−0.09 *	−0.01	−0.02	−0.54 *	−0.43 *	1.00

Notes: This table presents the pairwise correlation coefficients among the study variables. The asterisk * designates significance at the 10% level. Source: Authors own creation.

presents the pairwise correlation coefficients among the study variables. The correlation matrix shows no potential areas of multicollinearity that could influence the analysis. Notably, the highest correlations are between GDP per Capita Growth (GDPG) and Inflation (0.64 *), as well as the negative correlations between Institutional Quality (InstQ) with GDPG (−0.54 *), highlighting the intertwined nature of macroeconomic factors.

4.2. Logistic Regression Results

This study employed a panel logistic regression model to examine how firm-specific and macroeconomic factors influence the financial fragility of Jordanian Non-Financial Corporations (NFCs). The empirical results, summarized in

Table 4. Panel Logit Regression Results (Dependent Variable: FFI).

Variable	Coefficients
ROA	0.418 **
	0.000
MBV	−0.00015
	0.579
Size	0.179
	0.39
Leverage	0.00018
	0.88
GDPG	0.18 **
	0.02
Inflation	−0.179 *
	0.02
InstQ	35.8 **
	0.02
Sigma u (α)	1.341
Sigma e	1.956
Rho	0.538
Observations	458

Notes: The asterisks **, * designates significance at the 5%, and 10% level, respectively. Source: Authors own creation.

, provide nuanced evidence about the predictors of corporate fragility in an emerging market context.

The findings reveal that Return on Assets (ROA) plays a significant role in mitigating financial fragility, with a positive and statistically significant coefficient (0.418, p < 0.001). This outcome corroborates the Financial Instability Hypothesis (Minsky, 1992), which posits that firms with stronger internal financing capacities are less likely to transition from hedge to speculative or Ponzi positions. In line with prior studies (Khurshid et al., 2018; Indarti et al., 2020), higher profitability enhances a firm’s resilience by improving liquidity and reducing dependence on external finance. However, the magnitude of ROA’s effect suggests that profitability alone cannot fully safeguard firms against fragility, as profitability in emerging markets often reflects short-term efficiency rather than sustainable financial strength (Khan et al., 2023).

In contrast, market-to-book value (MBV), firm size, and leverage were not statistically significant predictors of financial fragility in either model. This result challenges conventional corporate finance theory, which often associates larger firms with better access to credit and lower default risk (E. Altman & Hotchkiss, 2005). The insignificance of size and leverage may reflect the structural characteristics of Jordan’s non-financial sector—dominated by medium-sized firms with limited capital market access and high exposure to domestic credit cycles. Similar patterns were observed in other emerging economies; for instance, Tran et al. (2023) found that firm size was not a reliable determinant of financial distress in Vietnamese firms, while Elhaj (2021) reported that leverage’s effect on financial stability weakens in contexts of weak financial intermediation and regulatory inefficiencies.

Turning to macroeconomic factors, the results highlight the significant influence of GDP per capita growth and inflation on financial stability. The positive and significant effect of GDP growth (β = 0.18, p < 0.02) suggests that improved economic conditions enhance firms’ repayment capacity and overall financial soundness. This aligns with Hafeez and Kar (2021) who demonstrated that macroeconomic expansion tends to reduce the incidence of financial distress in emerging markets. However, this relationship may also indicate that Jordanian NFCs remain procyclicality sensitive—benefiting from growth but vulnerable during downturns, consistent with Minsky’s cyclical fragility hypothesis.

Conversely, inflation exerts a significant and negative effect on financial stability (β = −0.179, p < 0.02), implying that rising prices exacerbate firms’ financial fragility by inflating input costs and eroding real profitability. This outcome is consistent with Santosa et al. (2020) and Tinoco et al. (2018), and is further supported by Neaime and Gaysset (2022), who found that inflationary pressures in MENA economies heighten firm vulnerability by destabilizing balance sheets and increasing default risk. In addition, the significance of macro-economic indicators in being able to predict financial distress in emerging markets, by Hafeez and Kar (2021). The finding highlight the importance of prudent monetary policy and inflation-targeting frameworks in containing systemic risk, particularly in small, open economies like Jordan.

Additionally, a strong positive correlation of 35.8 with a p-value below 0.02 is also identified for Institutional Quality (InstQ), suggesting that better institutional frameworks and governance correlate with better financial stability for private firms. This suggests that institutional frameworks and sound governance significantly influence the financial soundness of NFCs. The strength of this relationship highlights the importance of transparent, accountable, and effective institutions in creating an environment that fosters firm financial stability, and that strengthening institutional quality has a direct impact on firms’ financial stability. This underscores the strategic importance of institutional reforms and governance-oriented policy measures.

These findings align with previous studies, which emphasise the importance of high-quality institutions in building sustainability and resilience within economies. For example, Canh et al. (2021) show that improved quality institutions (government effectiveness, regulatory quality, rule of law, control of corruption, and political stability) is essential for reducing banking sector risk. Similarly, financial efficiency is heavily determined by institutional quality, and the impact is far stronger for countries with higher institutional quality (Khan et al., 2023; Ocampo, 2022).

5. Policy Implications and Recommendations

This research investigates the impact of major macroeconomic and firm-specific factors on the financial vulnerability of Jordanian Non-Financial Corporations (NFCs). The results provide recommendations for policymakers and corporate managers and regulators. The significant roles that profitability, institutional quality and inflation have in shaping financial stability establishes a fundamental basis for focused policy interventions and corporate governance changes. First, there is a need to highlight targeted financial support and restructuring initiatives in sectors exhibiting specific problems especially in vulnerable sectors such as “Hotels, Tourism and, Transportation”. Second, there is also a need to provide NFCs with improved financial literacy so that they have the information and capabilities to deal with financial distress. Resilience against financial shocks can be developed through enhanced educational opportunities focusing on risk assessment, financial management and contingency planning. Third, the positive relationship that exists between financial stability and Return on Assets (ROA) reinforces the importance of frameworks and regulations that assist firm profitability and operations efficiency.

To enhance productivity and profitability, the government could facilitate market access, stimulate innovation, and provide tax incentives for R&D. Finally, given the relevance of macroeconomic conditions, the government should promote reforms that establish an economic and institutional breed that is beneficial to Jordanian non-financial corporations. This includes promoting numeric GDP growth, controlling inflationary pressures, and improving institutional quality. The foundation for reducing financial vulnerability and increasing the economic resilience of Jordanian non-financial corporations starts with good governance, followed closely by stable macroeconomic conditions.

In this manner, Rozmainsky et al.’s exploration of the Financial Instability Hypothesis (FIH) in various jurisdictions, including Russia and the Netherlands, has significant policy implications and recommendations. The results suggest the need for economic policies to mitigate such vulnerabilities and the importance of recognizing and addressing the buildup of financial fragility in non-financial industries. They argue that austerity measures intended to stabilise economies may eventually exacerbate financial fragility and increase the number of businesses that operate like Ponzi schemes.

To sum up, in the short term, policymakers should focus on curbing inflationary pressures and providing targeted financial support to vulnerable sectors such as tourism and transportation. In the long term, strengthening institutional quality and governance mechanisms is essential to reduce systemic fragility. Encouraging innovation, promoting market diversification, and improving corporate financial literacy will enhance firms’ resilience to macroeconomic shocks.

6. Limitations and Future Research Recommendations

This study aims to fill a gap in the literature by examining the factors that contribute to financial fragility, with a specific focus on Jordan. We recognize several restrictions that open the door to additional study. The first is the small number of Jordanian NFCs in the sample, which limits the range of observations and the applicability of the conclusions. Consequently, it implies that via comparative studies across other nations, country groups, or regions, a future study could expand the applicability of the financial instability hypothesis. By revealing regional variations in the factors that contribute to financial fragility and its effects, these studies may provide a more thorough knowledge of the problem.

Second, the context and period of analysis are limited by the availability of data. Subsequent studies could significantly enhance the generalizability of these findings by expanding both the temporal scope and cross-country coverage. For instance, a broader dataset, encompassing a longer timeframe and multiple countries within the MENA region, would allow for a more robust analysis of how financial fragility evolves across different business cycles and under varied institutional settings.

Third, in the absence of an appropriate model, the number of explanatory variables that can be chosen is limited by data limits (Berk, 2010; Khatatbeh et al., 2022). In this regard, Leamer (2010) addresses the intrinsic constraints of economic data analysis, stressing that the inclusion of a large number of variables in multivariate analysis is limited by the need to make assumptions because of poor datasets. In his groundbreaking work on sensitivity analysis in regressions, he provided a tenable solution (Leamer, 1983, 1985).

Utilizing Sala-i-Martin’s (1997) form of Extreme Bounds Analysis (EBA) or newer Neural Network Classification Models would help immensely with the problem of model uncertainty that comes up in econometric analysis. It would also let you include as many explanatory variables as possible in the model (for example, Jaber et al., 2023; Khatatbeh & Moosa, 2022; Abu Alfoul et al., 2022).

Fourth, this study uses only one measure of financial fragility, following Nishi (2019), although other studies have used three different operationalizations based on Minsky’s financial stability hypothesis. Future studies could expand on this analysis by including more financial fragility proxies, as proposed by publications such as Mulligan (2013), Torres Filho et al. (2019), and L. E. Davis et al. (2019). By providing a more sophisticated knowledge of the dynamics of financial fragility in various circumstances, this method would enhance the analysis.

7. Conclusions

This study explores the effect of important firm-specific and macroeconomic factors on the financial fragility of Jordanian Non-Financial Corporations (NFCs) over the period between 2015 and 2021 by employing a panel logistic regression analysis. The study constructs a financial fragility index, grounded in Minsky’s (1992) financial instability hypothesis and methodically operationalized by Nishi (2019).

The findings demonstrate that profitability, measured by Return on Assets (ROA), positively affects financial stability and lowers the risk of financial vulnerability. Conversely, inflation exacerbates NFC’s financial vulnerability and higher rates of inflation result in greater financial vulnerability. The significant positive impacts of GDP growth and institutional quality are notable, as they highlight the importance of a country’s macroeconomic environment, governance, and institutional structure in promoting the financial health of corporations. To improve financial stability, our results recommend policy interventions aimed at enhancing institutional quality, effective inflation management, and corporate profitability.

Future studies could build on the current research by examining further macroeconomic or company-specific indicators of financial fragility. Additionally, studying how external shocks, such as pandemics and global financial crises, affect financial fragility may provide valuable insights. The breadth and relevance of financial instability hypothesis research could be expanded by a future study that compares different countries or regions to highlight distinctive factors that contribute to financial fragility.