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Barbara Dömötör Institute of Finance, Corvinus University of Budapest, Budapest, Fővám tér 9, H-1093 Hungary

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Abstract

Micro-level risk awareness affects macro-level financial stability as well. Thus, the corporate risk management practice impacts the exposures and the potential fragility of an economy. While corporate risk management is accepted to create value in an imperfect market, the effect of the firm size is not straightforward. Smaller, financially constrained firms can benefit more by engaging in risk management programs, but larger corporations face more complex risks and have more resources for this activity. Empirical studies on risk management focusing mainly on the US market, highlight a positive relation between the firm size and the quality of risk management that includes not just the hedging of financial risks, but the concept of integrated risk management as well. The aim of this paper is two-fold: first, to summarize the existing literature on corporate risk management with a special focus on the effect of corporate size; second, to contribute to the existing literature by investigating a Central European market, Hungary. The findings are similar to those of the existing global literature, as derivatives usage, and applications of an integrated risk management concept increase with firm size. Although all firms in the sample manage their foreign exchange risk, interest rate hedging and more sophisticated derivatives, like options, are much less widespread in Hungary, compared to the US and Asian peers. The size effect is proven for the objective criteria of risk management quality by comparative analysis and a structured modelling framework, however, the subjective self-evaluation was uncorrelated to the size.

Abstract

Micro-level risk awareness affects macro-level financial stability as well. Thus, the corporate risk management practice impacts the exposures and the potential fragility of an economy. While corporate risk management is accepted to create value in an imperfect market, the effect of the firm size is not straightforward. Smaller, financially constrained firms can benefit more by engaging in risk management programs, but larger corporations face more complex risks and have more resources for this activity. Empirical studies on risk management focusing mainly on the US market, highlight a positive relation between the firm size and the quality of risk management that includes not just the hedging of financial risks, but the concept of integrated risk management as well. The aim of this paper is two-fold: first, to summarize the existing literature on corporate risk management with a special focus on the effect of corporate size; second, to contribute to the existing literature by investigating a Central European market, Hungary. The findings are similar to those of the existing global literature, as derivatives usage, and applications of an integrated risk management concept increase with firm size. Although all firms in the sample manage their foreign exchange risk, interest rate hedging and more sophisticated derivatives, like options, are much less widespread in Hungary, compared to the US and Asian peers. The size effect is proven for the objective criteria of risk management quality by comparative analysis and a structured modelling framework, however, the subjective self-evaluation was uncorrelated to the size.

1 Introduction

Corporate risk management theory has a very broad literature investigating the relevance and the optimal models of corporate hedging. While the perfect market assumption of Miller and Modigliani (1958) states that a firm's value is unaffected by the management of financial risks and market imperfections, in reality, taxes, transaction costs, information asymmetry, and costs of financial distresses justify the importance of risk management as a significant part of corporate management (Smith – Stulz 1985; Lessard 1991; Froot et al. 1993; Tirole 2006; Voszka, 2021).

Corporate risk management gained importance in practice over the last decades (Stulz 1996; Nocco – Stulz 2006). In the 20th century, risk management consisted of buying insurance against some types of risks. Then, with the development of financial markets, corporations obtained access to a wide range of financial derivatives that allowed them to reduce their exposure and, consequently, the risk of their cashflow. The earlier concept of risk management and measurement was focused on certain types of risks separately and controlled their consequences independently. The holistic approach of risk management that considers the overall effect of the risk factors on the operation is captured by the Enterprise Risk Management (ERM) concept (Hull 2015). This is described by the Committee of Sponsoring Organizations of the Treadway Commission in 2004:

“Enterprise risk management is a process, effected by an entity's board of directors, management, and other personnel, applied in strategy setting and across the enterprise, designed to identify potential events that may affect the entity, and manage risk to be within its risk appetite, to provide reasonable assurance regarding the achievement of entity objectives.”

The new phenomenon that risk management should be considered for is the growing importance of the non-financial factors, such as environmental, social and governance (ESG) issues on corporate value (Aybars et al. 2019). The social capital built by ethical operations, such as the Corporate Social Responsivity (CSR) activities, can provide a downside protection in the times of stress (Lins et al. 2019). The firm's ESG/CSR profile and activities are influenced by many factors like ownership, market and leadership but the direction of the causal relation is not obvious (Gillan 2021).

In practice, corporate hedging is influenced by many factors, such as overall risk appetite (Berlinger – Váradi 2015) and the availability of financial markets, their liquidity and market risk premiums (Mian 1996; Joseph – Hewins 1997; Haushalter 2000; Csóka – Hevér 2018; Dömötör – Kovács 2018), management incentives (Tufano 1996; Bihary – Dömötör 2018), and/or behavioural effects (Brown et al. 2003; Faulkender 2005).

This paper aims to review the empirical studies of the literature investigating corporate risk management and to analyse the practice of the Hungarian firms, focusing on the question of whether better developed risk management practice comoves with the corporate size. The theoretical works of Froot et al. (1993) and Holmström – Tirole (2000) suggest that risk management can serve as a substitute for outside financing. Therefore, smaller firms, prior to their growing phase, are better motivated to engage in the risk management programs and, thus, avoid credit rationing. On the other hand, larger corporations face more complex risks and also have more scope (financial and human resources) to set up an internal risk management function (Nocco – Stulz 2006).

The empirical studies on the relation of corporate size and risk management activity conclude that firm size is a significant factor positively affecting risk management practice. This paper also confirms the positive relation, based on a comparative analysis of the sample and the correlation of latent variables of size and level of risk management quality.

Section 2 presents the empirical literature on the practice of corporate risk management, with particular focus on the effect of the firm size. Section 3 details the data and provides a descriptive analysis of the survey result. The fourth Section contains the analysis of the dataset through a latent variable model. Finally, the conclusions are derived.

2 Literature review

Earlier studies (detailed in the next sub-section) of the practice of corporate risk management focused on financial risks, mainly on questions dealing with the kinds of risk firms were facing, whether they were aware of them, and how they managed them (if they did). These studies presented a descriptive analysis of the survey results, combined with other – mainly financial – firm characteristics.

With the increasing relevance of the integrated risk management approach and the appearance of the ERM concept, the most important research question changed from being the description of the risk management practice to being the evaluation of the ERM status of firms and the factors determining it.

The global environmental changes sped up in the last years. The consequences of the climate change and also the effect of a global pandemic like the COVID-19 pose a new challenge to corporate risk management. Financial markets became more volatile, increasingly procyclical and cross-integrated, making financial contagion more likely (Davidovic 2021).

Those global uncertainties are much less manageable by pure financial tools, operational and strategic answers are needed that are more and more in the focus of corporate risk management. The environmental and socioeconomic considerations of uncertainty forces organizations to analyse more carefully the risk factors affecting their business continuity and also their sustainability relevance (Settembre-Blundo et al. 2021). During the pandemic caused market shocks, corporations with better focused crisis management are proved to ensure fewer negative returns (Cheema-Fox et al. 2021).

2.1 Descriptive studies

Foreign exchange (FX) risk management and product usage of the Australian firms were analysed by Batten et al. (1993) based on survey results of 72 large corporations. FX turnover, that can be considered as an indicator of size, was found to have the most significant effect on the risk management characteristics, such as usage of more sophisticated products and computer technology.

Financial risk management and derivative usage in risk management were investigated in two surveys at The Wharton School in 1995 and 1998. The analysis of the responses was published by Bodnar et al. (1998) focusing on the risk management practice of the US non-financial firms. The results were compared with a companion survey on the German non-financial firms in 1997 by Bodnar et al. (1999). They found that the derivative usage in risk management has some common features in both the countries: hedging of FX-risk is the most common; larger firms use derivatives significantly more often in both countries. They also detected some differences in the traded instruments and the goal of hedging.

The size effect in derivatives usage was also confirmed by Heaney et al. (1999), who analyzed the non-financial firms in Japan.

The Wharton School survey format was used by Sheedy (2006) to analyze corporate risk management in Hong Kong and Singapore. She found that derivative usage is more intense in Hong Kong and Singapore than in the US, but the size effect – that larger firms tend to use derivatives more – is the same in all the three countries. However, the difference between large and medium-sized corporations is not as significant in Asia.

A global extension of the survey was completed by Bodnar et al. (2011) as they analysed the responses of 1,100 firms all over the world. The results were very similar to the previous ones. Derivative usage is widespread, but more than one-third of the companies refuse using derivatives as a part of their risk management program. Mainly over-the counter (OTC) derivatives and basic instruments (e.g., forward contracts) are preferred. They found major differences in the practice of financial and non-financial firms, but location was not a distinguishing factor. The fact that larger companies manage their financial risks more was also confirmed.

In Hungary, the central bank (MNB) carried out two surveys in 2005 and 2007, the results were summarised by Bodnár (2006, 2009). The first survey investigated the exchange rate exposure of the small and medium-sized enterprises and the management of their exposure. According to them, the small enterprise sector is less risk-aware; it underestimates its risk exposure and does not manage it. The model found no relation between corporate size and FX risk management. The second survey included large corporations as well, with a focus on foreign exchange exposure. The overall results were similar to the previous ones: the Hungarian corporate sector is unaware of risk management tools, considering them expensive, complicated and ineffective. However, larger firms were found to be more aware of the usage and availability of derivatives for the risk management purposes.

The papers mentioned so far, contained descriptive analysis and comparison of the different categories where firm size was measured by the sales revenue, except for Bodnár (2006) which was based on the number of employees.

2.2 Studies on the evaluation of risk management (ERM) programs

Lechner and Gatzert (2018) grouped empirical studies on ERM into three main lines of research. The first one investigates the implementation of ERM, the second examines the firm characteristics that are determinants of the ERM, while the third searches for evidence of the impact of ERM on corporate value. The studies apply mainly public data sources like Compustat or financial reports of the firms in the sample, some of which were based on surveys.

Colquitt et al. (1999) investigated the characteristics and extent of integrated risk management and one of their focus areas was the size effect. They found that both derivative usage as well as the frequency of finite risk insurance and financial reinsurance increase with firm size. Their analysis was based on comparing the ratios among the groups with different firm sizes, similar to the method of Bodnar (1998) and Sheedy (2006).

Liebenberg – Hoyt (2003) modelled the determinants of appointing a corporate risk officer, which was considered a signal of using the ERM concept among the US insurers. They compared the firm characteristics of the group of firms that announced a Chief Risk Officer (CRO) appointment with a control group. Both of their models (comparative statistics and logit regression) failed to display a significant positive effect of the corporate size. Their insignificant results might have been due to the limited sample size (26 firms + 26 control firms) and the fact that CRO appointment is not necessarily a good measure of integrated risk management. On the other hand, later they found significant differences in the size between the ERM users and non-users on a wider panel dataset (Hoyt – Liebenberg 2011), where the ERM usage was identified by certain keywords in the financial reports and media appearances.

Positive correlation between corporate size and ERM implementation was exhibited by several further studies on the sample of the US firms (Beasley et al. 2005; Gordon et al. 2009; Pagach – Warr 2011; Baxter et al. 2013; Farrell – Gallagher 2015). These studies used multivariate models, linear regression and logistic regression to examine the effect of size – as an explanatory variable – on the ERM usage or the level of integrated risk management.

Besides the US, Golshan and Rasid (2012) analyzed the Malaysian corporate sector, Zhao and Singhaputtangkul (2016) interviewed the Chinese construction firms and in Europe Lechner and Gatzert (2018) investigated the German market. Of the studies listed, the size effect could not be proven only in the Malaysian sample. Golshan and Rasid (2012) found that only the financial leverage and the presence of a Big Four auditor have a significant effect on the ERM usage.

Table 1 summarizes the studies that modelled the size effect as a determinant of ERM.

Table 1.

Empirical studies of the size effect

Author(s)DateSampleSample sizeSize effectMethodSize measureData source
Batten et al.1993Australian firms72+Descriptive analysis, Chi-testFX-turnoverSurvey
Colquitt et al.1999US firms379+Descriptive analysisrevenueSurvey
Liebenberg – Hoyt2003US firms26 + 26noLogistic regressionln total assetsPublic database
Beasley et al.2005US and international firms123+Logistic regressionln revenuesSurvey
Gordon et al.2009US firms112+Linear regressionln total assetsPublic database
Pagach – Warr2011US firms77+Logistic regression, Hazard modelln assetsPublic database
Hoyt – Liebenberg2011US insurers117+Panel regressionln total assetsPublic database
Baxter et al.2013US firms165+Lin. regression, Logistic regressionln market cap.Public database
Golshan – Rasid2012Malaysian firms48 + 42noDescriptive analysis

Logistic regression
ln total assetsPublic database
Farrell – Gallagher2015US firms225+Linear regression, Logistic regressionln total assetsPublic database
Zhao – Singhaputtangkul2016Chinese construction firms35+Structural equation analysisFinancial grades in the contractor registration systemInterview
Lechner – Gatzert2018German firms115 + 45+Logistic regression, Hazard modelln total assetsPublic data

In addition to examining the significance of the differences between ERM users and non-users, the above studies examined the relation between ERM adoption and its determinants using linear and logistic regression models that model the predictive power of firm variables such as size.

The research of Zhao and Singhaputtangkul (2016) used the structural equation modelling (SEM) technique that aims to capture the relation among the latent variables. The technique consists of a measurement model that identifies the latent factors of the observable variables and a structural model that tests the relation between them.

According to the existing empirical literature, the effect of corporate size on risk management implementation and the presence of an integrated risk management concept are confirmed on the US, Australian, European, and also, the Asian samples.

Gatzert and Martin (2015) further summarize the empirical studies on the determinants of ERM, introducing all the factors — not only the size — as presented above.

3 Data and descriptive analysis

I investigated the Hungarian market based on the client database of the Hungarian branch of an international commercial bank (Dömötör 2014). All of the bank's active clients (about 100 firms) were approached, so the sampling method was a form of cluster sampling. As the bank focuses on large firms, the surveyed clientele came from the top 500 companies in Hungary. The data collection was implemented through a password-protected questionnaire on an online interface. I received 15 responses through the online interface and 10 questionnaires were sent back via e-mail; together with data recording through personal interviews, I obtained a total of 29 responses. This meant a response rate of about 30%.

The database is unique, as almost all corporations, which are active in risk management, were approached and the sample can be considered representative. The majority of the multinational companies have no such activity in Hungary, as most of them have centralised risk management, so the survey respondents cover a significant part of the total population surveyed.

The design of the questionnaire was based on the experiences of the research partner bank and the Wharton survey (1995) to investigate corporate risk management practices as extensively as possible. The questions were grouped into three types:

  • financial data of the annual report and other firm characteristics,

  • objective questions on risk management implementation, and

  • the subjective evaluation of the respondents on the company's risk management quality.

Although the firms in the sample were considered to be large companies in local markets, they were quite diverse according to their size. To analyze the firm characteristics according to the size, I built three groups, as Bodnar et al. (1998), based on the annual sales figures. Firms with total sales higher than 300 million USD (HUF 90 bn) in the 2011 fiscal year belonged to the large companies. Medium-sized firms had sales revenue between 85 and 300 million USD (HUF 25 – 90 bn). Companies with sales less than 85 million USD were considered to be small. About one-third of the companies belong to each category.

Table 2 presents the results together with those of previous studies. The majority (86%) of Hungarian firms in the sample use derivatives to reduce their foreign exchange risk and all the derivatives users trade forwards.1 There are significant differences among the groups. Large and medium-sized firms all use trade derivatives, but only 60% of the small companies use derivatives.

Table 2.

Derivative usage of firms with different size, %

USHong Kong and SingaporeHungary
Total507886
Large8389100
Medium4582100
Small126260

Although the results are comparable with certain limitations as the date of data collection and the definition of the categories differ, the Hungarian sample's results seem to be more similar to the Asian survey results.

To investigate the quality of risk managament several questions were asked about whether the company had each of the following sophisticated characteristics: the presence of an integrated risk management concept, a written risk management policy, and whether they use the contracts of International Swaps and Derivatives Association (ISDA) as a legal framework and apply hedge accounting. All those statements were considered to reflect an active risk management of the firm. The proportion of the companies with positive answers increased with the size, showing that larger firms are more risk-aware in all analyzed respects. Further aspects of risk management related to the derivatives usage showed that options are much less applied in Hungary – only 21% of the companies conclude options on average – in contrast to the 57% and 46% of option usage in the US and Asian samples, respectively.

Despite the fact that almost all companies reported that they have positions exposed to interest rate risk, they typically do not hedge against that kind of risk in Hungary: only 21% of the firms use derivative transactions to manage interest rate exposure. This ratio is much higher in the US (80%) and in Asia (70%). Both option usage and interest rate hedging frequency increase with the size. Table 3 summarizes the results.

Table 3.

Corporate risk management characteristics, %

RatioRM policyISDAHedge accountingOptionsInterest rate hedge
Total1005548212121
Large34.510070403050
Medium31.04556222211
Small34.520200100

One part of my survey contained some statements on the quality of risk management (detailed in the next session) that had to be evaluated on a 9-grade Likert scale (1 is the worst, 9 is the best), aiming to measure the subjective evaluation of the respondents, the CFO or the CRO of the firm. Table 4 contains the average and the standard deviation of the answers.

Table 4.

Subjective evaluation of the risk management

MeanStandard dev.
Total7.261.68
Large7.890.84
Medium7.841.17
Small6.162.14

Based on the results, the smaller firms evaluate the risk management of their company less, but the differences among the three groups are insignificant. This is partly consistent with the findings of Bodnár (1998), Sheedy (2006), as shown in Table 2. A similar survey was conducted by Horváth in 2020 focusing on the FX exposure of the Hungarian corporations. This research also confirms that the larger companies are more active in managing their risks, however, their attitude and goals differ if they have long or short position in HUF.

4 Structured model of the relation between risk management and corporate size

I analysed the size effect in a structured model that is similar to the above introduced SEM technique. At first, latent variables for the size and the risk management quality are searched from the observed variables using principal component analysis, and then, their correlation is investigated.

4.1 Modeling size

Firm size can be measured using different financial data, most frequently by total assets or net sales. The categorization of the previous section was based on net sales. However, choosing the total assets to build the groups would have caused some minor changes in the ordering.

As Figure 1 shows, financial data in our case contain outliers that cause a bias in the sample, the logarithm of the financial data is used for modelling purposes.

Fig. 1.
Fig. 1.

Distribution of the firms' size measures

Note: The box shows the middle 2 quarters, the dots can be considered outliers.

Citation: Acta Oeconomica 73, 3; 10.1556/032.2023.00023

To get a complex variable that considers more than a single dimension of the size, principal component analysis was used based on the logarithm of the following variables:

  1. -Total assets
  2. -Equity
  3. -Net sales
  4. -Number of employees
  5. -Operating profit
  6. -Pre-tax profit.

By the principal component analysis (PCA) method, uncorrelated latent variables can be identified under the correlated variables (Kovács 2009); in this case, factors representing the size of the company.

The correlation of the studied variables ranges between 0.62 and 0.94, as shown in Table 5, and are all significant at 99% level, the KMO measure2 is 0.776, making the data suitable for principal component analysis.

Table 5.

Correlation matrix of the logarithm of the size measures

ln_assetln_equityln_salesln_employeesln_op_profitln_pretax_profit
ln_asset10.879**0.879**0.617**0.909**0.918**
ln_equity0.879**110.000**0.818**0.837**0.843**
ln_sales0.879**10.000**10.818**0.837**0.843**
ln_employees0.617**0.818**0.818**10.675**0.704**
ln_op_profit0.909**0.837**0.837**0.675**10.944**
ln_pretax_profit0.918**0.843**0.843**0.704**0.944**1

Note: **correlation is significant at 99% level (2-sided).

Because of the strong relation between the variables, the first major component explains 88% of the total variance, so the data can be summarized into a single “size” factor. Consequently, size can be quantified with a single variable by saving the factor score of each company deriving from the PCA.

4.2 Components of risk management

To quantify the risk management quality of the company, objective and subjective survey variables were used. The objective variables were based on the answers to questions related to the risk management practice of the firm: presence of risk management policy, derivatives usage and administration. All of them are binary variables. On the other hand, the subjective evaluation on a 9-grade Likert scale formed the second group of variables.

Table 6 presents the objective and subjective variables used in the analysis.

Table 6.

Variables of corporate risk management quality

VariablesShort name
Objective variables
The firm has a (written) risk management strategy.RM_Strat
FX-risk is managed by matching the positions (natural hedge).Nat_hedge
FX-risk is hedged with derivatives.FX_Deriv
Interest rate risk is managed.IR_hedge
Hedge accounting is used to the accounting of derivatives.Hedge_acc
The risk of the hedge position is measured.Risk_meas
Subjective variables
The risk management strategy is adequate.Strategy
The implementation of risk management is consistent.Implementation
Banking partners help in managing risk.Banks
Fully confident about the features of hedging transactions.Knowledge
Hedging transactions support corporate operation.Opinion

The objective criteria are binary variables; their value is one if the statement is true for the company and zero if it is not. The KMO measure of the PCA of the objective criteria was below 0.5; therefore, that sample was not suitable for PCA. According to the KMO measure, two variables – “IR_hedge” and “Nat_hedge” – proved to be inadequate and were therefore omitted; the analysis was rerun with the other four. The two omitted variables are indeed not necessarily related to the quality of risk management. The absence of interest rate hedging under a normal yield curve can be a cost reduction choice made by the company. Natural hedging, on the other hand, can be part of a proactive risk management policy but it is also possible that the company has not adequately assessed the potential risks and hedging options. The KMO measure of the four-variable analysis is 0.596; the 0.016 value of the Bartlett test also indicates that the hypothesis of the independence of the variables can be rejected. Two components were identified, which explain 73% of the total variance.

Table 7 shows the correlations between the two principal components and the original variables.

Table 7.

Components of risk management quality based on objective variables

Objective variablesComponents
12
RM_Strat0.8230.286
FX_Deriv0.2920.741
Hedge_acc0.894−0.011
Risk_meas−0.0230.864

Note: For the figures in bold the correlation is significant at 99% level (2-sided).

The first component is related to risk management administration and strongly correlates with the written strategy and the application of hedge accounting variables. The other component correlates with the hedging goal of derivatives and with the risk measurement of hedging transactions. The objective measure of the quality of risk management was determined as the sum of the factor scores of two components. It is worth noting that this sum gives the same result as that of a single chosen component.

The value of the subjective variables is between 1 and 9, where 1 means that the respondent does not consider the statement true for the company at all and 9 indicates that the respondent thinks it to be entirely true. PCA was run again, in order to identify the indicators for measuring the quality of risk management based on the judgment of the respondent colleague of the firm. The resulting KMO measure is 0.704 and the hypothesis of the independence of the variables can also be rejected by the Bartlett test at all conventional significance levels.

One component was selected, which retained 73% of the total variance. The correlation between the component and the variables was strong and positive; it varied between 0.77 and 0.93.

Since evaluation on the scale is greatly affected by the respondent's personality, the centered variables were also examined. These indicate which statements the respondent evaluated as better or worse than his own average. To do this, the average value of the given company was subtracted from each value, and I performed the analysis with the resulting values, centered for the company. According to the centered values, two main components can be identified – the first explains 64.5% of the total variance while the second explains a further 22%.

In Table 8, the rotated component matrix shows the correlations of the components and the original subjective variables.

Table 8.

Components of risk management quality based on centered subjective variables

Subjective variablesComponents
12
Strategy_cent0.942−0.117
Implementation_cent0.929−0.172
Banks_cent−0.858−0.233
Knowledge_cent−0.0050.998
Opinion_cent−0.861−0.066

Note: For the figures in bold the correlation is significant at 99% level (2-sided).

The first component shows a strong positive correlation with those statements which evaluate the risk management strategy of the company and its implementation. It shows a strong negative correlation with the opinion of the respondents concerning the services provided by the banks and the usefulness of hedging transactions. The better the opinion of their own risk management strategy, the less satisfied the firms are (relatively) with the banking services and the hedging transactions.

The risk management attitude of the company and how the respondent evaluates the knowledge of the company regarding hedging transactions appear in a separate component. The opinion on how familiar the company is with the characteristics of hedging transactions does not correlate with other statements evaluating corporate risk management.

After procuring the latent variables of size and risk management quality, their correlation is to be examined. The latent variables include a single factor for size but different factors for the quality of risk management; as included in Table 9 together with their short names.

Table 9.

Factors of corporate size and quality of risk management

FactorShort name
Corporate sizesize
Risk management first objective factorobj_fact1
Risk management second objective factorobj_fact2
Sum of risk management objective factorsobj_fact_sum
Risk management subjective factorsubj_fact
Risk management first subjective factor based on centered variablessubj_cent_1
Risk management second subjective factor based on centered variablessubj_cent_2
Sum of risk management subjective factors based on centered variablessubj_fact_cent_sum

Table 10 contains the Pearson's correlation coefficients between the factors and their significance (P-values). The factors of different measures of risk management quality are strongly correlated, some of them derived from each other. Being so trivially related, their correlation is not of interest.

Table 10.

Correlation of corporate size and risk management quality

obj_fact1obj_fact2obj_fact_sumsubj_cent_1subj_cent_2subj_factsubj_fact_

cent_sum
Size0.637**0.1280.564**0.4040.2640.4420.371
(Significance)0.0030.5910.0100.0770.2600.0510.108

Note: **correlation is significant at 99% level (2-sided).

Two of the indicators of risk management quality – the first objective factor and the sum of the objective factors, both in bold – proved to be semi-strongly correlated with the size factor at 99% significance level. The subjective factor, based on the uncentered values, also has a moderate correlation with the size but the significance level is just below 95%. Therefore, the firm size measured by the logarithm of the main balance sheet and profit data strongly correlates with the first objective factor that represents the existence of a written risk management policy and hedge accounting rules. The subjective evaluation of the financial manager on the risk management of the firm also improves with the size (Csillag 2020).

The structured model confirms the results of the existing literature and the comparative analysis, proving that the firm size is positively correlated to the corporate risk management quality, also in Hungary.

5 Conclusion

The concept of enterprise risk management has gained importance among both professionals and academics in corporate finance in the last two decades. Empirical researches focus mainly on the US market, but some other studies reflect that corporations of the European, Australian and Asian markets act similarly to the US firms. This paper summarized the findings of the literature and compared them to the results of a survey that investigated the large Hungarian companies' risk management practice. The main focus of the research, the size effect in risk management, was examined by comparative analysis of the risk management characteristics of groups with different size, and a structured model was built to identify the correlation between the latent variables of size and risk management quality. Both analyses confirmed that in Hungary too larger companies have a more advanced risk management system that extends the usage of financial hedging with an integrated approach to a wider range of risks.

Although this study concentrated on the management of financial risks, they can not be separated from other types of risks that has been increasingly important in the last years. The quality of risk management determines the framework of the firms to be able to cope with the new global challenges, like climate risk.

Acknowledgement

This work was supported by Higher Education Institutional Excellence Program of the Ministry of Human Capacities, 20764/3/2018/FEKUTSTRAT.

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    • Export Citation
  • Bodnar, G. M.Gebhardt, G. (1999): Derivatives Usage in Risk Management by US and German Non‐Financial Firms: A Comparative Survey. Journal of International Financial Management & Accounting, 10(3): 153187.

    • Search Google Scholar
    • Export Citation
  • Bodnar, G. M.Giambona, E.Graham, J. R.Harvey, C. R.Marston, R. C. (2011): Managing Risk Management. In: AFA 2012 Chicago Meetings Paper.

    • Search Google Scholar
    • Export Citation
  • Bodnár, K. (2006): Exchange Rate Exposure of Domestic Small and Medium Size Corporations – Results of a Survey (in Hungarian). Hitelintézeti Szemle, 5(4): 131.

    • Search Google Scholar
    • Export Citation
  • Bodnár, K. (2009): Exchange Rate Exposure of Hungarian EnterprisesResults of a Surve. MNB Occasional Papers, No. 80.

  • Brown, G. W.Crabb, P.Haushalter, D. (2003): Are Firms Successful at Selectively Hedging? SSRN 385169.

  • Cheema-Fox, A.Laperla, B. R.Wang, H. S.Serafeim, G. (2021): Corporate Resilience and Response to COVID-19. Journal of Applied Corporate Finance, 33(2): 2440.

    • Search Google Scholar
    • Export Citation
  • Colquitt, L. L.Hoyt, R. E.Lee, R. B. (1999): Integrated Risk Management and the Role of the Risk Manager. Risk Management and Insurance Review, 2(3): 4361.

    • Search Google Scholar
    • Export Citation
  • Committee of Sponsoring Organizations of the Treadway Commission (2004): COSO Enterprise Risk Management – Integrated Framework.

  • Csillag, I. (2020): GDP and Beyond: Analysis of the Economic Policy in Hungary from 1998 –. Acta Oeconomica, 70(3): 333360.

  • Csóka, P.Hevér, J. (2018): Portfolio Valuation Under Liquidity Constraints with Permanent Price Impact. Finance Research Letters, 26: 235241.

    • Search Google Scholar
    • Export Citation
  • Davidovic, M. (2021): From Pandemic to Financial Contagion: High-Frequency Risk Metrics and Bayesian Volatility Analysis. Finance Research Letters, 42: 101913.

    • Search Google Scholar
    • Export Citation
  • Dömötör, B. (2014): Market Risk Hedging Under Liquidity Constraints. Dissertation. Corvinus University of Budapest, Doctoral School of Business Adminstration.

    • Search Google Scholar
    • Export Citation
  • Dömötör, B.Kovács, E. (2018): Determinants of FX Risk Hedging: Evidence from Hungary. Hungarian Statistical Review, 1(1): 2337.

    • Search Google Scholar
    • Export Citation
  • Farrell, M.Gallagher, R. (2015): The Valuation Implications of Enterprise Risk Management Maturity. Journal of Risk and Insurance, 82(3): 625657.

    • Search Google Scholar
    • Export Citation
  • Faulkender, M. (2005): Hedging or Market Timing? Selecting the Interest Rate Exposure of Corporate Debt. The Journal of Finance, 60(2): 931962.

    • Search Google Scholar
    • Export Citation
  • Froot, K. A.Scharfstein, D. S.Stein, J. C. (1993): Risk Management: Coordinating Corporate Investment and Financing Policies. The Journal of Finance, 48(5): 16291658.

    • Search Google Scholar
    • Export Citation
  • Gatzert, N.Martin, M. (2015): Determinants and Value of Enterprise Risk Management: Empirical Evidence from the Literature. Risk Management and Insurance Review, 18(1): 2953.

    • Search Google Scholar
    • Export Citation
  • Gillan, S. L.Koch, A.Starks, L. T. (2021): Firms and Social Responsibility: A Review of ESG and CSR Research in Corporate Finance. Journal of Corporate Finance, 101889.

    • Search Google Scholar
    • Export Citation
  • Golshan, N. M.Rasid, S. A. (2012): Determinants of Enterprise Risk Management Adoption: An Empirical Analysis of Malaysian Public Listed Firms. International Journal of Social and Human Sciences, 6(2012): 119126.

    • Search Google Scholar
    • Export Citation
  • Gordon, L. A.Loeb, M. P.Tseng, C. Y. (2009): Enterprise Risk Management and Firm Performance: A Contingency Perspective. Journal of Accounting and Public Policy, 28(4): 301327.

    • Search Google Scholar
    • Export Citation
  • Haushalter, G. D. (2000): Financing Policy, Basis Risk, and Corporate Hedging: Evidence from Oil and Gas Producers. The Journal of Finance, 55(1): 107152.

    • Search Google Scholar
    • Export Citation
  • Heaney, R. A.Koga, C.Oliver, B.Tran, A. (1999): The Size Effect and Derivative Usage in Japan .The Australian National University Working Paper Series in Finance, No. 99–02.

    • Search Google Scholar
    • Export Citation
  • Holmström, B.Tirole, J. (2000): Liquidity and Risk Management. Journal of Money, Credit and Banking, 32(3): 295319.

  • Horváth G. (2020): Vállalatok devizaárfolyam-kockázat kezelési szokásai a gyakorlatban (Corporate Foreign Exchange Risk Management in Practice) (in Hungarian). Thesis, Finance Master.

    • Search Google Scholar
    • Export Citation
  • Hoyt, R. E.Liebenberg, A. P. (2011): The Value of Enterprise Risk Management. Journal of Risk and Insurance, 78(4): 795822.

  • Hull, J. (2015): Risk Management and Financial Institutions. (4th ed.) John Wiley & Sons.

  • Joseph, N. L.Hewins, R. D. (1997): The Motives for Corporate Hedging among UK Multinationals. International Journal of Finance & Economics, 2(2): 151171.

    • Search Google Scholar
    • Export Citation
  • Kovács, E. (2009): Statistical Analysis of Financial Data (in Hungarian). Corvinus University of Budapest: Tanszék Kft.

  • Lechner, P.Gatzert, N. (2018): Determinants and Value of Enterprise Risk Management: Empirical Evidence from Germany. The European Journal of Finance, 24(10): 867887.

    • Search Google Scholar
    • Export Citation
  • Lessard, D. R. (1991): Global Competition and Corporate Finance in the 1990s. Journal of Applied Corporate Finance, 3(4): 5972.

  • Liebenberg, A. P.Hoyt, R. E. (2003): The Determinants of Enterprise Risk Management: Evidence from the Appointment of Chief Risk Officers. Risk Management and Insurance Review, 6(1): 3752.

    • Search Google Scholar
    • Export Citation
  • Lins, K. V.Servaes, H.Tamayo, A. (2019): Social Capital, Trust, and Corporate Performance: How CSR Helped Companies During the Financial Crisis (and Why It Can Keep Helping Them). Journal of Applied Corporate Finance, 31(2): 5971.

    • Search Google Scholar
    • Export Citation
  • Mian, S. L. (1996): Evidence on Corporate Hedging Policy. Journal of Financial and Quantitative Analysis, 31(3): 419439.

  • Modigliani, F.Miller, M. H. (1958): The Cost of Capital, Corporation Finance and the Theory of Investment. The American Economic Review, 48(3): 261297.

    • Search Google Scholar
    • Export Citation
  • Nocco, B. W.Stulz, R. M. (2006): Enterprise Risk Management: Theory and Practice. Journal of Applied Corporate Finance, 18(4): 820.

    • Search Google Scholar
    • Export Citation
  • Pagach, D.Warr, R. (2011): The Characteristics of Firms that Hire Chief Risk Officers. Journal of Risk and Insurance, 78(1): 185211.

    • Search Google Scholar
    • Export Citation
  • Settembre-Blundo, D.González-Sánchez, R.Medina-Salgado, S.García-Muiña, F. E. (2021): Flexibility and Resilience in Corporate Decision Making: A New Sustainability-Based Risk Management System in Uncertain Times. Global Journal of Flexible Systems Management, 22(2): 107132.

    • Search Google Scholar
    • Export Citation
  • Sheedy, E. (2006): Corporate Risk Management in Hong Kong and Singapore. Managerial Finance, 32(2): 89100.

  • Smith, C. W.Stulz, R. M. (1985): The Determinants of Firms' Hedging Policies. Journal of Financial and Quantitative Analysis, 20(4): 391405.

    • Search Google Scholar
    • Export Citation
  • Stulz, R. M. (1996): Rethinking Risk Management. Journal of Applied Corporate Finance, 9(3): 825.

  • Tirole, J. (2006): The Theory of Corporate Finance .Princeton University Press.

  • Tufano, P. (1996): Who Manages Risk? An Empirical Examination of Risk Management Practices in the Gold Mining Industry. The Journal of Finance, 51(4): 10971137.

    • Search Google Scholar
    • Export Citation
  • Voszka, É. (2021): The Dynamics of State Ownership: The Decisive Role of Crises. Acta Oeconomica, 72(2): 235257.

  • Walter, Gy. (2014): Practice of Corporate Financing (in Hungarian). Budapest: Alinea Fontes.

  • Zhao, X.Singhaputtangkul, N. (2016): Effects of Firm Characteristics on Enterprise Risk Management: Case Study of Chinese Construction Firms Operating in Singapore. Journal of Management in Engineering, 32(4): 05016008.

    • Search Google Scholar
    • Export Citation
1

On the hedging instruments and possibilities on the Hungarian market, see Walter (2014).

2

Kaiser-Meyer-Olkin (KMO) adequacy measure is the ratio of the sum of the square of the correlations and the sum of the square of the correlations plus the sum of the square of the partial correlations. If this value is below 0.5, the sample is not suitable for principal component analysis.

  • Aybars, A.Ataünal, L.Gürbüz, A. O. (2019): ESG and Financial Performance: Impact of Environmental, Social, and Governance Issues on Corporate Performance. In: Dincer, H.Yüksel, S. (eds): Handbook of Research on Managerial Thinking in Global Business Economics. IGI Global, pp. 520536.

    • Search Google Scholar
    • Export Citation
  • Batten, J.Mellor, R.Wan, V. (1993): Foreign Exchange Risk Management Practices and Products Used by Australian Firms. Journal of International Business Studies, 24(3): 557573.

    • Search Google Scholar
    • Export Citation
  • Baxter, R.Bedard, J. C.Hoitash, R.Yezegel, A. (2013): Enterprise Risk Management Program Quality: Determinants, Value Relevance, and the Financial Crisis. Contemporary Accounting Research, 30(4): 12641295.

    • Search Google Scholar
    • Export Citation
  • Beasley, M. S.Clune, R.Hermanson, D. R. (2005): Enterprise Risk Management: An Empirical Analysis of Factors Associated with the Extent of Implementation. Journal of Accounting and Public Policy, 24(6): 521531.

    • Search Google Scholar
    • Export Citation
  • Berlinger, E.Váradi, K. (2015): Risk Appetite. Pénzügyi Szemle/Public Finance Quarterly Journal of Public Finance, 60(1): 4962.

    • Search Google Scholar
    • Export Citation
  • Bihary, Z.Dömötör, B. (2018): How Do Manager Incentives Influence Corporate Hedging? Working Paper, Corvinus University of Budapest, Faculty of Economics.

    • Search Google Scholar
    • Export Citation
  • Bodnar, G. M.Hayt, G. S.Marston, R. C. (1998): 1998 Wharton Survey of Financial Risk Management by US Non-Financial Firms. Financial Management, 25(4): 113133.

    • Search Google Scholar
    • Export Citation
  • Bodnar, G. M.Gebhardt, G. (1999): Derivatives Usage in Risk Management by US and German Non‐Financial Firms: A Comparative Survey. Journal of International Financial Management & Accounting, 10(3): 153187.

    • Search Google Scholar
    • Export Citation
  • Bodnar, G. M.Giambona, E.Graham, J. R.Harvey, C. R.Marston, R. C. (2011): Managing Risk Management. In: AFA 2012 Chicago Meetings Paper.

    • Search Google Scholar
    • Export Citation
  • Bodnár, K. (2006): Exchange Rate Exposure of Domestic Small and Medium Size Corporations – Results of a Survey (in Hungarian). Hitelintézeti Szemle, 5(4): 131.

    • Search Google Scholar
    • Export Citation
  • Bodnár, K. (2009): Exchange Rate Exposure of Hungarian EnterprisesResults of a Surve. MNB Occasional Papers, No. 80.

  • Brown, G. W.Crabb, P.Haushalter, D. (2003): Are Firms Successful at Selectively Hedging? SSRN 385169.

  • Cheema-Fox, A.Laperla, B. R.Wang, H. S.Serafeim, G. (2021): Corporate Resilience and Response to COVID-19. Journal of Applied Corporate Finance, 33(2): 2440.

    • Search Google Scholar
    • Export Citation
  • Colquitt, L. L.Hoyt, R. E.Lee, R. B. (1999): Integrated Risk Management and the Role of the Risk Manager. Risk Management and Insurance Review, 2(3): 4361.

    • Search Google Scholar
    • Export Citation
  • Committee of Sponsoring Organizations of the Treadway Commission (2004): COSO Enterprise Risk Management – Integrated Framework.

  • Csillag, I. (2020): GDP and Beyond: Analysis of the Economic Policy in Hungary from 1998 –. Acta Oeconomica, 70(3): 333360.

  • Csóka, P.Hevér, J. (2018): Portfolio Valuation Under Liquidity Constraints with Permanent Price Impact. Finance Research Letters, 26: 235241.

    • Search Google Scholar
    • Export Citation
  • Davidovic, M. (2021): From Pandemic to Financial Contagion: High-Frequency Risk Metrics and Bayesian Volatility Analysis. Finance Research Letters, 42: 101913.

    • Search Google Scholar
    • Export Citation
  • Dömötör, B. (2014): Market Risk Hedging Under Liquidity Constraints. Dissertation. Corvinus University of Budapest, Doctoral School of Business Adminstration.

    • Search Google Scholar
    • Export Citation
  • Dömötör, B.Kovács, E. (2018): Determinants of FX Risk Hedging: Evidence from Hungary. Hungarian Statistical Review, 1(1): 2337.

    • Search Google Scholar
    • Export Citation
  • Farrell, M.Gallagher, R. (2015): The Valuation Implications of Enterprise Risk Management Maturity. Journal of Risk and Insurance, 82(3): 625657.

    • Search Google Scholar
    • Export Citation
  • Faulkender, M. (2005): Hedging or Market Timing? Selecting the Interest Rate Exposure of Corporate Debt. The Journal of Finance, 60(2): 931962.

    • Search Google Scholar
    • Export Citation
  • Froot, K. A.Scharfstein, D. S.Stein, J. C. (1993): Risk Management: Coordinating Corporate Investment and Financing Policies. The Journal of Finance, 48(5): 16291658.

    • Search Google Scholar
    • Export Citation
  • Gatzert, N.Martin, M. (2015): Determinants and Value of Enterprise Risk Management: Empirical Evidence from the Literature. Risk Management and Insurance Review, 18(1): 2953.

    • Search Google Scholar
    • Export Citation
  • Gillan, S. L.Koch, A.Starks, L. T. (2021): Firms and Social Responsibility: A Review of ESG and CSR Research in Corporate Finance. Journal of Corporate Finance, 101889.

    • Search Google Scholar
    • Export Citation
  • Golshan, N. M.Rasid, S. A. (2012): Determinants of Enterprise Risk Management Adoption: An Empirical Analysis of Malaysian Public Listed Firms. International Journal of Social and Human Sciences, 6(2012): 119126.

    • Search Google Scholar
    • Export Citation
  • Gordon, L. A.Loeb, M. P.Tseng, C. Y. (2009): Enterprise Risk Management and Firm Performance: A Contingency Perspective. Journal of Accounting and Public Policy, 28(4): 301327.

    • Search Google Scholar
    • Export Citation
  • Haushalter, G. D. (2000): Financing Policy, Basis Risk, and Corporate Hedging: Evidence from Oil and Gas Producers. The Journal of Finance, 55(1): 107152.

    • Search Google Scholar
    • Export Citation
  • Heaney, R. A.Koga, C.Oliver, B.Tran, A. (1999): The Size Effect and Derivative Usage in Japan .The Australian National University Working Paper Series in Finance, No. 99–02.

    • Search Google Scholar
    • Export Citation
  • Holmström, B.Tirole, J. (2000): Liquidity and Risk Management. Journal of Money, Credit and Banking, 32(3): 295319.

  • Horváth G. (2020): Vállalatok devizaárfolyam-kockázat kezelési szokásai a gyakorlatban (Corporate Foreign Exchange Risk Management in Practice) (in Hungarian). Thesis, Finance Master.

    • Search Google Scholar
    • Export Citation
  • Hoyt, R. E.Liebenberg, A. P. (2011): The Value of Enterprise Risk Management. Journal of Risk and Insurance, 78(4): 795822.

  • Hull, J. (2015): Risk Management and Financial Institutions. (4th ed.) John Wiley & Sons.

  • Joseph, N. L.Hewins, R. D. (1997): The Motives for Corporate Hedging among UK Multinationals. International Journal of Finance & Economics, 2(2): 151171.

    • Search Google Scholar
    • Export Citation
  • Kovács, E. (2009): Statistical Analysis of Financial Data (in Hungarian). Corvinus University of Budapest: Tanszék Kft.

  • Lechner, P.Gatzert, N. (2018): Determinants and Value of Enterprise Risk Management: Empirical Evidence from Germany. The European Journal of Finance, 24(10): 867887.

    • Search Google Scholar
    • Export Citation
  • Lessard, D. R. (1991): Global Competition and Corporate Finance in the 1990s. Journal of Applied Corporate Finance, 3(4): 5972.

  • Liebenberg, A. P.Hoyt, R. E. (2003): The Determinants of Enterprise Risk Management: Evidence from the Appointment of Chief Risk Officers. Risk Management and Insurance Review, 6(1): 3752.

    • Search Google Scholar
    • Export Citation
  • Lins, K. V.Servaes, H.Tamayo, A. (2019): Social Capital, Trust, and Corporate Performance: How CSR Helped Companies During the Financial Crisis (and Why It Can Keep Helping Them). Journal of Applied Corporate Finance, 31(2): 5971.

    • Search Google Scholar
    • Export Citation
  • Mian, S. L. (1996): Evidence on Corporate Hedging Policy. Journal of Financial and Quantitative Analysis, 31(3): 419439.

  • Modigliani, F.Miller, M. H. (1958): The Cost of Capital, Corporation Finance and the Theory of Investment. The American Economic Review, 48(3): 261297.

    • Search Google Scholar
    • Export Citation
  • Nocco, B. W.Stulz, R. M. (2006): Enterprise Risk Management: Theory and Practice. Journal of Applied Corporate Finance, 18(4): 820.

    • Search Google Scholar
    • Export Citation
  • Pagach, D.Warr, R. (2011): The Characteristics of Firms that Hire Chief Risk Officers. Journal of Risk and Insurance, 78(1): 185211.

    • Search Google Scholar
    • Export Citation
  • Settembre-Blundo, D.González-Sánchez, R.Medina-Salgado, S.García-Muiña, F. E. (2021): Flexibility and Resilience in Corporate Decision Making: A New Sustainability-Based Risk Management System in Uncertain Times. Global Journal of Flexible Systems Management, 22(2): 107132.

    • Search Google Scholar
    • Export Citation
  • Sheedy, E. (2006): Corporate Risk Management in Hong Kong and Singapore. Managerial Finance, 32(2): 89100.

  • Smith, C. W.Stulz, R. M. (1985): The Determinants of Firms' Hedging Policies. Journal of Financial and Quantitative Analysis, 20(4): 391405.

    • Search Google Scholar
    • Export Citation
  • Stulz, R. M. (1996): Rethinking Risk Management. Journal of Applied Corporate Finance, 9(3): 825.

  • Tirole, J. (2006): The Theory of Corporate Finance .Princeton University Press.

  • Tufano, P. (1996): Who Manages Risk? An Empirical Examination of Risk Management Practices in the Gold Mining Industry. The Journal of Finance, 51(4): 10971137.

    • Search Google Scholar
    • Export Citation
  • Voszka, É. (2021): The Dynamics of State Ownership: The Decisive Role of Crises. Acta Oeconomica, 72(2): 235257.

  • Walter, Gy. (2014): Practice of Corporate Financing (in Hungarian). Budapest: Alinea Fontes.

  • Zhao, X.Singhaputtangkul, N. (2016): Effects of Firm Characteristics on Enterprise Risk Management: Case Study of Chinese Construction Firms Operating in Singapore. Journal of Management in Engineering, 32(4): 05016008.

    • Search Google Scholar
    • Export Citation
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Senior editors

Editor(s)-in-Chief: Prof. Dr. Mihályi, Péter

Editor(s): Ványai, Judit

Editorial Board

  • Ádám Török (Chairman) / University of Pannonia; Budapest University of Technology and Economics
  • Edina Berlinger / Corvinus University of Budapest, Department of Finance
  • Beáta Farkas / Faculty of Economics and Business Administration, University of Szeged
  • Péter Halmai / Budapest University of Technology and Economics; National University of Public Service
  • István Kónya / Institute of Economics Centre for Regional and Economic Studies, University of Pécs
  • János Köllő / Institute of Economics Centre for Regional and Economic Studies
  • István Magas / Corvinus University of Budapest, Department of World Economy; University of Physical Education, Department. of Sports and Decision Sciences
 

Advisory Board

  • Ǻslund, Anders, Institute of International Economics, Washington (USA)
  • Kolodko, Grzegorz, Kozminski University, Warsaw (Poland)
  • Mau, Vladimir, Academy of National Economy (Russia)
  • Messerlin, Patrick A, Groupe d’Economie Mondiale (France)
  • Saul Estrin, London School of Economics (UK)
  • Wagener, Hans-Jürgen, Europa Universität Viadrina (Germany)

Corvinus University of Budapest
Department of Economics
Fővám tér 8 Budapest, H-1093, Hungary
E-mail: vanyai.judit@krtk.hu  

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2022  
Web of Science  
Total Cites
WoS
314
Journal Impact Factor 0.8
Rank by Impact Factor

Economics 334/380
TBA

Impact Factor
without
Journal Self Cites
0.6
5 Year
Impact Factor
0.8
Journal Citation Indicator 0.29
Rank by Journal Citation Indicator

Economics 421/581

 

Scimago  
Scimago
H-index
18
Scimago
Journal Rank
0.23
Scimago Quartile Score

Economics and Econometrics Q3

Scopus  
Scopus
Cite Score
1.1
Scopus
CIte Score Rank
Economics and Econometrics 521/705 (26th PCTL)
TBA
Scopus
SNIP
0.540

2021  
Web of Science  
Total Cites
WoS
285
Journal Impact Factor 0,939
Rank by Impact Factor Economics 326/379
Impact Factor
without
Journal Self Cites
0,646
5 Year
Impact Factor
0,740
Journal Citation Indicator 0,34
Rank by Journal Citation Indicator Economics 389/570
Scimago  
Scimago
H-index
15
Scimago
Journal Rank
0,285
Scimago Quartile Score Economics and Econometrics (Q3)
Scopus  
Scopus
Cite Score
1,4
Scopus
CIte Score Rank
Economics and Econometrics 436/696 (Q3)
Scopus
SNIP
0,507

2020  
Total Cites 275
WoS
Journal
Impact Factor
0,875
Rank by Economics 325/377 (Q4)
Impact Factor  
Impact Factor 0,534
without
Journal Self Cites
5 Year 0,500
Impact Factor
Journal  0,38
Citation Indicator  
Rank by Journal  Economics 347/549 (Q3)
Citation Indicator   
Citable 37
Items
Total 37
Articles
Total 0
Reviews
Scimago 13
H-index
Scimago 0,292
Journal Rank
Scimago Economics and Econometrics Q3
Quartile Score  
Scopus 225/166=1,4
Scite Score  
Scopus Economics and Econometrics 392/661 (Q3)
Scite Score Rank  
Scopus 0,668
SNIP  
Days from  289
submission  
to acceptance  
Days from  447
acceptance  
to publication  

2019  
Total Cites
WoS
212
Impact Factor 0,914
Impact Factor
without
Journal Self Cites
0,728
5 Year
Impact Factor
0,650
Immediacy
Index
0,156
Citable
Items
45
Total
Articles
45
Total
Reviews
0
Cited
Half-Life
3,9
Citing
Half-Life
9,5
Eigenfactor
Score
0,00015
Article Influence
Score
0,052
% Articles
in
Citable Items
100,00
Normalized
Eigenfactor
0,01891
Average
IF
Percentile
28,437
Scimago
H-index
12
Scimago
Journal Rank
0,439
Scopus
Scite Score
214/165=1,3
Scopus
Scite Score Rank
Economics and Econometrics 355/637 (Q3)
Scopus
SNIP
0,989

 

Acta Oeconomica
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Acta Oeconomica
Language English
Size B5
Year of
Foundation
1966
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per Year
1
Issues
per Year
4
Founder Magyar Tudományos Akadémia
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Address
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ISSN 0001-6373 (Print)
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