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Article

The Impact of Financial Derivatives on European Bank Value and Performance

by
Bassam Al-Own
1,*,
Mohannad Obeid Al Shbail
2,*,
Zaid Jaradat
3 and
Ghaith N. Al-Eitan
1
1
Finance & Banking Department, School of Business, Al al-Bayt University, Mafraq P.O. Box 25113, Jordan
2
Accounting Department, School of Business, Al al-Bayt University, Mafraq P.O. Box 25113, Jordan
3
Auditing and Business Low Department, School of Business, Al al-Bayt University, Mafraq P.O. Box 25113, Jordan
*
Authors to whom correspondence should be addressed.
Risks 2026, 14(2), 39; https://doi.org/10.3390/risks14020039
Submission received: 18 December 2025 / Revised: 9 February 2026 / Accepted: 10 February 2026 / Published: 12 February 2026
(This article belongs to the Special Issue Financial Investment, Derivatives Hedging, and Risk Management)
Versions Notes

Abstract

Using a panel dataset of 385 European bank-year observations covering the 2012 to 2022 period, this study aimed to investigate the impact of derivatives on bank value and performance. We used bank-level panel data and conducted several multivariate statistical analyses, i.e., ordinary least squares (OLS), random-effects, and feasible generalized least squares (FGLS) regressions, to examine the ways in which using derivatives for different purposes influences bank value and performance. The regression results indicated a positive and significant association between hedging derivatives and bank performance, while trading derivatives had a negative effect on bank performance and value. Furthermore, the findings suggest that using such derivatives for hedging does not enhance value. Regarding the practical implications of this study and banking sector soundness, financial market regulators and policymakers should be cautious of the potential negative consequences of extensive trading derivative use. In particular, maintaining an acceptable level in this regard is essential to ensuring that the costs of engaging in derivative markets do not surpass their benefits. Hedging through derivatives may not translate into higher bank value, thus managers should justify to investors how such hedging derivatives enhance shareholder wealth. Additional research could focus on whether using derivatives in the banking industry offers any palpable advantage in the intermediate to long term; whether their use by non-financial organizations has different implications that than of financial firms; and the extent to which such financial instruments are useful for enhancing bank value.

1. Introduction

Derivatives are used by most large financial institutions to reduce their exposure to financial risks (Lau 2016; Brewer et al. 1996). This instrument was introduced and continues to act as an effective and efficient tool for financial risk management. However, banks now also extensively use derivatives to support speculation activities (Keffala 2021; Whidbee and Wohar 1999).
The use of financial derivatives increased dramatically in the last two decades. Abdel-Khalik and Chen (2015) highlighted that the global notional derivative value increased by more than $600 trillion between 1995 and 2012, soaring from $57.5 trillion to $696 trillion. More recently, the Bank for International Settlements further reported that the growth in derivative notional amounts increased from $72.1 trillion in 1998 to $715 trillion in 2023.1
This important financial instrument’s role in and impact on risk management have long been the subject of debate among academics and financial regulators, even before the onset of the most recent financial crisis. For instance, in 2002, Warren Buffett, the co-founder and the CEO of Berkshire Hathaway, remarked that derivatives are “time bombs, for both parties that deal in them and the economic system”. In his view, derivatives are “financial weapons of mass destruction, carrying dangers that, while now latent, are potentially lethal” (Buffett 2003). In contrast, Alan Greenspan, the US Federal Reserve Board Chairman, suggested that they play an important role in developing “a far more flexible, efficient and resilient financial system” (Greenspan 2005).
Over the past two decades, derivative use has continued to divide policymakers and market participants. Much of the criticism about their impact on performance occurred in the aftermath of the last financial crisis. Derivatives were held responsible for the many significant financial losses across blue-chip firms such as Allied Lyons, Metallgesellschaft, and AIG during the 1990s (Bachiller et al. 2021). Nevertheless, many studies have since produced results that refute the claim that financial derivatives were the main contributor to the financial crisis, providing evidence that their use can act as an effective risk management tool during times of crisis (Bedendo and Bruno 2012).
It is important that bank stakeholders understand why and how managers use derivatives and how this may consequently affect firm value and financial performance. Risk-averse managers are often responsible for deciding to use derivatives (Lau 2016; Zamzamin et al. 2021; Nguyen and Faff 2002). Policymakers and banking sector regulators must also evaluate the potential role of financial derivatives to maintain sound financial systems and market stability. Despite their potential importance, previous studies have paid less attention to the effect of derivatives on banking, despite the sector’s important role in supporting and maintaining a healthy economic system.
The existing literature on risk management does show an obvious schism regarding the impact of derivative usage on firm value. Proponents highlight evidence painting derivatives as a tool that effectively hedges risk and increases performance. They argue that, by reducing agency costs (Froot et al. 1993) or increasing firm leverage (Leland 1998), derivatives play a clear role in reducing both tax liability and potential bankruptcy costs (Smith and Stulz 1985; Stulz 1996). Many studies provide empirical evidence that firms can use derivatives to generate income and enhance performance (Bartram et al. 2011; Bazih and Vanwalleghem 2021). However, those holding the opposite view suggest that excessive use is mainly led by manager risk aversion and can damage firm value and performance (Belghitar et al. 2013; Butt et al. 2024). The literature thus shows that, due to their inefficacy in reducing risk when used for speculation activities, hedging activities reduce firm value (Adam et al. 2017; Hagelin et al. 2007). There therefore appears to be little agreement on whether the use of financial derivatives does in fact lead to improved firm performance (Bachiller et al. 2021).
In this study, the main objective is therefore to investigate any linkage between derivative use and bank value and performance. In order to achieve this objective, a dataset that includes 385 European bank-year observations was sourced. The sample period covered the years 2012 to 2022 inclusive, and all firms were drawn from the banking sector, as this is commonly considered the main, most intensive participant in derivative markets (Titova et al. 2020). In addition, this work differentiates between hedging and speculation derivatives, meaning that the results demonstrate how the extent and purpose of their use both influence bank performance. This study offers important policy implications and suggests that the potential risk from trading derivatives can negatively influence bank performance. More involvement in hedging derivatives may lead to better accounting outcome; however, monitoring the extent of trading derivative use is essential in maintaining financial sector soundness and stability. The costs of using derivatives should not surpass their benefits.
To the best of the researchers’ knowledge, this is the first study to analyze the impact of derivative usage on bank performance, using both notional and fair derivative values after the most recent financial crisis. While an increasing number of studies are investigating the impact of derivative usage on firm performance, the majority of these either use a dummy variable to measure financial derivatives or have focused exclusively on US banks without distinguishing between the various purposes of such measures. In this study, we take advantage of the improved derivative disclosure post-crisis, unpacking the impact of derivatives according to their reported purpose to address this research gap. Additionally, in order to provide constructive results, we use both notional and fair derivative values. We assume that the separation between the effect of hedging and trading derivatives on bank performance contributes significantly to risk management and derivative research. Indeed, using a European banking sample to examine the differences and similarities in the purposes of derivative use can influence bank performance is vital issue.
The next section presents a more comprehensive literature review, while Section 3 outlines the research method and describes the data, Section 4 reports the results, and Section 5 concludes the study.

2. Literature Review

Based on (Modigliani and Miller 1958) capital market theory, firm value ought to be insensitive to corporate risk management activities. However, more recently, some scholars have recommended that, in the presence of market friction, using derivatives might be beneficial to firm performance where agency costs, tax liabilities, or the cost of financial distress are significant (Smith and Stulz 1985; Aretz and Bartram 2010; Titova et al. 2020). In practice, the rapid and growing use of financial derivatives has mainly resulted from an increased interest across the banking sector in managing and reducing the exposure risk inherent to banking activities (Huan and Parbonetti 2019). However, speculation represents another important and widespread reason that banks use derivatives (Miloș and Miloș 2022).
Studies on risk management explain how derivatives enable risk-averse executives to manage cash flow and firm value volatility (Aretz and Bartram 2010). This implies that derivative usage effectively improves firm performance, contributing to higher shareholder wealth and enhancing firm value. However, previous empirical studies on the impact of such derivatives on bank performance before the financial crisis have documented mixed results.
Compared to those focusing on non-financial firms, existing empirical studies focusing on the impact of derivatives on financial institution performance appear to be less extensive. This can be explained by an inability to distinguish between the hedging and speculative use of derivatives, at least until recently (Titova et al. 2020). Earlier studies focused on non-financial firm samples certainly documented mixed results. Many prior empirical studies found that derivative usage has a positive influence on firm performance and value (Bartram et al. 2009; Ayturk et al. 2016; Lau 2016; Zamzamin et al. 2021; Alam and Gupta 2018). However, other scholars have provided evidence undermining this positive association (Belghitar et al. 2013; Ben Khediri 2010; Yildiz Savas and Kapusuzoglu 2020; Butt et al. 2024; Fauver and Naranjo 2010; Wen et al. 2021). Furthermore, while there is extensive prior empirical research examining the impact of derivatives on performance in non-financial sectors, few studies have investigated similar effects in the banking industry; those that do exist have demonstrated similarly ambiguous results.
Several existing studies in this area have also only focused on the US banking industry. Said (2011) demonstrated that the US banks’ derivative use is associated with better bank performance. Ghosh (2017) further illustrated the opposing impact of derivative usage on US commercial banks’ profits in post-crisis periods; however, their results did reveal that total derivative usage was linked to improved ROAs before and during the crisis. Li and Yu (2010) showed that US banks’ decisions to use derivatives are associated with better financial performance, defined primarily by ROA. Shen and Hartarska (2018) showed that for small community banks in the US, profitability’s sensitivity to credit risk is lower for derivative users.
A few studies have also examined how the decision to use derivatives may influence European banking sector firms. For example, Titova et al. (2020) developed panel data models to examine the impact of derivatives on European bank performance over the 2005 to 2010 period. Their results showed a positive association between hedging derivatives, as measured by both notional and firm value. Nevertheless, this positive impact turned negative in the post-crisis period, raising concerns around both trading and hedging derivatives. Furthermore, they highlighted that heavy involvement in derivative transactions reduces bank value. Using the generalized method of moments (GMMs), Miloș and Miloș (2022) concluded that derivative usage in the European financial sector negatively affects market value. They explained that participants in European markets react negatively to a high level of derivatives. However, focusing exclusively on the role of trading derivatives, Chang et al. (2018) suggested that their usage by European banking sector firms led to enhanced profitability between 2004 and 2008.
In a large-scale, global study of the banking sector, Purnanandam (2007) used panel data from 8000 banks to show that commercial banks use interest rate derivatives to reduce the cost of financial distress and maintain smooth cash flows. Bazih and Vanwalleghem (2021) also showed that financial derivative usage in the emerging market sector undermines bank value, as measured using Tobin’s Q. More recently, Keffala (2021) used GMM to address the impact of derivatives on 32 Islamic banks’ performance between 2007 and 2017. The results indicated that Islamic banks are important participants in the derivative market, preferring to use financial derivatives for trading rather than hedging purposes. In terms of Islamic bank performance, the results also revealed positive but weak impacts from options and swap contracts. However, a negative association was found between forwards and bank performance.
In contrast to international empirical evidence, country-specific studies on the role of derivatives in shaping bank performance have been mixed and contradictory. For instance, Al Fazari et al. (2022) showed that, for a sample of UK financial firms, hedging derivatives can enhance bank value, as measured by Tobin’s Q. However, they found no association between derivative use and financial performance, measured using ROE. They also reported a negative impact of hedging derivatives on market performance, measured using firm stock returns.
In contrast, Taşkın and Sarıyer (2020) noted that derivative usage diminishes profitability within the Turkish banking sector, and other studies have discussed how it may contribute to higher bank default risk (Instefjord 2005; Li and Marinč 2014; Al-Own et al. 2018). In their sample, Koski and Pontiff (1999) also highlighted a lack of difference between derivative users and non-users in terms of both risk and performance.
Overall, the literature offers no decisive conclusion regarding the impact of financial derivative use on firm performance. There is a wide array of studies available, but their results often conflict with each other, particularly regarding whether derivatives enhance firm performance and value in practice. In addition, previous studies mainly focused on the impact of financial derivatives on the performance of non-financial firms. Studies conducted in the European context do suggest that this market exhibits important differences compared to that of the U.S., as well as emerging markets, which could explain some of the conflicting results within the literature.
The current study thus seeks to address several gaps in the literature. Its focus on the European banking sector is important for its uniqueness, as this is considered one of the most active geographical markets for derivatives. Additionally, many previous studies have measured derivatives by means of a binary variable (Koski and Pontiff 1999; Lau 2016; Bartram et al. 2009), while, in this study, we use both notional and fair value. Many prior studies have also focused on the general influence of derivatives on performance without considering the purpose of their use in each case (Miloș and Miloș 2022; Lau 2016). To produce more granular results, this study thus focuses on the post-crisis period and unpacks the impact of derivatives based on their reported purpose (hedging or trading).

Hypothesis Development

H1. 
Use of hedging derivatives (HD) and bank performance.
Risk management theory predicts that derivatives employed for hedging purposes should improve firm outcomes by reducing the volatility of cash flows and asset values. Hedging can lower the expected costs of financial distress, support more stable investment and lending policies, and reduce the need to hold costly precautionary capital or liquidity buffers (Froot et al. 1993; Leland 1998; Stulz 1996). For banks, which are naturally exposed to interest rates, exchange rates, and credit risks, derivatives provide tools for managing these exposures more efficiently than through balance-sheet adjustments alone. This stabilization can translate into smoother earnings, enhanced expected performance, and, ultimately, higher market valuations.
Though mixed overall, empirical evidence provides support for the value-enhancing role of hedging derivatives when used prudently and transparently. Studies of both non-financial firms and banks show that hedging activities can be associated with higher Tobin’s Q, improved profitability, and lower sensitivity to risk exposure (Bartram et al. 2009; Titova et al. 2020; Shen and Hartarska 2018). Even where results are not uniformly positive, they generally suggest that well-designed hedging strategies help contain downside risk without materially sacrificing upside potential. In the European banking context, which is characterized by intensive derivative use and sophisticated risk management frameworks, hedging derivatives are therefore expected to contribute positively to bank performance and value. Thus, we propose the following:
H1. 
The use of hedging derivatives is positively associated with banks’ value and performance.
H2. 
Use of trading derivatives (TD) and bank performance/value.
Unlike hedging derivatives, trading derivatives are primarily employed to generate income from market-making, arbitrage, and speculative positions. In theory, trading activities can enhance bank performance by exploiting informational advantages, client flows, and market inefficiencies, thereby increasing non-interest income and diversifying revenue sources. In periods of normal market conditions and with adequate risk controls, such activities may contribute positively to profitability and, by extension, to bank value (Chang et al. 2018). Trading derivatives can also provide liquidity to markets and deepen client relationships, which may have indirect benefits for franchise value.
However, trading derivatives also expose banks to substantial market, model, and counterparty risks, which can amplify losses during periods of stress. Several studies highlight that heavy involvement in derivative trading may increase earning volatility, raise default risk, and erode firm value, particularly when positions are large relative to capital or when risk management systems are weak (Instefjord 2005; Li and Marinč 2014; Titova et al. 2020; Miloș and Miloș 2022). Evidence from emerging markets and crisis periods similarly points to a negative or ambiguous association between speculative derivative use and bank value (Purnanandam 2007; Bazih and Vanwalleghem 2021). Given these concerns, and market participants’ growing scepticism regarding complex trading books, this study expects that trading derivatives are, on balance, detrimental to bank value and performance. Accordingly,
H2. 
The use of trading derivatives is negatively associated with banks’ value and performance.

3. Research Method

3.1. Data

Based on a panel dataset formed from financial statement information, we conducted empirical analysis of the impact of derivative usage on European bank performance. The bank sample included selected publicly listed European banking institutions registered in the main European market indices and members of the European Banking Association. Publicly listed banks were selected due to the fact that public companies are required to disclose comprehensive detailed information about their use of financial instruments such as derivatives and other risk management operations on a regular basis. Since 2006, disclosure practices regarding derivative-based financial instruments and their purpose have improved (Al-Own et al. 2018), influencing the usefulness of such studies.
Dependent and independent variable data for the 2012 to 2022 period were collected from consolidated annual reports, from information available on companies’ official websites or from stock exchange data for each country. The DataStream database was also used to collect data for the control variables, measured as shown in Table 1.
The year 2012 was selected as the starting year for several reasons. The first is that the latest amendments to the international accounting standard board, International Financial Reporting Standard 7 (IFRS 7) and International Accounting Standard 39 (IAS 39) in 2010, came into effect only after the second half of 2011.2 The adoption of international Accounting Standard IAS 39 “Financial Instruments: Recognition and Measurement”, together with its subsequent amendments, encouraged European banks to disclose further information regarding the classification of derivative usage in relation to hedging instruments and the fair value of their derivative contracts. Moreover, the implementation of IAS 39 resulted in a notable improvement in disclosure practices regarding hedging derivatives compared to previous years (Bahgat 2002). Compliance with IFRS 7 also forced publicly listed firms to provide more information, enhancing risk management disclosure transparency.
Additionally, the sample period start date of 2012 allowed us to avoid various macroeconomic problems, such as the 2008 financial and European debt crises. European banks experienced several downward trends during these two significant crises, including dramatic declines in deposit levels, market capitalisation, total asset book value and profitability (Weigand 2016). These two severe financial crises had a serious negative impact on the European banking industry until at least 2011 (Ferreira 2020). However, the use of more than 10 years of data was deemed necessary to facilitate better comprehension and develop a more well-rounded perspective regarding the trends, patterns and long-term dynamics of the market, as this is more representative than any short-term period (Ananzeh et al. 2024). Overall, adopting a decade-long timespan offered a higher level of consistency for quantitative analysis.
The final sample featured 35 banks from 17 European countries: Austria, Belgium, Denmark, Finland, France, Germany, Hungary, Italy, Malta, the Netherlands, Norway, Poland, Portugal, Spain, Sweden, Switzerland, and the United Kingdom. Following earlier studies, banks from the United Kingdom were included, as the country was part of the European Union for almost all of the sample period (Miloș and Miloș 2022). To ensure more reliable findings and better focus the analysis, certain countries with macroeconomic problems and shocks were excluded. Banks that underwent mergers and those with missing financial statement information regarding derivative contracts were also excluded. The final dataset thus consisted of 385 European bank-year observations covering the 2012–2022 period. Appendix A shows the names of the selected banks.
Financial institutions within the banking sector were selected as a focus, as these represent the major, dominant participants in derivative markets (Al-Own et al. 2018; Chang et al. 2018). In addition, the financial sector was one of earliest sectors to comply with IFRS adoption (Yamani et al. 2021). Similarly, European countries deserve specific attention; due to similarities in many economical and financial aspects, such as financial regulation, currency, and political circumstances, they constitute a unique class.
During data collection, country-specific regulation and disclosure approaches to implementing international accounting standards represented a major challenge. The adoption of international accounting standards in European countries nevertheless allowed for significant differences among financial institutions.

3.2. Estimation Method

A panel data approach was used to estimate how derivative use can influence bank performance. Panel data regression analysis was selected as the most suitable estimation method to ascertain the impact of derivative usage on performance in the financial sector as such estimations allow for the analysis of multiple observations over a long period, improving accuracy by mitigating the effect of potential omitted variable bias. Additionally, panel data regression can control for the complex relationships between variables seen in industries such as the banking sector (Von Tamakloe et al. 2023).
Panel data for 35 European banks from 2012 to 2022 were analyzed to examine the impact of using derivatives on firm value and performance. More specifically, three regression models were run to account for the following dependent variables: Tobin’s Q, which measures firm value, and ROA and ROE, both of which are used to measure financial performance. The regression models were structured as follows:
y i t = α + β x i t + ε i t
where y is the dependent variable, x is the independent variable, α and β are the coefficients, i is the index for banks, t is the time index, and ε is the error term. To test the extent to which derivative usage influences bank performance, following prior research, a model was estimated in which the dependent variables were ROE and Tobin’s Q and the main explanatory variable of interest was derivative usage (Shen and Hartarska 2013; Lau 2016; Zamzamin et al. 2021; Al Fazari et al. 2022; Ghosh 2017).
To examine whether changing the purpose of derivative use affects bank performance differently, usage was split into hedging and trading derivatives. Econometric models based on panel data were thus developed:
P e r f o m a n c e i , t = α + β 1 D E R i , t − 1 + β 2 O I M i , t + β 3 N P M i , t + β 4 A T i , t + β 5 L E V i , t + β 6 G R O i , t + β 7 S Z i , t + ε i , t
V a l u e i , t = α + β 1 D E R i , t − 1 + β 2 O I M i , t + β 3 N P M i , t + β 4 A T i , t + β 5 L E V i , t + β 6 G R O i , t + β 7 S Z i , t + ε i , t
with performance and value measured using ROE and Tobin’s Q ratios, respectively. The proxy for derivatives was continuous variables measured using derivatives’ notional value based on the purpose of their use (hedging or trading) for the fiscal year, divided by total assets. Following prior research (Titova et al. 2020; Li and Marinč 2014; Keffala 2021; Bazih and Vanwalleghem 2021; Ghosh 2017), the ratio of notional principal amount scaled by total assets was used to measure the extent of derivative usage for each purpose. Moreover, as the main dependent variables used to measure bank performance were calculated as ratios in percentage points, we considered it more consistent to use an independent variable that is also expressed as a ratio to avoid size-related bias (Titova et al. 2020). Using the ratio of derivative contract notional value (hedging and trading) to total assets thus enabled us to capture the degree of financial derivative involvement of the European banks examined (Ben Khediri 2010; Li and Marinč 2014; Zamzamin et al. 2021).
Following prior research on derivatives and firm performance (Bartram et al. 2009; Lau 2016; Titova et al. 2020; Al Fazari et al. 2022), we include a set of control variables that are theoretically and empirically related to bank performance and value. Operating income margin (OIMit) and net profit margin (NPMit) capture operating efficiency and overall profitability, which are directly linked to both market valuation and accounting-based performance. The asset turnover ratio (ATit) controls for how efficiently banks use their assets to generate revenue, thereby isolating the incremental effect of derivative usage from differences in business efficiency.
We also control for leverage (LEVit), growth opportunities (GROit), and size (SZit), which are standard determinants of firm value and risk. Measured as the sum of current and long-term debt scaled by total assets, leverage reflects the bank’s capital structure and risk profile, both of which can influence the decision to use derivatives and performance outcomes. Growth opportunities are proxied by the natural logarithm of the market-to-book value of assets (GROit), capturing future investment prospects that may simultaneously affect derivative policies and valuation. Measured as the natural logarithm of total sales, bank size (SZit) controls for scale effects, diversification, and information environment, all of which prior studies show are associated with derivative use and firm value. The inclusion and measurement of these control variables follow established practice in derivative performance research (e.g., Bartram et al. 2009; Lau 2016; Titova et al. 2020; Al Fazari et al. 2022).
In all models, a panel regression framework was adopted, with the decision between fixed and random effect models being made based on the Hausman test results. This study also utilized a multiple regression model to investigate how the extent of derivative use for hedging or trading might influence firm performance. To achieve this, a Pooled Ordinary Least Squares (OLS) regression approach was adopted, as this approach can address heteroscedasticity and autocorrelation issues (Ananzeh et al. 2024). The model also included a dummy to control for country and time effects.

4. Empirical Results and Discussion

4.1. Descriptive Statistics

Table 2 shows the variables’ descriptive statistics. This study developed a panel analysis of 385 bank-year observations for the 2012 to 2022 period. Bank performance, as measured by ROE, varied from as low as −5.30% to a maximum of 13.80%. The mean ROE percentage was 7.16%, similar to those reported by Miloș and Miloș (2022) for 120 European financial institutions over the 2008 to 2021 period. The mean for Tobin’s Q, the second measure of bank performance, was 0.9927, consistent with those reported by prior European studies (Liang et al. 2013; Busta et al. 2014). Examining measures of the extent to which trading and hedging derivatives are used gave values of 4.0791 and 0.1936, respectively. This shows that European banks are more heavily involved in trading than hedging derivatives, consistent with the findings of prior European banking industry studies (Chang et al. 2018; Titova et al. 2020).
In terms of bank characteristics, the average operating interest and net profit margins were 34.80% and 25.06%, respectively. The mean for bank asset turnovers was 0.0195, with a maximum (minimum) value of 0.0398 (0.0095). The average leverage ratio was 5.42%, with a maximum (minimum) value of 0.1054 (0.0130). The mean values for derivative use for growth opportunities and size were 5.58 and 22.4, with minima of 1.2 and 19.06, respectively.
Table 3 illustrates the correlation coefficients found between the variables included in the analysis. The results indicate that the model used is not biassed and that multicollinearity is not a concern, based on the low correlation coefficient values. Furthermore, as Table 3 shows, the trading derivative correlation coefficients indicate significant and negative associations at the 1% level with all performance indicators. However, the hedging derivative variable shows a positive, though insignificant, correlation with many performance indicators. Overall, the trading derivative measure is highly correlated with the dependent variables. The results also suggest that banks with greater derivative use for trading purposes demonstrate worse performance.

4.2. Regression Results

Following prior empirical studies, an OLS regression random effects model was adopted to investigate the impact of derivatives on bank performance (Lau 2016; Wen et al. 2021; Titova et al. 2020; Fauver and Naranjo 2010). The results estimated using OLS regressions are presented in Table 4. Alongside several control variables, ROE is included as the dependent variable in relation to trading derivatives, and hedging derivatives as the independent variables.
The result estimated using Tobin’s Q acts as a second dependent variable in relation to both trading and hedging derivatives as independent variables, using the same control variables. The results reported in Table 4 provide evidence of the influence of trading derivatives on bank performance. Based on these, a negative and significant association between trading derivatives and bank performance is documented at the 1% level, as measured by ROE. As the second performance indicator and measured using Tobin’s Q, firm value shows a negative and highly significant association with trading derivatives at the 1% level. This finding suggests that trading activities using derivatives contribute to worsening bank performance and decreasing bank value. This negative impact could be due to several reasons. For example, the intensive use of trading derivatives increases market volatility for bank returns and reduces valuations (Titova et al. 2020), while investors and market participants have a more negative perception regarding derivative use for trading activities than for risk management (Koonce et al. 2008; Miloș and Miloș 2022). The negative effect of trading derivatives can thus be related to general concerns regarding their use and the potential for large losses during and after a crisis period (Titova et al. 2020; Al-Own et al. 2018; Keffala 2015). The results also imply that derivatives held for trading purposes in the banking sector are destructive to bank value and performance. Using derivatives for speculative purposes is detrimental to firm value and increases risk exposure (Adam et al. 2017); this is consistent with the findings of Ghosh (2017), who provided evidence of the negative impact of financial derivative transactions on bank profitability post-crisis. More recent studies also show that derivative use diminishes bank performance (Yildiz Savas and Kapusuzoglu 2020; Al Fazari et al. 2022; Taşkın and Sarıyer 2020; Wen et al. 2021) and undermines bank value (Miloș and Miloș 2022; Bazih and Vanwalleghem 2021). In contrast, Chang et al. (2018) reported the positive impact of trading derivatives on bank performance and value.
Regarding the effect of hedging derivatives on bank performance, and according to the results in Table 4, there is a positive and significant impact as measured using ROE. Consistent with prior studies, the results suggest that when banks use more derivatives for hedging purposes, a better financial performance is expected (Pramborg 2004). Nevertheless, this improvement in performance is not necessarily translated into a higher bank value as measured by Tobin’s Q. The current analysis thus does not provide evidence that hedging derivatives affect bank value. Although the coefficient for Tobin’s Q was positive, its insignificance implies that hedging does not add value. According to Wagner’s (2007) theoretical model, the use of financial innovation such as derivatives may increase concerns about bank activities, increase opacity, or create agency problems. This opacity has the potential to affect bank value (Tobin’s Q). Investors’ negative perception regarding derivative use can also be an important factor in explaining why the use of hedging derivatives did not translate into higher bank value. This is particularly true for post-crisis periods, where reactions to derivative use become unfavourable.
The results reported in Table 4 show that derivative use for risk management is expected to enhance accounting performance and contribute to better ROE. In contrast, using derivatives for trading activities negatively influences bank performance. These results suggest that banks should focus more on hedging financial exposures using derivative contracts; however, bank managers should also consider the costs associated with using trading derivatives. This finding also indicates that banks should use more hedging rather than trading derivatives, because trading as a strategy is not in the best interest of bank owners wishing to maximize wealth.
Several issues may explain these results. The first is that mangers can incorporate new viewpoints and use derivatives to speculate within the context of firm hedging programmes by changing the size and the time of derivative contracts. This can result in higher exposure to risk and decrease firm value (Bachiller et al. 2021). Additionally, there are extensive studies discussing the impact of hedging on firm value from an agency theory perspective. Undiversified mangers with a great deal of human capital invested in the firm may use selective hedging to reduce cash flow volatility, but at the expense of shareholder value (Smith and Stulz 1985; Hagelin et al. 2007). Previous empirical studies have also noted that the decision to use hedging derivatives does not drive firm value (Ben Khediri 2010). Seok et al. (2020) argue that to enhance their value, firms should extensively use derivatives for hedging. Lau (2016) shows that while derivative usage contributes to better accounting performance, it is negatively related to firm value.
In post-crisis periods, many investors are unable to easily distinguish the purpose of financial institutions’ derivative use (Titova et al. 2020; Ghosh 2017). Li and Marinč (2014) thus concluded that large banks are less transparent about trading activities. In addition, managers may use hedging derivatives to lower cash flow volatility and smooth earnings in order to avoid any possible unexpected negative impacts (earning surprises) on earning patterns. When managers expand firm markets, this extends their operations by generating more sales, leaving them exposed to additional, higher financial risks. These firms may use hedging derivatives to manage financial positions and smooth earnings, to achieve benchmark levels, or to take advantage of any possible growth opportunities. In support of this, previous studies have shown that managers use hedging derivatives as a mechanism to maintain profit stability and minimize earning variability (Barton 2001; Cadot et al. 2021). Using hedging derivatives as a corporate risk management tool enables managers to reduce cash flow and firm value volatility (Aretz and Bartram 2010). Belghitar et al. (2013) showed that derivatives offer an effective instrument for reducing firm exposure; however, they gave no evidence that derivatives enhance firm value.
The findings suggest that using derivatives as a risk management strategy can improve bank accounting performance, but may not necessarily enhance firm value. This is similar to the evidence introduced by another strand of the empirical literature regarding the ways in which hedging derivatives do not lead to lower risk levels (Al-Own et al. 2018). Chong et al. (2014) thus argued that hedging is a complementary risk reduction strategy.
The use of a random effect model helped us shed more light on the impact of using trading derivatives on firm performance and value. According to the results presented in Table 5, the random effect model supports the OLS regression results using a continuous variable, confirming the negative impact of trading derivatives on both bank performance and value.
We determined the negative and statistically significant associations between trading derivatives, ROE and Tobin’s Q at the 1% and 10% level, respectively. Overall, Table 5 demonstrates that the extent of trading derivative use is negatively related to bank performance and value. This finding is consistent with the idea that using derivatives for trading activities has an inverse influence on bank value. The intensive use of derivatives in this regard is thus a negative sign for investors, who may seek to avoid investing in these firms (Yildiz Savas and Kapusuzoglu 2020). However, using derivatives for hedging purpose has no effect on bank value, implying that investors in the European banking sector do not assign a premium value to such measures. Executives must thus provide investors with more details about their hedging strategies and how these may increase shareholder value.
In terms of control variables, the results of the OLS and Random effect models reveal that the coefficients for OIM, NPM, and size are positive and statistically significant, while those for leverage and growth opportunities are negative and statistically significant. These results indicate that large banks are characterized by higher performance and value. Past studies have shown that firm size is expected to be positively associated with derivative usage and firm performance (Lau 2016; Bazih and Vanwalleghem 2021; Keffala 2021). Large banks demonstrate better performance and are more likely to use derivatives as they have knowledge and expertise in using them to support economies of scale (Pramborg 2004).
Consistent with prior risk management studies examining the determinants of firm value and performance, profitability measures were included as bank-specific explanatory variables (Titova et al. 2020; Lau 2016; Sahoo and Sahoo 2020; Wen et al. 2021). OIM and NPM were used to capture the effects of profitability on firm performance. It is generally believed that high-profit results are a signal of good performance, and they are associated with higher market valuations (Belghitar et al. 2013). Based on a positive and significant association between firm performance and profitability variables, the OIM and NPM results are robust. With respect to asset turnover, the results show that when banks use derivatives for hedging purposes, asset turnover is both positive and significant. In terms of explaining both bank performance and value, this indicates that management efficiency may be similarly positive and significant when interacting with hedging derivatives. A negative and significant relationship was found between leverage and performance variables. Consistent with the previous literature, higher leverage reduces bank profitability, as measured by ROE, and reduces value, as measured by Tobin’s Q (Ben Khediri 2010). This finding indicates that the likelihood of financial distress increases with leverage, which in turn reduces firm value. As in Zamzamin et al.’s (2021) study, the current analysis shows a negative association between firm performance and growth opportunities. This implies that banks with more growth opportunities have lower market values as measured using Tobin’s Q. Overall, and in line with the figures for non-financial firms, the estimated control variable results suggest that size, leverage, profitability, growth opportunities, and asset turnover tend to influence both bank performance and value.
These results show that financial derivatives serve two different roles when used as a risk management or speculative tool. The hedging of derivatives typically enhances bank performance, while trading decreases bank performance. However, previous research has shown that, after the last financial crisis, the positive influence of derivatives on European banks’ value and profitability not only became less pronounced, but in many cases was inversed (Titova et al. 2020). While the new IFRS standards represent an important attempt to improve reporting transparency, derivative reporting remains a complex topic that attracts criticism (Cadot et al. 2021).

4.3. Robustness Test

In this study, we performed various additional robustness tests to check result consistency. We changed the econometric method from ordinary least squares and random effect regressions to the Feasible Generalized least squares regression (FGLS) model. We were motivated to choose this model due to its abilities to address autocorrelation and heteroscedasticity (Wooldridge 2010). Additionally, the FGLS model has commonly been used in previous bank-related studies, offering a relevant and robust estimation technique (Eltweri et al. 2024; Ahmad and Azad 2024).
To further check the robustness of our results, we followed previous literature and used return on assets (ROA) and net interest margin (NIM) as alternative bank performance measurements (Eltweri et al. 2024; Liang et al. 2013; Al Fazari et al. 2022; Zamzamin et al. 2021). Again following prior studies, we measure NIM as the ratio of net interest income to total assets, while we measure ROA as the ratio of net profit to total assets (Chang et al. 2018; Taşkın and Sarıyer 2020). Additionally, we used the fair value of hedging derivatives scaled by total assets and fair value of trading derivatives scaled by total assets as additional proxies for derivative usage extent. Many previous empirical studies use fair value as an explanatory variable to measure the extent of derivative use (Titova et al. 2020; Infante et al. 2020; Huan and Parbonetti 2019). The control variables are the same as those included in the OLS and random models.
Table 6 reports the estimation results using alternative dependent and independent variable measures. We found consistent evidence that the fair value of trading derivatives reduces banks’ net interest margin and ROA, and that of hedging derivatives is positively associated with bank performance.
We further replicate our main regression models using the FGLS model. The FGLS analyses show that the results presented in previous sections are largely unchanged. The results confirm the negative impact of notional trading derivative value on bank performance and value using ROE and Tobin’s Q, respectively. The results also reveal that hedging derivatives are not associated with bank value. For brevity, the FGLS estimation results using the notional value of derivatives are not reported here, but are available upon request.
Together, the multivariate statistical analyses used in this study, i.e., OLS, random effect, and FGLS regressions, suggest that the impact of derivative use extend on bank performance depends on the purpose of usage. Using derivatives for trading activities has a negative impact on bank value and performance, while using hedging derivatives enhances bank performance but not value. Our results are robust to different specifications and consider the different impacts of derivatives based on the purpose of usage. Using alternative measures for our main proxies of the extent of using derivatives and bank performance, we verified the robustness of our results.

5. Conclusions

This study examined the impact of European banks’ purposes for using financial derivatives on bank performance. It revealed some important findings regarding the different effects of trading and hedging derivatives on bank performance. Our analysis shows that the purpose of using derivatives influences bank performance and value in different ways. Such findings are robust to various aspects of bank performance. More specifically, financial performance was measured using ROE, with Tobin’s Q used to measure bank value. The extent of trading derivative use is negatively associated with bank value and performance. In addition, a positive and significant relationship exists between the extent of using hedging derivatives and financial performance. However, the results do indicate that the extent of using hedging derivatives is not a value-enhancing factor. The results also revealed that European banks are more extensively involved in trading than hedging derivatives (see Table 2).
The findings in this paper suggest that, regardless of purpose, these financial tools are not value-enhancing; indeed, they have a tendency to be value-undermining for the European banking sector. Moreover, the results imply that banks use hedging derivatives to promote earning stability, a finding that is in line with both our theoretical prediction and previous empirical studies.
This study offers potentially important implications for regulators, policymakers, investors, and managers. Financial market regulators should be careful about the potential risk inherent to trading derivatives and the negative consequences of their extensive use in trading activities. It is particularly important to monitor banks’ use of these instruments to ensure a sound and stabile financial sector. The main objective for policymakers and bank operation supervisors must thus be to maintain acceptable levels of trading for derivatives and to assure that the costs of engaging in derivative markets do not surpass their benefits. Managers must also be prudent regarding firm value, understanding the main goals of using derivatives and the potential impact of these instruments on performance. Using derivatives for risk management may enhance financial performance; however, the risk of their use for trading may be a source of concern for investors.
One of the main limitations of this study is that it focuses on the total notional value of all derivative contracts, while different types thereof may have different effects on bank performance. These results may not thus apply to all derivative types. It would therefore be interesting for future studies to investigate the impact of different kinds of derivatives on bank performance or to establish connections between derivative strategies and bank governance variables to investigate their impact on bank performance. Moreover, it would be extremely useful to study the effect of offsetting contracts for hedging derivatives on bank performance. A more efficient econometric method may also be used to address any potential endogeneity problems (e.g., a dynamic or panel model with instrumental variables). Furthermore, the current study only focused on the European banking sector; future studies could thus usefully examine the impact of financial derivatives on performance in more detail by focusing on bank-level selections or by using a different financial firm sample such as insurance firms, pension funds, or mutual funds to capture differences. Nevertheless, the results presented here yield valuable insights for both policymakers and financial market practitioners.

Author Contributions

Conceptualization, B.A.-O.; Methodology, B.A.-O.; Software, M.O.A.S. and Z.J.; Validation, Z.J.; Formal analysis, G.N.A.-E.; Investigation, M.O.A.S.; Resources, B.A.-O.; Data curation, B.A.-O.; Writing—original draft, G.N.A.-E.; Writing—review & editing, Z.J. and G.N.A.-E.; Visualization, M.O.A.S.; Supervision, B.A.-O.; Project administration, M.O.A.S.; Funding acquisition, G.N.A.-E. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Data Availability Statement

The data presented in this study are openly available in [DataStream database] [https://product.datastream.com/dsws/1.0/DSLogon.aspx] (accessed on 18 February 2025).

Conflicts of Interest

The authors declare no conflict of interest.

Appendix A

Table A1. List of the 35 European banks.
Table A1. List of the 35 European banks.
BankCountryBankCountryBankCountry
ABN Amro GroupNLCommerzbankDENatWestGB
Banca MPSITCredit AgricoleFRNordea Bank SE
Banco SabadellESDanske BankDKOTP BankHU
Banco SantanderESDeutsche BankDEPKO Bank PL
Bank of IrelandIEDNB bankNOSEBSE
BarclaysGBErste Group BankATSociete GeneraleFR
BCP GroupPTHandelsbankenSEStandard CharteredGB
BNP ParibasFRHSBCGBSwedbankSE
BOVMAING Group NLSydbankDK
BPCEFRIntesa SanpaoloITUBS GroupCH
BPER BancaITKBC BankBEUniCreditIT
CaixaBankESLloydsGB

Notes

1
https://www.bis.org/publ/otc_hy2311.pdf (accessed on 15 August 2025).
2
https://www.iasplus.com/en/news/2010/October/news6281 (accessed on 15 August 2025).

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Table 1. Explanation of variables.
Table 1. Explanation of variables.
Variable (Acronym) DefinitionData Source
Hedging derivatives (HD) Annual notional value of derivatives used for hedging purpose scaled by total assetsAnnual report
Trading derivatives (TD)Annual notional value of derivatives used for trading purpose scaled by total assetsAnnual report
Tobin’s Q (TQ)Equity market capitalization plus the book value of total liabilities, divided by the book value of total assetsDatastream and annual report
Fair value hedging (FHD)Fair value of derivatives used for hedging purposes divided by total assets Annual report
Fair value trading (FTD)Fair value of derivatives used for trading purposes divided by total assetsAnnual report
Return on equity (ROE)Net income at the end of the year scaled by the book value of total equityAnnual report
Net interest margin (NIM) Net interest income of the year scaled by book value of total assetsAnnual report
Return on assets (ROA)Net income of the year scaled by the book value of total assetAnnual report
Operating income margin (OIM)Annual operating income scaled by saleDatastream
Net profit margin (NPM) Annual profit for the year scaled by saleDatastream
Assets turnover (AT)Annual Sales at the end of the year scaled by the book value of total assetsDatastream
Leverage (LEV)The sum of current debt and long-term debt divided by total assetsDatastream
Growth opportunities (GRO)The natural logarithm of the market-to-book value of assetsDatastream
Size (SZ)The natural logarithm of total salesDatastream
Table 2. Summary statistics.
Table 2. Summary statistics.
VariableObservationsMeanStandard DeviationMinMax
TD3854.07917.43230.000138.9962
HD3850.19360.22940.00001.0023
TQ3850.99270.02080.94931.4094
ROE3850.07160.0400−0.05300.1380
OIM3850.34800.13320.03690.5938
NPM3850.25060.1346−0.06810.6024
AT3850.01950.00650.00950.0398
LEV3850.05420.01540.01300.1054
GRO3855.58311.42301.25988.1316
SZ38522.46841.444119.064025.5471
Table 3. Correlation matrix.
Table 3. Correlation matrix.
TDHDTQ ROEOIMNPMATLEVGROSZ
TD1.0000
HD0.02961.0000
TQ−0.1471 ***−0.07611.0000
ROE−0.1576 ***0.07020.2957 ***1.0000
OIM−0.1852 ***−0.02850.3131 ***0.4536 ***1.0000
NPM−0.0840 *0.01290.3116 ***0.5546 ***0.6269 ***1.0000
AT−0.1750−0.00280.04830.04190.06630.00361.0000
LEV−0.2279 ***0.00390.01720.1117 **0.2717 ***0.2810 ***0.3708 ***1.0000
GRO0.1442 ***0.0783−0.2172 ***−0.0094−0.1040 **−0.0947 *−0.1052 **−0.1284 **1.0000
SZ0.2577 ***0.0787−0.2024 ***−0.1090 **−0.3131 ***−0.2775 ***−0.2715 ***−0.3718 ***0.3518 ***1.0000
Note(s): *, **, *** represent the significance at level p < 0.1, p < 0.05, p < 0.01; t-statistics are in the parentheses.
Table 4. OLS regression results for derivatives and bank performance.
Table 4. OLS regression results for derivatives and bank performance.
TradingHedging
ROETQROETQ
TD−0.0006 ***(−2.73)−0.0002 *(−1.80)
HD 0.0132 *(1.84)−0.0042(−0.98)
OIM0.0533 ***(−3.24)0.0267 ***(−2.77)0.0566 ***(3.51)0.0274 ***(2.87)
NPM0.1441 ***(−8.89)0.0330 ***(−3.48)0.1428 ***(8.99)0.0331 ***(3.52)
AT0.4162(−1.47)0.1640(−0.99)0.7054 ***(4.36)0.2441 **(2.55)
LEV−0.2316 *(−1.83)−0.2323 ***(−3.13)−0.2653 **(−2.19)−0.2346 ***(−3.27)
GRO0.0011(−0.87)−0.0024 ***(−3.25)0.0001(−0.01)−0.0027 ***(−3.65)
SZ0.0023 *(−1.68)−0.0009(−1.07)0.0027 **(1.97)−0.0007(−0.82)
Constant−0.034(−1.00)1.0182 ***(−51.27)−0.0473(−1.43)1.0132 ***(51.59)
N385 385 385 385
Banks35 35 35 35
Adjusted R20.3396 0.1645 0.3583 0.1717
Note(s): *, **, *** represent the significance at level p < 0.1, p < 0.05, p < 0.01; t-statistics are in the parentheses.
Table 5. Random effect regression results for derivatives and bank performance.
Table 5. Random effect regression results for derivatives and bank performance.
TradingHedging
ROETQROETQ
TD−0.0007 ***(−2.88)−0.0002 *(−1.8)
HD 0.0128(1.58)−0.0042(−0.98)
OIM0.0528 ***(3.25)0.0267 ***(2.77)0.0562 ***(3.52)0.0274 ***(2.87)
NPM0.1377 ***(8.55)0.0330 ***(3.48)0.1373 ***(8.66)0.0331 ***(3.52)
AT0.4644(1.65)0.1640(0.99)0.7052 ***(4.34)0.2441 **(2.55)
LEV−0.2422 *(−1.91)−0.2323 ***(−3.13)−0.2674 **(−2.20)−0.2346 ***(−3.27)
GRO0.0012(1.00)−0.0024 ***(−3.25)0.0001(0.07)−0.0027 ***(−3.65)
SZ0.0022(1.61)−0.0009(−1.07)0.0025 *(1.85)−0.0007(−0.82)
Constant−0.0308(−0.92)1.0182 ***(51.27)−0.0421(−1.28)1.0132 ***(51.59)
N385 385 385 385
Banks35 35 35 35
R2 within0.2836 0.1151 0.2867 0.1068
R2 between0.6698 0.6248 0.7137 0.6079
R2 overall0.3514 0.1797 0.3699 0.1756
Hausman test
(p-value)		10.2882 (0.9015) 9.4472
(0.3081)		 9.7403
(0.2038)		 9.2406
(0.3021)
Heteroskedasticity156.8011
(0.1373)		 153.5222
(0.1446)		 157.9391
(0.1327)		 154.2066
(0.1361)
Note(s): *, **, *** represent the significance at level p < 0.1, p < 0.05, p < 0.01; z-statistics are in the parentheses.
Table 6. FGLS regression results of fair value derivatives and bank performance.
Table 6. FGLS regression results of fair value derivatives and bank performance.
TradingHedging
ROANIMROANIM
FVT−0.0276 ***(−3.53)−0.0792 ***(−5.32)
FVH 0.6036 ***(6.13)0.0528 ***(3.33)
OIM−0.0145 **(−2.24)0.0181(1.04)−0.0076(−1.26)−0.0978 ***(−4.79)
NPM0.0263 ***(3.88)−0.0096(−0.54)0.0181 ***(2.67)0.1797 ***(8.45)
AT−0.1209(−0.99)−0.9551 ***(−4.40)−0.0066(−0.12)−0.9765 **(−2.58)
LEV−0.0914(−1.25)0.0378(0.31)−0.1246 ***(−3.48)−0.4054 ***(−2.67)
GRO0.0013(1.51)−0.0022 *(−1.69)0.0002(0.43)−0.0052 ***(−3.02)
SZ0.0002(0.20)0.0013(0.96)−0.0003(−0.68)0.0005(0.22)
Constant0.0094(0.53)0.0543(1.56)0.0244(2.22)0.0652(1.15)
N385 385 385 385
Banks35 35 35 35
Wald chi262.28 53.80 61.01 154.46
Prob-chi20.0000 0.0000 0.0000 0.0000
Hausman test
(p-value)		4.1157
(0.7663)		 2.4786
(0.9287)		 4.1081
(0.7672)		 2.4849
(0.9282)
Heteroskedasticity177.6237
(0.0059)		 172.3998
(0.0064)		 174.3579
(0.0038)		 176.7933
(0.0069)
Note(s): *, **, *** represent the significance at level p < 0.1, p < 0.05, p < 0.01; z-statistics are in the parentheses.
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Al-Own, B.; Al Shbail, M.O.; Jaradat, Z.; Al-Eitan, G.N. The Impact of Financial Derivatives on European Bank Value and Performance. Risks 2026, 14, 39. https://doi.org/10.3390/risks14020039

AMA Style

Al-Own B, Al Shbail MO, Jaradat Z, Al-Eitan GN. The Impact of Financial Derivatives on European Bank Value and Performance. Risks. 2026; 14(2):39. https://doi.org/10.3390/risks14020039

Chicago/Turabian Style

Al-Own, Bassam, Mohannad Obeid Al Shbail, Zaid Jaradat, and Ghaith N. Al-Eitan. 2026. "The Impact of Financial Derivatives on European Bank Value and Performance" Risks 14, no. 2: 39. https://doi.org/10.3390/risks14020039

APA Style

Al-Own, B., Al Shbail, M. O., Jaradat, Z., & Al-Eitan, G. N. (2026). The Impact of Financial Derivatives on European Bank Value and Performance. Risks, 14(2), 39. https://doi.org/10.3390/risks14020039

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