Determinants of Financial Risks Pre- and Post-COVID-19 in Companies Listed on Euronext Lisbon

Abstract

The COVID-19 pandemic had a significant impact on the economy and the stability of financial markets, creating challenges and financial risks for companies. This study analyzes the financial reports of companies listed on Euronext Lisbon with the aim of examining financial risk disclosures and calculating their determinants. For this purpose, data was collected from the Euronext Lisbon website as well as the companies’ own websites. Once the data were gathered, 16 companies were analyzed over a five-year period, from 2018 to 2022. Using panel data regression techniques (e.g., fixed effects regression models), it was observed that profitability, capital structure, and size have a positive but not statistically significant relationship with interest risk. Conversely, size and capital structure they have a positive and significant relationship with liquidity risk. Profitability has a positive and significant relationship with insolvency risk. Macroeconomic variables do not exhibit consistent signs across all models. This research provides insights into how the determinants of financial risks influence risks during a pandemic period.

1. Introduction

In recent years, enterprise financial risk management has gained significant prominence due to major global events, such as the financial crisis and the COVID-19 pandemic. These crises have highlighted the importance of companies’ abilities to effectively identify, manage, and disclose risks, particularly in critical financial markets like Euronext Lisbon. The transparency and quality of information disclosed by publicly traded companies have become essential, especially during periods of uncertainty when investors and regulators rely heavily on accurate and comprehensive data.

Despite the extensive research on financial risk management and disclosure practices (El-Chaarani et al., 2023; Heitmann et al., 2023; Smaoui et al., 2020; Suardi et al., 2022), a notable gap persists in understanding how these practices evolve in response to global crises, particularly in smaller financial markets like Portugal. Much of the existing literature focuses on larger, more extensively studied markets, often overlooking the unique dynamics and challenges that markets like Euronext Lisbon face. Addressing this gap, the present study explores changes in financial risk disclosure practices during the pre- and post-COVID-19 periods, focusing on companies listed on Euronext Lisbon.

The primary objective of this research is to analyze shifts in the disclosure of liquidity risk, insolvency risk, and interest rate risk in response to the pandemic while identifying the determinants of these risks. Using firm-specific characteristics such as size, capital structure, and profitability, alongside macroeconomic factors like Gross National Product and inflation, this study employs a data panel regression techniques (e.g., fixed effects regression models) on a sample of 16 companies, totaling 80 observations over a five-year period (2018–2022). The inclusion of COVID-19 as a dummy variable allows for a nuanced understanding of its impact on corporate behavior. Grounded in theoretical frameworks such as agency, stakeholder, signaling, and trade-off theories, the study consolidates insights from various empirical studies to identify common determinants of financial risk during global crises.

This research provides original contributions to the field by focusing on the underexplored Portuguese market, offering insights that complement findings from larger financial markets. Furthermore, it bridges theoretical and empirical gaps by integrating established theories with empirical data, yielding a multidimensional perspective on financial risk management and disclosure during a period of unprecedented economic disruption.

The remainder of this article is organized as follows. The next section discusses the study’s theoretical framework, followed by a detailed explanation of the research methodology, including data collection and analysis methods. Subsequently, the results are presented and analyzed, drawing comparisons with existing literature. Finally, the article concludes by summarizing the key findings, discussing their implications, and suggesting avenues for future research.

2. Theoretical Framework

2.1. Risk and Risk Disclosure

Risk is inevitable in any company and market (Amran et al., 2009). It can be defined as the uncertainty or undesirability associated with a particular outcome of a specific event. Risk exists when there is the possibility of not achieving the desired result (Pinho et al., 2011). Risk can have negative and positive components; thus, companies should not only control their exposure to risks by minimizing potential adverse effects but also leverage the benefits of positive risks (Lajili & Zéghal, 2005).

To build on this foundational understanding of risk, it is important to distinguish between its main classifications. Risks are broadly categorized into firm-specific risks and systematic risks. Firm-specific risks, unique to individual companies or industry sectors, can often be mitigated through portfolio diversification (Pinho et al., 2011). In contrast, systematic risks, such as those arising from inflation and interest rate fluctuations, are market-wide and beyond the control of individual entities, thus requiring different management approaches. These two categories provide a foundation for distinguishing the strategies companies must adopt to effectively manage different types of risks.

Firm-specific risk refers to those that impact an individual company, group of companies, or industry sector and is characterized as unique, residual, and non-systematic risk (Fernandes et al., 2019). Systematic risk, by contrast, encompasses market-wide factors beyond the control of individual entities, such as economic instability or changes in government policies. Understanding the interplay between these risk categories is crucial, as systematic risks often compel companies to adopt strategies that address broader market conditions rather than internal challenges.

The work of Lajili and Zéghal (2005) further highlights the critical role of risk disclosure in supporting decision-making processes. They argue that the effective communication of risk-related information provides stakeholders with essential insights into a company’s operational vulnerabilities and opportunities. Moreover, their research emphasizes that transparency in risk disclosure not only enhances accountability but also serves as a mechanism for mitigating information asymmetry between firms and their investors. These insights underscore the importance of robust financial risk management frameworks, particularly in complex and interconnected market environments.

In response to these challenges, the importance of risk-related information has grown significantly, as it provides users with fundamentally relevant data that can be useful for company administrators in strategic decision-making (Lajili & Zéghal, 2005). business risk can be defined as the risk of equity capital invested in the company (Fernandes et al., 2019). This relevance is amplified in the context of systematic risks, which may lead shareholders or company partners to demand a risk premium due to their inherent undiversifiable nature. Consequently, business risk, defined as the risk of equity capital invested in the company (Fernandes et al., 2019), underscores the critical need for robust disclosure practices.

Further classifying risks into economic, financial and global categories adds nuance to this discussion. Economic or operational risks arise from uncertainties such as demand volatility or price instability. Financial risks involve losses stemming from changes in financial markets (Pinho et al., 2011) and global risks reflect the broader vulnerabilities that span economic and financial dimensions (Fernandes et al., 2019). This tripartite categorization helps to contextualize risks within the operational and market environments companies navigate.

Recognizing the diversity of financial risks, international regulatory frameworks such as IFRS 7 provide a structured approach to their disclosure. Financial risks are subdivided into credit risk, market risk (including interest rate, exchange rate, and price risks), and liquidity risk. Each category addresses distinct aspects of financial uncertainty, emphasizing the multifaceted nature of risk management. For instance, credit risk concerns the failure to meet obligations within agreed timeframes (Silva, 2015), while market risks encompass fluctuations in interest and exchange rates, impacting asset values and liabilities (Pinho et al., 2011).

Building on these classifications, liquidity risk, closely tied to the conversion of assets into cash, highlights a company’s ability to meet its obligations. This risk often arises when assets and liabilities are misaligned, illustrating the importance of effective financial planning to maintain operational stability (Pinho et al., 2011). Similarly, global risks emphasize the interconnectedness of financial and economic vulnerabilities, urging companies to adopt holistic approaches to risk management (Fernandes et al., 2019).

To support these practices, mandatory risk disclosure frameworks have been established in various developed countries, including those outlined in IFRS 7 and local regulations like Ordinance 220/2015 in Portugal. These frameworks mandate the disclosure of interest rates, exchange rates, and credit risks, ensuring consistency and transparency. Such requirements are complemented by the Commercial Companies Code provisions, which obligate listed entities to disclose financial risks in their annual management reports.

By integrating these insights, this discussion underscores the multifaceted nature of risk, its management, and the critical role of disclosure. The alignment of regulatory standards with practical risk management highlights the evolving expectations for corporate transparency, particularly in navigating complex and interconnected market environments.

2.2. Agency, Stakeholder, Signaling, and Trade-Off Theories

The theoretical foundation of this study is built on four interrelated theories, each contributing to the understanding of risk management, organizational performance, and disclosure practices. agency theory (Jensen & Meckling, 1976) explains the conflicts arising from the separation of ownership and control, with a focus on the importance of monitoring and alignment mechanisms to mitigate agency costs. In the context of risk management, this theory underscores the significance of transparent disclosure in aligning stakeholders’ interests and reducing potential conflicts.

Jensen and Meckling (1976) define agency costs as the sum of monitoring expenses, warranty expenses, and residual losses. While monitoring expenses aims to control the agent’s harmful actions, warranty expenses ensure that the agent does not engage in actions detrimental to the principal and compensate the principal if harm occurs. These costs are unavoidable because it is impossible to ensure that an agent acts entirely in accordance with the principal’s interests without some form of cost. According to agency theory, a company’s profitability is directly linked to its financial performance. Profitability will improve if managers optimize their capital structures and reduce financial risks (Heitmann et al., 2023; El-Chaarani, 2023).

Stakeholder theory, as proposed by Freeman and McVea (2005), suggests that managers are compelled to create organizational structures that address the needs of various stakeholders, especially in the face of environmental changes. In this theory, a stakeholder is defined as any individual or group that can influence or be affected by the achievement of a company’s objectives. This theory emphasizes that organizations should consider the interests of all stakeholders—not just shareholders—when making decisions. The theory’s primary objective is to integrate the diverse interests of shareholders, employees, customers, suppliers, and other groups to ensure the company’s long-term success. This inclusive approach to decision-making results in a more responsible and sustainable strategy, ultimately benefiting the organization and its stakeholders. Freeman and McVea (2005) argue that the key to successful management lies in understanding the various circumstances of stakeholders, allowing managers to create strategic options with the support of all parties involved, thereby ensuring the company’s survival.

Signaling theory, developed by Spence (1973), explains the behavior of companies in markets characterized by information asymmetry. According to this theory, firms can use signals to convey information to investors, shaping future expectations and reducing uncertainty. These signals are particularly important in addressing the information imbalance between the company and external parties. By voluntarily disclosing information, high-quality firms differentiate themselves from lower-quality firms (Watson et al., 2002).

The theory supports disclosing specific performance metrics such as investment, profitability, and efficiency ratios, as these indicators can highlight a company’s strengths. However, when a company’s performance is poor, it may refrain from disclosing such information, thereby signaling a lack of confidence in its prospects (Watson et al., 2002). In line with signaling theory, Cardillo and Chiappini (2022) found that companies engaging in environmental and social disclosure practices can mitigate information asymmetry and distinguish themselves from competitors that lack such practices. This theory also identifies a potential limitation to the extent of environmental and social disclosure.

Trade-off theory posits that companies should aim for an optimal debt level that balances the benefits of debt—such as tax advantages—and the risks associated with bankruptcy. This theory recognizes that factors such as agency costs can lower the ideal debt ratio while other forces, such as tax incentives, encourage higher debt levels. The combination of these forces leads to an optimal capital structure. Several studies indicate that companies use this theory when making capital structure decisions (Serrasqueiro & Caetano, 2015). For example, small and medium-sized enterprises (SMEs) often maintain a manageable level of debt due to the company’s size and potential for growth in debt capacity.

Moreover, trade-off and agency theories address the role of agency costs that arise from conflicts of interest between different organizational agents, such as owners, creditors, and managers (Costa et al., 2015). This suggests that a company’s profitability is influenced not only by its financial performance but also by its capital structure and financial management system. El-Chaarani (2023) emphasizes that enhancing capital structures and reducing financial risks are key to improving the profitability of financial institutions.

This study provides a comprehensive framework for analyzing risk disclosure practices by integrating these four theories. The interplay of these theoretical constructs offers valuable insights into how organizations respond to financial crises and the role of disclosure in aligning interests, minimizing risks, and optimizing organizational performance.

3. Hypothesis Development

Since the beginning of the COVID-19 pandemic, several studies have been developed on financial risks and COVID-19. However, it is essential to note that there are few studies on this topic in Portugal compared to the number of studies conducted internationally. The consequences of COVID-19 have led to many studies being developed to verify, investigate, and evaluate the pandemic’s impact on the financial performance of companies, banks, and capital markets. Authors such as Apergis et al. (2023); Archanskaia et al. (2023); El-Chaarani (2023); El-Chaarani et al. (2023); Feng et al. (2021); Heitmann et al. (2023); Kaya (2022); Smaoui et al. (2020); Suardi et al. (2022); Zhao (2021) are some of the authors who developed their studies based on this objective. These studies support the goals of this paper, which aim to analyze the spread of various risks before and after the COVID-19 period and to identify their determinants.

However, other authors have developed their studies within the theme of COVID-19 and financial risks with distinct objectives, such as determining the relationship between liquidity and credit risk (Magwedere & Marozva, 2022), examining how credit risk is sensitive to government policies (Cardillo & Chiappini, 2022), analyzing how some banking groups manage their exposure to interest rate risk by also examining the determinants of interest rate spreads (Esposito et al., 2015), and assessing how changes in financing liquidity conditions can affect the volatility of share portfolios (Kocaarslan & Soytas, 2021). Additionally, Mahssouni et al. (2023) analyzed the impact of COVID-19 and digital transformation on the financial performance of a set of companies with listed values. On the other hand, some studies focused not on COVID-19 but on the economic crisis between 2004–2011 (Hainz et al., 2014) and explored market-level developments (Smaoui et al., 2020) regarding different types of risks and financial performance.

The studies mentioned above used different methodologies for data collection and processing. When focusing on research with objectives similar to this work, it is clear that the most common method is using different types of regressions. For example, El-Chaarani (2023) and El-Chaarani et al. (2023) apply the multiple regression model and t-tests. Similarly, Apergis et al. (2023), Feng et al. (2021), and Heitmann et al. (2023) employed the Generalized Method of Moments Regression System. The first two authors also used the panel regression model and multiple regression models, respectively. Suardi et al. (2022) adopted a panel regression model, while Kaya (2022) utilized a generalized vector autoregressive model. Lastly, Archanskaia et al. (2023) and Zhao (2021) opted for a linear regression model, which differs from the panel regression model only in the type of data used. Understanding the relationship between the variables used in these regressions is essential for drawing conclusions and making predictions. For instance, the multiple regression model used by El-Chaarani (2023) and El-Chaarani et al. (2023) includes nearly identical variables: the dependent variables are return on assets and the return on equity, while the independent variables include credit risk, liquidity risk, management efficiency, oil price, inflation, GNP, capital structure, and bank size. However, El-Chaarani (2023) also included non-traditional Islamic activities.

On the other hand, linear regression models feature distinct variables in different studies. In Zhao (2021), the variables include current assets, current liabilities, total assets, accumulated profit, market value, book value of total liabilities, and EBIT. Contrarily, Archanskaia et al. (2023) used turnover as the dependent variable and as independent variables, such as the number of COVID-19 deaths, government strictness, mobility, GNP, economic support, non-health government expenditure, consumer and business confidence, and indicators of value chain exposure.

Regarding the Generalized Method of the Moments Regression System, standard variables include the interest rate used by Apergis et al. (2023) and Feng et al. (2021). However, the studies differ in other variables: Feng et al. (2021) included foreign exchange reserves, consumer prices, COVID-19 confirmed cases, exchange rate volatility, and government response indices, while Apergis et al. (2023) focused on the illiquidity index, closing percent quoted spread, volatility, capitalization, stock price index, lockdown index, and death tolls.

In the panel regression model, some variables overlap across studies. Kaya (2022) analyzed insolvency risk (dependent variable), company characteristics (dummy variables), inflation, and GNP—variables shared with Smaoui et al. (2020). Additionally, Smaoui et al. (2020) included the total loans-to-assets ratio, management efficiency, bank size, market development, competition, religiosity, legal system, corruption control, and liquidity. Meanwhile, Suardi et al. (2022) considered turnover ratio, liquidity, volatility, and COVID-19 cases and deaths.

Regarding the conclusions, the studies broadly agree that the COVID-19 pandemic negatively affected liquidity and increased its risk (e.g., El-Chaarani, 2023; Kocaarslan & Soytas, 2021; Magwedere & Marozva, 2022; Suardi et al., 2022; Apergis et al., 2023). Similarly, research by El-Chaarani et al. (2023); Heitmann et al. (2023); and Mahssouni et al. (2023), highlights the pandemic’s detrimental impact on the financial performance of companies and banks. Kaya (2022) found that insolvency risk for SMEs increased significantly during the pandemic. Overall, the studies show that COVID-19 amplified risks in banks and companies, particularly interest rate, insolvency, and liquidity risks.

3.1. Size

According to the trade-off approach, large companies tend to increase their debt level due to the lower probability of bankruptcy and to improve the tax benefits of debt. In recent years, many researchers have analyzed the relationship between the size of banks and the various risks and returns, considering unstable periods. El-Chaarani (2023) suggests that the size of banks is not significant during unstable periods since small and large banks face the same difficulties during the pandemic period. However, in another study, El-Chaarani et al. (2023), which analyzed the financial performance and the impact of COVID-19 on it, concluded that the size of the bank had a significant and positive effect on both the return on assets and the return on equity, before and during the pandemic, with the positive impact being more significant during the pandemic.

According to Smaoui et al. (2020), the size of the banks in their research (Islamic and conventional) has a negative and significant effect on insolvency risk, in line with the Too-Big-To-Fail hypothesis. This expression is used to characterize large and highly important companies and banks, whose failure would significantly impact the economy. This negative effect is more noticeable in Islamic banks than in conventional banks, indicating that Islamic banks have a higher risk of insolvency. This conclusion may be because Islamic banks have limited access to hedging instruments compared to conventional banks.

Similarly, Heitmann et al. (2023), when analyzing the impact of the COVID-19 pandemic on performance indicators, concluded that the size of the bank is a positive predictor, not only between trading and operating revenues but also between the liquid assets ratio and short-term financing. Additionally, they observed that the larger the bank, the lower the liquidity risk.

H1: 

The positive relationship between the level of financial risk and the COVID-19 period is moderated by the size of the companies.

3.2. Capital Structure

Capital structure refers to the different options companies use to finance their operations and investments through the combination of debt and equity. Financing decisions are highly important since the efficient combination of different sources of financing can help reduce financing costs while increasing the company’s overall value. When defining capital structure, it is essential to consider a set of theories, such as the trade-off, agency, and signaling theories.

According to the trade-off theory, companies seek to ensure that their capital structure maximizes their value, always considering the existing trade-offs between the benefits and costs of debt and equity. In the case of the signaling theory, the choice of capital structure can also be an indication for investors regarding the company’s prospects. Regarding the agency theory, capital structure can often be used to reduce agency problems, such as conflicts of interest between shareholders and management.

According to El-Chaarani et al. (2023), the capital structure, before and during the pandemic, showed a positive relationship with the financial performance of banks, but it was still insignificant. This conclusion is in line with the values presented by the t-test, which verified that the value of the capital structure did not undergo any relevant change during the pandemic. In another study, El-Chaarani (2023) confirmed the findings of El-Chaarani et al. (2023) and found that capital structure had an insignificant effect on the financial performance of banks before and during the pandemic.

H2: 

The positive relationship between the level of financial risk and the COVID-19 period is moderated by the capital structure of companies.

3.3. Profitability

Profitability is defined as the ability of companies to obtain an income greater than their expenses, i.e., profits. This concept can be analyzed from two distinct perspectives. The operational perspective consists of the relationship between results and turnover, and the strategic perspective refers to the relationship between results and investment (Fernandes et al., 2019). Profitability influences the company’s financial decisions, shaping key aspects such as resource allocation and long-term planning.

El-Chaarani (2023), in his research, concludes that, according to several theories, the profitability of companies is related to their financial performance, capital structure, and their financial management system. According to agency theory, agency conflicts arising from misaligned interests can negatively affect profitability since managers may make incorrect decisions focused solely on their benefit. Conversely, the stakeholder theory suggests that financial decisions should not focus exclusively on company profits but rather create a balance that benefits the stakeholders. Such an approach promotes long-term and more stable profitability.

As profitability influences capital structure, it is essential to consider the implications of the trade-off theory. The decision on financing operations—through debt or equity—affects the profits generated and profitability. In addition, the profitability of each company can be used as a signal of its financial health in the market, according to signaling theory.

Heitmann et al. (2023) and El-Chaarani et al. (2023) further demonstrate that the COVID-19 pandemic had a negative impact on profitability, either by increasing costs, decreasing revenues, or both simultaneously. Their studies also conclude that financial risks and political and economic uncertainty result in a decrease in profitability.

H3: 

The positive relationship between the level of financial risk and the COVID-19 period is moderated by companies’ profitability.

3.4. Gross Nacional Product

GNP represents the result of the economic activity of institutional units’ residents in each territory during a given period. It is also a fundamental indicator for assessing economic activity and the overall performance of an economy. As such, it serves as a crucial metric for analyzing trends and identifying economic impacts, such as those caused by the COVID-19 pandemic.

There are several studies that use GNP as an indicator to verify the impact of COVID-19 on financial performance. Interestingly, contrary to many other studies, El-Chaarani (2023) concluded that GNP has no effect on financial returns during the pandemic. However, in another article, the same author reported contrasting findings, concluding that GNP had a significant effect on the financial performance of banks, more specifically on the variables of return on assets and equity.

According to Smaoui et al. (2020), GNP growth had a positive impact on the z-score, which supports the hypothesis that economic expansion leads to greater wealth and lower insolvency rates. Similarly, Kaya (2022) concluded that GNP shows a negative signal regarding insolvency, effectively reducing it. During periods of high GNP growth, SMEs may experience advantages in the demand for their products and services. Consequently, they obtain higher revenues and a lower risk of insolvency.

H4: 

The positive relationship between the level of financial risk and the COVID-19 period is moderated by GNP.

3.5. Inflation

In a market economy, the prices of goods and services fluctuate, with some prices falling and others rising. Inflation occurs when there is a gradual and general increase in the prices of goods and services, not just for particular items. This phenomenon affects various aspects of economic stability and financial decision-making.

According to El-Chaarani (2023), the increase in the number of COVID-19 cases had a negative impact on the inflation rate, as it increased as a result. Even so, the author did not find a significant effect on financial returns during the pandemic, suggesting that inflationary pressures did not directly influence profitability metrics or investment outcomes in this context.

Smaoui et al. (2020) and Kaya (2022) found that inflation always had a negative and significant effect due to the pandemic. In the first case, Smaoui et al. (2020) concluded that this impact was related to the risk of insolvency, while Kaya (2022) associated it with the availability of bank loans. Additionally, in the study by Kaya (2022), the findings indicated that inflation is not significant for other variables or has low significance. During periods of high inflation, there is a high aversion to risk. Therefore, the availability of bank loans and credit lines tends to decrease (Kaya, 2022).

H5: 

The positive relationship between the level of financial risk and the COVID-19 period is moderated by inflation.

3.6. Insolvency Risk

Insolvency risk is the probability that a company or individual will not be able to meet its commitments. This risk is a key indicator of financial health and can have broad implications for creditors, investors, and the economy.

According to the signaling theory, effective planning, control, and management of financial resources can signal reduced insolvency risk. On the other hand, the agency theory suggests that managers may act in their interests, even to the detriment of creditors, which can increase the company’s insolvency risk. In contrast, the trade-off theory emphasizes that companies must achieve a balance between the tax benefits of debt and the associated risk of insolvency, ensuring that neither aspect outweighs the other.

According to Kaya (2022) and Smaoui et al. (2020), insolvency risk in both studies always has a negative and significant impact. Smaoui et al. (2020), attribute this effect to a decrease in the z-score, which is a consequence of greater management inefficiency. Kaya (2022) concluded that companies that faced insolvency problems in the past will be more likely to encounter bank credit problems in the future, indicating the lasting implications of insolvency challenges.

H6: 

There is a positive relationship between the level of insolvency risk and the COVID-19 period.

3.7. Liquidity Risk

Liquidity risk reflects the possibility that a company, financial institution, or even an investor may not have sufficient funds to meet its financial commitments. This risk occurs when liquid assets are unavailable to meet immediate needs. As a critical factor in financial stability, liquidity can impact both short-term obligations and long-term strategic opportunities.

Liquidity can play a fundamental role in improving a company’s financial performance. As concluded in the study by Mahssouni et al. (2023), greater liquidity allows companies greater flexibility in managing not only financial obligations but also in capitalizing on investment opportunities. However, this flexibility requires careful management. According to the trade-off theory, companies must find a balance between maintaining an adequate level of liquidity and seeking investments that maximize the return on capital. Similarly, the signaling theory, emphasizes that a company must use its management policies and practices to show creditors and investors that it is prepared to meet all its financial commitments, thereby reducing concerns about liquidity risk.

According to the study by El-Chaarani (2023), the emergence of the pandemic led to increased liquidity risk; however, there is no significant association between this risk and the financial performance of banks before and during the pandemic. In contrast, Suardi et al.’s (2022) research concludes that the COVID-19 pandemic accentuates liquidity risk and further intensifies the vulnerability of individual stock market liquidity to aggregate liquidity shocks in financial markets. This highlights the varying effects of liquidity risk depending on the context and financial sector.

H7: 

There is a positive relationship between the level of liquidity risk and the COVID-19 period.

3.8. Interest Rate Risk

Interest rate risk is part of market risk and results from changes in the value of financial instruments due to changes in interest rates. This type of risk directly influences financial institutions, affecting their positions, valuation, and overall stability. Any movement in interest rates impacts the institution’s position and current value. In addition, these changes directly impact a bank’s earnings, particularly on net interest income.

According to the trade-off theory, companies need to find a balance between the benefits of protection against interest rate risk and the costs associated with different types of financing. Similarly, the signaling theory suggests that companies should use their policies and capital structure to provide signals to investors and creditors, indicating their ability to manage and mitigate this risk effectively.

In the study by Feng et al. (2021), interest rate changes exhibited a negative and significant influence on exchange rate fluctuations. This relationship may stem from multiple factors, including the attractiveness of financial assets, as higher interest rates often make financial assets more desirable for trading. In the research by Hainz et al. (2014), interest rate risk was shown to have a pronounced effect, as it increases spreads for all loans. This effect is explained by banks’ need to hedge against the risk of borrowing in the interbank market, leading to the application of higher spreads to loans.

H8: 

There is a positive relationship between the level of interest rate risk and the COVID-19 period.

4. Methodology

4.1. Sample Definition

In this study, the target population investigated comprises companies listed on Euronext Lisbon, a total of 34 companies. The sample analyzed is made up only of companies that are part of the index that regulates and reflects the evolution of the price of the largest companies listed on the Lisbon Stock Exchange, the PSI-20. Therefore, this sample comprises 16 companies that corresponded to a total of 80 observations.

The data required to prepare this study were collected from the companies’ websites and the Euronext Lisbon database. The reports covered the period between 2018 and 2022, before, during, and after COVID-19. Although the COVID-19 pandemic was officially declared by the World Health Organization (WHO) on 11 March 2020, the virus emerged in late 2019, with the first cases recorded in December 2019 in Wuhan, China (Huang et al., 2020). This period marked the initial outbreak and subsequent global spread of the virus, leading to significant disruptions in world markets, including knee-jerk reactions from companies and governments. These factors collectively justify the inclusion of the end of 2019 and the whole of 2020 as the pandemic period in the analysis of companies’ behavior and risk management practices. Including 2019 ensures that the early signals of market responses to COVID-19 are captured, reflecting a comprehensive timeline of how the pandemic influenced financial risks and disclosure practices.

4.2. Dependent Variables

Each sample element has its characteristics. However, only those that can be assessed are considered study variables. Variables are essential not only in the development of hypotheses but also in the collection and analysis of data, and they can also be classified as dependent or independent. Dependent variables are those influenced or affected by changes in independent variables.

Therefore, since this study aims to analyze the disclosure of different risks and calculate their determinants in companies with values listed on Euronext Lisbon in the pre-and post-COVID-19 Era, liquidity risk, insolvency risk, and interest rate risk were defined as dependent variables. The metric used for the liquidity risk variable is the quotient between current assets and current liabilities, i.e., the calculation of overall liquidity. Insolvency risk was calculated using Altman’s Z-score. Interest rate risk corresponds to the interest rate percentage, as shown in

Table 1. Table of Dependent Variables Description.

Dependent Variables	Calculation Formulas for the Variables
Liquidity Risk	Current Assets/Current Liabilities
Insolvency Risk	Altman’s Z-Score
Interest Rate Risk	Interest Rate

.

4.3. Independent Variables

The study analyzes financial risk disclosure practices using a sample of 16 publicly traded companies listed on Euronext Lisbon, covering the period between 2018 and 2022. To explore the determinants of financial risks, the analysis considers three dependent variables: liquidity risk, insolvency risk, and interest rate risk. These variables are examined against a set of independent variables, including firm-specific characteristics and macroeconomic indicators. Liquidity risk is measured as the ratio of current assets to current liabilities (expressed as a percentage), while insolvency risk is quantified through the debt-to-equity ratio (expressed as a percentage). Interest rate risk is evaluated using the sensitivity of interest expenses to interest rate changes (measured in basis points). Independent variables include firm size (measured by total assets in millions of euros), capital structure (debt-to-asset ratio, as a percentage), profitability (return on assets, as a percentage), Gross National Product (GNP, in millions of euros), and inflation rate (annual percentage change). Additionally, COVID-19 is introduced as a dummy variable, taking the value of 1 for observations during the pandemic period and 0 otherwise.

Independent variables are those analyzed and observed by the researcher to assess their impact on the dependent variables. This study’s selected independent variables are firm size, profitability, capital structure, GNP, inflation, and COVID-19. These variables are expected to influence or cause changes in the dependent variables.

The variable firm size was derived from the natural logarithm of total assets reported by the selected companies in their financial statements. Profitability was calculated as the operational return, determined by the ratio of operating income to total assets. The capital structure was measured using the financial autonomy formula, calculated as the ratio of equity to total assets. Inflation and GNP were obtained through their respective growth rates.

The variable COVID-19 is a dummy variable, allowing the integration of categorical information into statistical models. This variable assumes two distinct values, typically 0 and 1. In this case, 0 represents the periods before (2018) and after COVID-19 (2022), while 1 represents the years during the pandemic (2019, 2020, 2021), as indicated in

Table 2. Table of Independent Variables Description.

Independent Variables	Calculation Formulas for the Variables
Firm Size	Natual Logarithm of Total Assets
Capital Structure	Equity/Assets
Profitability	Operating Income/Total Assets
GNP	Growth Rate
Inflation Rate	Growth Rate
COVID-19	Dummy Variable = 1—2019, 2020, and 2021 During the pandemic; 0—2019 and 2022 Pre-pandemic

.

4.4. Econometric Model

To process the data, we employed content analysis and panel data regression techniques. Content analysis is a method that can be applied in both quantitative and qualitative research, encompassing a set of interpretative techniques that aim to understand and extract meaning from textual and communicational data. There are various types of content analysis, but this study uses qualitative content analysis, as the objective is to examine and investigate the content and values presented in the companies’ financial reports.

The analysis was conducted using panel data regression techniques. The fixed effects approach in panel data is frequently used to limit selection bias problems (Brown et al., 2011) and controls unobserved firm-specific and/or time-invariant heterogeneities. The panel data regression models diagnostic statistical tests (F statistic, Breusch-Pagan statistic, and Hausman statistic) validated the fixed effects model.

Thus, the panel data fixed effect regression models for this study can be defined as:

Regression Models

Regression 1:

Liquidity Risk = β₀ + β₁ COVID-19 + β₂ Size + β₃ Capital Structure + β₄ Profitability + β₅ GNP + β₆ Inflation + β₇ COVID-19Size + β₈ COVID-19Capital Structure + β₉ COVID-19Profitability + β₁₀ COVID-19GNP + β₁₁ COVID-19 × Inflation + γ + ε

Regression 2:

Insolvency Risk = β₀ + β₁ COVID-19 + β₂ Size + β₃ Capital Structure + β₄ Profitability + β₅ GNP + β₆ Inflation + β₇ COVID-19Size + β₈ COVID-19Capital Structure + β₉ COVID-19Profitability + β₁₀ COVID-19GNP + β₁₁ COVID-19 × Inflation + γ + ε

Regression 3:

Interest Rate Risk = β₀ + β₁ COVID-19 + β₂ Size + β₃ Capital Structure + β₄ Profitability + β₅ GNP + β₆ Inflation + β₇ COVID-19Size + β₈ COVID-19Capital Structure + β₉ COVID-19Profitability + β₁₀ COVID-19GNP + β₁₁ COVID-19 × Inflation + γ + ε

where:

–

Liquidity Risk is a dependent variable calculated as the ratio of current assets to current liabilities.

–

Insolvency Risk is a dependent variable measured using Altman’s Z-score.

–

Interest Rate Risk is a dependent variable corresponding to the interest rate percentage.

–

COVID-19 is a variable representing the years within the effective COVID-19 period (2019, 2020, 2021).

–

Size is the natural logarithm of total assets.

–

Capital Structure is calculated as the ratio of equity to total assets.

–

Profitability is calculated as the operational profitability, which is the ratio of operating income to total assets.

–

GNP and Inflation represent their respective growth rates.

Authors such as Zhao (2021) consider this statistical approach robust, essential, and fundamental for testing and verifying the linear relationship between variables.

5. Results

5.1. Descriptive Analysis

Descriptive analysis is a process that allows describing and summarizing data, whether from a sample or a population. The main objective of this analysis is to understand the main characteristics of a data set and obtain an overview of it without distortion or loss of any information.

Table 3. Descriptives Statistics.

Description	N	Minimum	Maximum	Mean	Standard Deviation
Insolvency	80	−0.26	16.29	1.46	2.86
Liquidity	80	0.171	4.43	1.05	0.66
Interest	80	0.04%	11.17%	2.49%	2.07%
Size	80	16.340	25.180	21.415	2.090
Inflation	80	−0.30%	8.10%	2.32%	3.09%
Capital	80	3.03%	95.63%	32.61%	18.69%
Profitability	80	0.01%	20.09%	5.15%	4.22%
GNP	80	−7.60%	6.80%	2.04%	5.10%
Valid N (from list)	80
	Categories	N	%
COVID-19	No = 0	32	40
	Yes = 1	48	60

presents the descriptive statistics of our study.

The analysis of Table 3 reveals that the sample consists of 80 observations. Insolvency risk values range from −0.26 to 16.29, with a mean of 1.46 and a standard deviation of 2.86. The large amplitude between the minimum and maximum values highlights significant variability within this variable. Furthermore, the high standard deviation indicates substantial data dispersion, reflecting the sample’s irregularity and lack of homogeneity.

Liquidity risk ranges from 0.17 to 4.43, with a mean value of 1.05 and a standard deviation of 0.66. These figures suggest a moderate dispersion of the data relative to the mean. Additionally, based on the mean value, it can be concluded that most companies demonstrate positive liquidity, indicating their capability to meet financial obligations.

Interest rate risk values range from 0.04% to 11.17%, with a mean of 2.49% and a standard deviation of 2.07%. This variable reveals considerable variability in the applied interest rates. Nevertheless, the standard deviation shows that the values remain relatively close to the mean. It is also evident that the interest payments amount to 2.49% of total liabilities.

Size, calculated based on the natural logarithm of total assets, ranges from 16.34 to 25.18, with a mean of 21.415 and a standard deviation of 2.090. The extensive range between the minimum and maximum values indicates substantial variability, which can be attributed to the diversity of sectors represented by the selected companies. The low standard deviation reflects significant low dispersion in the asset values.

Inflation values range from −0.30% to 8.10%, with a mean of 2.32% and a standard deviation of 3.09%. The substantial variability in inflation can largely be attributed to inflationary pressures induced by the pandemic.

The capital structure variable shows values ranging from 0.03 to 0.96, with a mean of 0.33 and a standard deviation of 0.19. There is little dispersion of values relative to the mean. The presented values allow us to conclude that the companies analyzed generally use a reasonable level of equity capital but still need external funding. We can infer that, in general, the companies demonstrate moderate financial autonomy.

Regarding profitability, it ranges from 0.01% to 20.09%, with a mean of 5.15% and a standard deviation of 4.22%. There is a significant difference between the values presented. Based on the mean, we can conclude that, in general, the companies are managing their assets effectively and generating an operational profit of 5.15%, a reasonable profit level.

GNP shows values ranging from −7.60% to 6.80%, with a mean of 2.04% and a standard deviation of 5.10%. The values highlighted for the mean and standard deviation allow us to conclude that there is significant variation in GNP growth rates, reaching both negative and positive values.

The COVID-19 dummy variable shows that 60% of the observations correspond to the years during the COVID-19 period, and 40% refer to pre- and post-pandemic periods.

Table 3 also allows for an analysis and provides an overall view of the variables, showing the variation and distribution of the data. Some variables demonstrate a large dispersion, indicating a lack of symmetry. The data from the COVID-19 variable may influence subsequent analyses, as there is a larger number of observations during the pandemic period.

5.2. Correlation Matrix

Pearson correlation is a fundamental statistical measure in data analysis. This test allows quantifying and understanding the linear relationship between two continuous variables. An appropriate representation cannot be obtained if there is no linear relationship between the variables. The Pearson correlation coefficient can range from +1 to −1, with 0 indicating no relationship between the two variables.

Table 4. Correlation Table—Liquidity Risk.

		Liquidity	Insolvency	Interest	Size	Inflation	Capital	Profitability	GNP	COVID-19
Liquidity	Pearson Correlation	1
	Sig. (2-tailed)
Insolvency	Pearson Correlation	−0.001	1
	Sig. (2-tailed)	0.993
Interest	Pearson Correlation	−0.155	0.525 **	1
	Sig. (2-tailed)	0.170	0.000
Size	Pearson Correlation	−0.265 *	−0.523 **	−0.223 *	1
	Sig. (2-tailed)	0.017	0.000	0.047
Inflation	Pearson Correlation	0.176	−0.002	0.100	0.056	1
	Sig. (2-tailed)	0.119	0.987	0.377	0.623
Capital	Pearson Correlation	0.206	−0.038	−0.335 **	−0.129	0.059	1
	Sig. (2-tailed)	0.067	0.736	0.002	0.253	0.601
Profitability	Pearson Correlation	0.250 *	0.009	−0.155	−0.286 *	0.222 *	0.387 **	1
	Sig. (2-tailed)	0.025	0.937	0.169	0.010	0.048	0.000
GNP	Pearson Correlation	0.133	−0.018	0.045	0.035	0.666 **	0.051	0.232 *	1
	Sig. (2-tailed)	0.240	0.875	0.694	0.756	0.000	0.654	0.038
COVID-19	Pearson Correlation	−0.013	−0.010	−0.102	−0.009	−0.540 **	−0.033	−0.252 *	−0.444 **	1
	Sig. (2-tailed)	0.909	0.931	0.366	0.939	0.000	0.774	0.024	0.000

*. The correlation is significant at the level 0.05 (2-tailed). **. The correlation is significant at the level 0.01 (2-tailed).

represents the Pearson correlations of the liquidity risk model. By analyzing the correlations presented, it is possible to verify that there is no issue of multicollinearity, as no correlation exceeds 80%. Multicollinearity occurs when two or more variables are highly correlated in a regression model, which can affect the quality of the results and make the interpretation of results difficult.

Regarding the independent variables, inflation, capital structure, profitability, and GNP exhibit positive correlations, as their correlation coefficients are greater than 0. In contrast, the other independent variables—size and COVID-19—show negative correlation values.

The independent variables are not significant, indicating that there is no evidence that they directly affect the liquidity risk of the companies. The statistically significant and highest correlation coefficients in the model belongs to the size and profitability (r = −0.265 and r = 0.250, respectively).

6. Regression Results

6.1. Liquidity Risk

Table 5. Regression Table—Liquidity Risk.

Description	Model 1		Model 2		Model 3		Model 4		Model 5
	β	p-Value	β	p-Value	β	p-Value	β	p-Value	β	p-Value
(Constant)	−29.292	0.000	−28.730	0.000	−27.653	0.000	−28.085	0.000	−28.416	0.000
COVID-19 * Size	−0.024	0.527
COVID-19 * Capital			−0.003	0.994
COVID-19 * Profitability					−3.019	0.179
COVID-19 * GNP							13.349	0.211
COVID-19 * Inflation									7.119	0.211
COVID-19	0.476	0.557	−0.033	0.842	0.117	0.419	−0.485	0.193	−0.154	0.248
Size	1.379	0.000	1.354	0.000	1.300	0.000	1.342	0.000	1.342	0.000
Capital	2.859	0.000	2.813	0.000	2.937	0.000	2.769	0.000	2.769	0.000
Profitability	−1.091	0.493	−1.318	0.401	−0.979	0.528	−1.193	0.438	−1.193	0.438
GNP	0.487	0.623	0.494	0.619	0.841	0.406	−14.175	0.228	−0.827	0.565
Inflation	−2.972	0.127	−2.825	0.145	−2.863	0.134	4.532	0.462	−2.588	0.178
Firm fixed effects	Included		Included		Included		Included		Included
R-Squared	0.651		0.648		0.659		0.658		0.658
F	12.069		11.965		12.439		12.374		12.374
p-value	0.000		0.000		0.000		0.000		0.000
Durbin-Watson	2.082		2.047		1.849		2.049		2.049
Panel Data Diagnostic
F	14.354		14.215		14.764		14.398		14.398
p-value	0.000		0.000		0.000		0.000		0.000
Breusch-Pagan	9.827		9.999		11.102		10.364		10.364
p-value	0.002		0.002		0.000		0.001		0.001
Hausman	103.238		101.590		100.928		102.091		102.091
p-value	0.000		0.000		0.000		0.000		0.000

allows us to analyze and interpret the results obtained from the different models for liquidity risk. In this sense, it is possible to conclude that all regression models are statistically significant since all of them present an F statistical test with a p-value < 0.01.

Regarding R², this allows us to measure how much the independent variables explain the variations in the dependent variable. The values presented for this statistical measure ranges from 64.8% to 65.9%.

The Durbin-Watson test is used to test the presence of autocorrelation in the sample. Autocorrelation means the correlation of a variable with itself over time. With the data presented, it is possible to conclude that there are any potential problems of autocorrelation.

None of the interest variables (COVID-19, COVID-19 * Size, COVID-19 * Capital, COVID-19 * Profitability, COVID-19 * GNP, and COVID-19 * Inflation) present statistical significance, allowing us to conclude that they do not influence liquidity risk.

This section may be divided by subheadings. It should provide a concise and precise description of the experimental results, their interpretation, and the experimental conclusions that can be drawn.

6.2. Insolvency Risk

Table 6. Regression Table—Insolvency Risk.

Description	Model 1		Model 2		Model 3		Model 4		Model 5
	β	p-Value	β	p-Value	β	p-Value	β	p-Value	β	p-Value
(Constant)	−8.449	0.266	−4.398	0.565	−4.766	0.542	−4.129	0.592	−4.313	0.572
COVID-19 * Size	−0.168	0.136
COVID-19 * Capital			−0.177	0.846
COVID-19 * Profitability					0.781	0.874
COVID-19 * GNP							7.411	0.751
COVID-19 * Inflation									3.953	0.751
COVID-19	3.595	0.137	0.052	0.885	−0.044	0.889	−0.256	0.753	−0.072	0.805
Size	0.422	0.230	0.240	0.499	0.259	0.477	0.239	0.500	0.239	0.500
Capital	1.245	0.312	1.017	0.460	0.880	0.492	0.889	0.483	0.889	0.483
Profitability	10.981	0.002	9.317	0.008	9.305	0.008	9.461	0.007	9.461	0.007
GNP	−2.935	0.160	−2.866	0.187	−2.979	0.184	−11.033	0.668	−3.622	0.253
Inflation	−2.180	0.590	−1.141	0.784	−1.130	0.786	2.945	0.827	−1.008	0.810
Firm fixed effects	Included		Included		Included		Included		Included
R-Squared	0.218		0.156		0.155		0.156		0.156
F	60.741		56.026		56.014		56.092		56.092
p-value	0.000		0.000		0.000		0.000		0.000
Durbin-Watson	2.256		2.058		2.058		2.059		2.059
Panel Data Diagnostic
F	60.932		56.113		55.960		56.184		56.184
p-value	0.000		0.000		0.000		0.000		0.000
Breusch-Pagan	119.475		119.163		119.672		119.358		119.358
p-value	0.000		0.000		0.000		0.000		0.000
Hausman	108.491		108.166		107.218		107.465		107.465
p-value	0.000		0.000		0.000		0.000		0.000

allows us to conclude that all regression models for insolvency risk are statistically significant, since all of them present an F statistical test with a p-value < 0.01.

The R² ranges from 15.5% to 21.8%. In addition, it is possible to verify that potential problems of autocorrelation is minimal.

None of the interest variables (COVID-19, COVID-19 * Size, COVID-19 * Capital, COVID-19 * Profitability, COVID-19 * GNP, and COVID-19 * Inflation) present statistical significance, allowing us to conclude that they do not influence insolvency risk.

6.3. Interest Rate Risk

Table 7. Regression Table—Interest Rate Risk.

Description	Model 1		Model 2		Model 3		Model 4		Model 5
	β	p-Value	β	p-Value	Β	p-Value	β	p-Value	β	p-Value
(Constant)	0.053	0.717	0.004	0.980	−0.004	0.979	−0.036	0.094	−0.018	0.094
COVID-19 * Size	0.002	0.137
COVID-19 * Capital			−0.009	0.609
COVID-19 * Profitability					0.009	0.925
COVID-19 * GNP							-0.728	0.799
COVID-19 * Inflation									-0.388	0.898
COVID-19	−0.052	0.117	0.000	0.985	−0.003	0.571	0.022	0.152	0.004	0.506
Size	0.001	0.841	0.001	0.908	0.001	0.861	0.002	0.795	0.002	0.795
Capital	0.006	0.810	0.015	0.554	0.010	0.685	0.012	0.592	0.012	0.592
Profitability	0.001	0.984	0.016	0.797	0.019	0.766	0.013	0.829	0.013	0.829
GNP	−0.023	0.565	−0.022	0.581	−0.025	0.559	0.777	0.105	0.048	0.405
Inflation	0.068	0.385	0.054	0.496	0.054	0.495	−0.348	0.165	0.041	0.598
Firm fixed effects	Included		Included		Included		Included		Included
R-Squared	0.088		0.056		0.052		0.098		0.098
F	6.361		6.057		6.019		6.456		6.456
p-value	0.000		0.000		0.000		0.000		0.000
Durbin-Watson	1.340		1.347		1.349		1.288		1.288
Panel Data Diagnostic
F	6.019		5.991		5.946		6.247		6.247
p-value	0.000		0.000		0.000		0.000		0.000
Breusch-Pagan	34.263		31.895		31.728		33.527		33.527
p-value	0.000		0.000		0.000		0.000		0.000
Hausman	104.875		109.072		5.899		106.092		106.092
p-value	0.000		0.000		0.000		0.000		0.000

presents the results obtained from the interest rate risk regression. In these models, it is possible to verify that all regression models are significant (p-value < 0.001), contrary to what was previously concluded.

Regarding R², this allows us to determine to what extent the variations in the dependent variable are justified by the independent variables. The values presented for this statistical measure ranges from 5.2% and 9.8%.

The Durbin-Watson test is used to verify the presence of autocorrelation in the sample. With the data presented, it is possible to conclude that potential problems of autocorrelation are minimal. None of the interest variables (COVID-19, COVID-19 * Size, COVID-19 * Capital, COVID-19 * Profitability, COVID-19 * GNP, and COVID-19 * Inflation) present statistical significance, allowing us to conclude that they do not influence interest rate risk.

7. Discussion of Results

This chapter presents and analyzes the results obtained. Results indicate that there is a positive and significant (p-value < 0.01) between liquidity risk and size. Regarding the relationship between the variable COVID-19 * Size and liquidity risk, it can be concluded that the relationship is negative and not significant (p-value > 0.1). Therefore, H1, which hypothesized that size would have a moderating effect between liquidity risk and the COVID-19 period, is not confirmed. These results align with those found in Heitmann et al. (2023) found that size positively affects liquidity risk. However, other research presents opposite results. El-Chaarani (2023) concluded that the size of banks is not significant.

Regarding insolvency risk, size exhibits a positive but not significant relationship, as observed with liquidity risk. This result does not align with Smaoui et al. (2020), who found that the size variable had a significant negative effect on insolvency risk. In the interest rate risk model, size shows a positive but not significant relationship (p-value > 0.1) in all presented models. Results also indicate that the variable COVID-19 * size is not statistically significant with insolvency risk and interest rate risk. Thus, H1 is not confirmed for insolvency risk and interest rate risk. According to agency theory, agency conflicts may lead managers to make decisions focused on their interests, such as bonuses, causing the company to invest in risky projects, which could consequently increase interest rate risk.

The capital structure variable presents a positive and significant relationship with liquidity risk (p-value < 0.01). With insolvency risk, this variable shows a positive but also non-significant relationship (p-value > 0.1). For interest rate risk, capital structure reveals a positive but not significant relationship (p-value > 0.1). Moreover, regarding the relationship between the variable COVID-19 * Capital and liquidity, insolvency and interest rate risk, is not significant (p-value > 0.1)Thus, it is possible to conclude that H2 is not supported. These results align with those found by El-Chaarani (2023), who concluded that capital structure does not affect financial performance before or during the pandemic. According to trade-off theory, the relationship between capital structure and interest rate risk is negative. Companies should seek a balance that maximizes their value while minimizing interest rate risk.

Regarding GNP and inflation, these variables have no impact or relationship with any of the dependent variables. In this sense, hypotheses 4 and 5 are not confirmed, as any of these macroeconomic variables does not moderate the relationship between risk levels and the COVID-19 period. The results do not align with those found by other authors. Kaya (2022) concluded that GNP showed a negative sign. However, Smaoui et al. (2020) found that GNP had a positive effect on insolvency risk. Regarding inflation, Kaya (2022) and Smaoui et al. (2020) concluded that it had a negative and significant effect on insolvency and interest rate risk during the pandemic.

As for profitability, it shows a negative but not significant (p-value > 0.1) relationship with liquidity risk. Regarding insolvency risk it shows a positive and significant relationship (p-value < 0.01). In interest rate risk, this variable presents a negative but not significant relationship (p-value > 0.1). Moreover, the relationship between the variable COVID-19 * Profitability and liquidity, insolvency and interest rate risk, is not significant (p-value > 0.1). Therefore, H3 is not validated.

Finally, it can also be concluded that the variable COVID-19 pandemic had no impact (p-value > 0.1) on any of three financial risk considered (liquidity, insolvency and interest rate risks). Therefore, hypotheses 6 to 8 cannot be supported, as the results do not indicate a positive relationship between risk levels and the COVID-19 period. The results obtained for liquidity risk align with the study by El-Chaarani et al. (2023). However, the study by Suardi et al. (2022) found that the pandemic increased liquidity risk and the vulnerability of individual liquidity in the stock market. Regarding interest rate risk, conclusions opposite to those reached in this study were also obtained. In the study by Feng et al. (2021), it was found that the COVID-19 pandemic led to negative and significant behavior not only in interest rates but also in exchange rates.

In the interest rate risk models the variations in adjusted R² are small, indicating that the variables included in the additional models do not contribute to explaining the hypotheses described.

According to

Table 8. Hypotheses.

	Liquidity Risk	Insolvency Risk	Interest Rate Risk
Size * COVID-19 (H1)	Not supported	Not supported	Not supported
Capital Structure * COVID-19 (H2)	Not supported	Not supported	Not supported
Profitability * COVID-19 (H3)	Not supported	Not suported	Not supported
GNP * COVID-19 (H4)	Not supported	Not supported	Not supported
Inflation * COVID-19 (H5)	Not supported	Not supported	Not supported
COVID-19 (H6 a H8)	Not supported	Not supported	Not supported

, it is evident that any of the hypotheses related to the independent variables size, capital structure, and profitability cannot be validated when associated with any of the three financial risks considered (liquidity, insolvency, and interest rate risk), as they present a p-value lower than 10%. The same is also true for the hypotheses related to the country-level variables GNP and inflation.

8. Conclusions

This study aimed to analyze the disclosure of various financial risks during the COVID-19 pandemic and calculate the determinants of these risks. The econometric analysis of the data revealed several insights regarding the contribution of the variables in explaining the dependent variables.

Main findings indicate that COVID-19 did not influence the level of financial risks and this relationship is not moderated by either firm-level variables (e.g., size, capital structure, and profitability) nor country-level variables (e.g., GNP and inflation).

However, liquidity risk is positively associated with size and capital structure. Insolvency risk is positively associated with profitability.

It is also concluded that the GNP and inflation variables do not show significance in any models. Throughout the models, the sign of these variables is not constant, and none of the tested hypotheses are confirmed.

This study provides valuable insights into the determinants of financial risks and the impact of COVID-19 on risk disclosure practices among companies listed on Euronext Lisbon. However, several limitations must be acknowledged, offering pathways for future research.

One notable limitation lies in the low R-squared values observed in the regression models for interest rate risk, indicating a weak explanatory power. This suggests that the variables included in the study may not fully capture the factors influencing financial risks. Future research could address this limitation by exploring additional explanatory variables, such as industry-specific factors, governance practices, or external economic shocks, to enhance the robustness of the models.

Additionally, the sample size of 16 companies, while reflective of the Euronext Lisbon market, limits the generalizability of the findings. Expanding the dataset to include a larger number of firms, or conducting cross-country comparisons with similar financial markets, could provide more comprehensive insights. Larger sample sizes would also improve the statistical power of the analyses, enabling more precise estimates of the relationships between variables.

Finally, while the redefinition of the COVID-19 dummy variable aimed to capture the pandemic’s timeline accurately, future research could refine this approach further by considering quarterly data or introducing interaction terms to capture temporal dynamics more granularly. This would provide a more detailed understanding of how companies adjusted their risk disclosure practices over time.

Acknowledging these limitations, this study serves as a foundation for future research, which can build upon these findings by incorporating a broader range of variables, more extensive datasets, and advanced methodological approaches to further illuminate the complexities of financial risk disclosure and management.