ANALYSIS OF THE EFFECTS OF FINANCING AND RISK ......Annals of the „Constantin Brâncuşi”...

Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Issue 2/2018

„ACADEMICA BRÂNCUŞI” PUBLISHER, ISSN 2344 – 3685/ISSN-L 1844 - 7007

ANALYSIS OF THE EFFECTS OF FINANCING AND RISK MANAGEMENT ON THE

VALUE OF FIRMS LISTED ON THE BUCHAREST STOCK EXCHANGE

MIHAELA BRÎNDUȘA TUDOSE

LECTURER PHD, FACULTY OF TEXTILE AND LEATHER ENGINEERING AND INDUSTRIAL

MANAGEMENT, ”GHEORGHE ASACHI” TECHNICAL UNIVERSITY, IASI, ROMANIA

e-mail:[email protected], [email protected]

VALENTINA DIANA RUSU

RESEARCHER PHD, DEPARTMENT OF INTERDISCIPLINARY RESEARCH IN SOCIAL

SCIENCES AND HUMANITIES, ”ALEXANDRU IOAN CUZA” UNIVERSITY, IASI, ROMANIA

e-mail: [email protected]

Abstract

The paper aims to assess the effects of financing and risk management on firm value of companies and to

provide additional evidence in the area of empirical debates. For these purposes, we performed a regression analysis

(based on the fixed-effects model), which reflects the effects of the two policies (i.e. financing and risk management) on

the value of the firms listed on the Bucharest Stock Exchange. The dependent variable was value of the firm; we used

Tobin’s Q as a proxy. Independent variables were: financial structure, risk management, financial return and firm size.

The findings reveal that two of the four examined variables yield significant positive effects on the value of firms

(financing structure and return on assets); for the other two independent variables analysed (risk management and firm

size), the hypotheses were rejected.

Keywords: financial structure, risk management, return on assets, firm size, firm value.

Clasificare JEL: G32

1. Introduction

A review of the diverse range of debates that seek to reveal the real effects of financing will

clearly show that the attention of researchers is focused on performance and on firm value,

respectively. Investigating the relationship between capital structure and firm performance/value is

particularly important given that the maximisation of shareholder wealth depends on the link

between debt level and the firm’s performance.

In the same context, of key interest are the theoretical arguments and empirical evidence on

corporate risk management as a lever for creating value for shareholders. In particular, we refer to

studies that suggest that shareholders’ wealth can be enhanced by hedging against capital risks and

taking advantage of capital market imperfections that lead to underinvestment and asset substitution

problems.

In the area of theoretical debates, universally and unconditionally, it is acknowledged that

financing policy is essential to ensuring the long-term survival of the firm and that risk

management is required due to the potential impact of risk factors on the firm’s performance.

Empirical studies show that there is mixed (conflicting) evidence regarding the effects of financing

and that there is not yet sufficient evidence for the effects of risk management.

The experience has showen that firms that are still using a traditional risk management

approach are not able to sustain their performance because of the complexity and fast changing

business environment. To help the firms, the traditional risk management model has been replaced

by an enterprise-wide view of risk rapidly, enterprise risk management (ERM) system. The

difference between ERM and traditional ways of managing risks is in how the entity centralizes

comprehensive risk management structure and processes at strategic level as an extension of its

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control system. ERM is a comprehensive and integrated approach that calls for high-level oversight

of the firm entire risk portfolio aligned with the strategic objectives of the firm, instead of having

many different individual managers to oversee specific risks in isolation.

The main objective of this paper was to examine the effects of financing and risk

management on the value of companies listed on the Bucharest Stock Exchange and to provide

additional evidence in the area of empirical debates.

The study is structured as follows: section two presents the state of knowledge about the

effects of financing and of risk management (outlining the main areas of research and conclusions

reached); the database and the methodology of the research are detailed in section 3; section 4 lists

the findings of the research, while the last section summarises the conclusions.

2. Literature review

2.1 Effects of financing. Most researchers agree that the assessment of financing effects is

dependent on the input of information into the measurement system and the tools employed. In

addition to the classical indicators used in financial analysis (return on investment, leverage, capital

efficiency, liquidity, cash flow), the so-called modern indicators of value creation come into play.

Special attention is given to proxy variables for estimating firm value. The most relevant example

is the use of Tobin’s Q variable to estimate firm value; the indicator features consistently in recent

major research.

Although the ultimate purpose of research on the topic was the same (to identify an optimal

debt levels that generates the estimated effects), findings have been contradictory:

- some studies have provided empirical evidence in favour of the positive correlation

between capital structure and firm performance/value [12; 20; 40; 41; 1];

- other studies have provided evidence supporting a negative correlation [29; 27; 2];

- other studies have shown that below a certain debt level, the firm’s performance tends to be

negatively correlated with its share of debt [10];

- other studies have delivered mixed evidence given that, to assess the financial structure,

debt was examined in terms of maturities or depending on whether firms belong to various sectors

or industries, having different growth opportunities [37; 24; 28].

2.2 Risk management. Cases when derivatives are used are premised on the assumption of the

existing positive theories, which contend that risk management at firm level, amid capital market

imperfections, is beneficial to company shareholders. Although these theories suggest various ways

in which hedging against corporate risks can enhance shareholder value (for instance, reducing the

costs of financial distress), empirical evidence remains controversial and a common view has not

been reached.

In order to assess the effects of risk management on firm value, proxy variables (such as the

Tobin’s Q variable) are used frequently, factoring in the fact that a firm should invest up to the

point where its market value becomes equal to its book value. Empirical studies have provided

conflicting evidence.

As we have shown in the case of the financing of firms, empirical tests of the effects of

hedging on the firm’s value are affected by endogeneity problems; in other words, the impact of

risk management on firm value is largely dependent on the range of hedging instruments selected

and the sample selection models. For instance, Bartram et al. (2011) studied a global sample of

6896 non-financial firms in 47 countries and provided rigorous evidence that the use of financial

derivatives reduces the risk to the firm, but also some evidence that the use of derivatives is

correlated with higher firm value [6].

To sum up, positive theories of value creation through corporate risk management and their

corresponding extensive body of empirical evidence suggest, as a backdrop, that the derivatives

used occupy a central place in research. For example, Bartram et al. (2009) showed that the use of

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derivatives is part of the company’s financial strategy, which requires knowledge of the following

aspects [8]: the type and magnitude of financial risks, the availability of risk management tools, and

the firm’s operating environment. Studies have also shown that the use of derivatives is linked to

debt levels and maturity, dividend policy, the liquidity of operating assets and the extent of hedging

[5] At the same time, it is worth recalling that not all companies use derivatives, but rather rely on

traditional instruments hedging [7; 22].

To meet the risk, nowadays, the enterprises implement enterprise risk management (ERM)

systems. There are some studies that have empirically analyzed the impact of ERM systems on

performance of companies. Thus, Gordon et al (2009), based on a sample of 112 US firms, showed

that organizations are improving their performance by employing the ERM concept [15]. The basic

argument presented in this paper is that the relation between ERM and firm performance depends

on a series of factors which are affecting the firm: environmental uncertainty, industry competition,

firm size, firm complexity, and board of directors’ monitoring.

Quon et al. (2012) have explored 156 non-financial firms and have analyzed the relationship

between Enterprise Risk Management and firm performance, during 2007 and 2008. Their results

showed that ERM information did not predict or have any appreciable effect on business

performance. So, their conclusion is that the assessed levels of economic or market risk exposure or

consequences are related to firm performance.

Ping and Muthuveloo (2015) examined the implementation of Enterprise Risk Management

(ERM) on firm performance of Public Listed Companies on main market in Bursa Malaysia using a

questionnaire survey [35]. Their results showed that the implementation of ERM have significant

positive influence on firm performance. Other variables, such as firm size and firm complexity,

were also found to significantly influence the relationship between ERM implementation and firm

performance.

More recent studies, Florio and Leoni (2017) have investigated if there is a relationship

between the implementation of enterprise risk management (ERM) systems and the performance of

Italian listed companies [13]. The results show that firms with advanced levels of ERM

implementation present higher performance, both as financial performance and market evaluation.

They also highlight that the relationship between ERM implementation and firm performance in an

under-investigated context in many countries. The paper of Kopia et al (2017) show that there is

diversity in scientific literature of how to measure performance in the ERM-context. They try to

develop a unified view of how ERM influences performance of organizations.

3. Matherial and methods

3.1 Establishing the coordinates of the sample being researched

Sample. To ensure relevant outcomes, we aimed for the research to be based on companies

subject to comparable tax treatments, common bankruptcy rules, comparable market rules, similar

financial traditions. Prompted by these considerations, we decided to perform the empirical

research on a sample of 90 non-financial companies listed on the Bucharest Stock Exchange.

As regards the time frame of the analysis, we considered the 2006-2011 interval, precisely in

order to capture the particular evolution of the examined phenomena before and during the crisis.

• The sources underpinning the database that was used in the empirical research. The data

were collected from two sources: a) the annual financial statements and the related explanatory

notes (available both on the website of the Ministry of Finance, in the business operators

information section, and on the website of each individual firm); b) the information available on

website of the Bucharest Stock Exchange and other investment firms.

• Nature of the data. Initially, we considered market values for the purposes of the analysis;

however, given that for many variables market values are not available (or difficult to secure within

reasonable timeframe) we turned to a mix between book values and market values.

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• Identifying the theoretical underpinnings of the research. Researching how financing is

secured by firms listed on the Bucharest Stock Exchange and their attitudes on risk, we found

arguments to associate these companies with pecking order theory.

3.2 Presenting the examined variables and defining the hypotheses

The dependent variable - firm value. We used Tobin’s Q as a proxy for firm value. In

agreement with the purpose of this research, we used as benchmark the following determination

formula: TA

DVPfTobin'sQ , where: VPf – the firm’s market value (resulting from multiplying the

market value of the share and the number of shares); D – debts (book value); TA – book value of

total assets.

Independent variables – financial structure, risk management, financial return and firm size.

a) Financial structure. To control for the relationship between capital structure and firm

value variable, we used leverage (LEV) as variable (assessed in terms of the ratio between total

debt and equity). Introducing this variable in the analysis is justified for two reasons:

• it provides a picture of how the firm succeeds in combining the two sources of financing

(own and borrowed) to fund investments; the advisability of resorting to various financing sources

depends on the benefits that each situation in part offers; for example, taking on debt provides the

opportunity to make investment much easier than financing through issuing new shares; as an effect

of the tax advantages that it provides, debt offers the opportunity to create value for parties

involved;

• there is empirical evidence that the financial leverage may enhance firm value:

- Aggarwal and Kyaw (2008), who constructed their arguments based on the idea that debt

compels managers to channel the cash flows towards “productive” goals, thus avoiding “wasteful,

petty” spending [3];

- Kayo and Kymura (2011) have shown that the concerns of companies to optimise their

financial structure (in the context of minimising financing costs) translate into a beneficial impact

on firm value [20].

Although we associated the financing behaviour of firms in the sample with pecking order

theory (which estimates a negative correlation between the debt level and firm performance/value),

in view of the low debt level (total and short-term) of firms in the sample, we considered as more

relevant the option for the positive correlation; we hypothesised:

H1: Leverage yields positive effects on the value of the firm.

b) Risk management. To control for the relationship between risk management and firm

value, we used a dummy variable, for which we defined the following values: 1 - for companies

that implemented risk management programs and 0 - for companies without interests in the area of

risk management.

The option to include this variable in the analysis is justified on two grounds:

• it addresses the objective of the research (by highlighting the effects of risk management on

firm value);

• it is frequently used in scientific research in the field, for example:

- Pagach and War (2010) have argued (and have provided empirical evidence) that risk

management creates value as it reduces the volatility of net cash flows [30];

- Liebenberg and Sommer (2008) have shown a positive and significant correlation between

managing risks and firm value [23];

- Hoyt and Liebenberg (2011) have provided evidence of increased firm value through risk

management (with the rise ranging between 3.6% and 17%) [18].

- Allayannis and Weston (2001) estimated this increase at 4% [4].

In light of the remarks made above, we formulate the second hypothesis:

H2: Risk management results in enhanced firm value.

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c) Return on assets. In finance, the return on assets reflects the firm’s capacity to generate

profit; in other words, the return on assets reflects the extent of the effective use and management

of assets. The return on assets (ROA) was calculated using the ratio of current profit to total assets.

Accepting profitability as an independent variable is justified on the following grounds:

- it incorporates the cumulative effect of using the two financing sources;

- it is dependent on the extent to which the firm hedges against risks;

- it highlights both sides of profit: a source of compensating equity providers (shareholders)

and evidence of the remuneration capacity of debts;

- it is consistent with Tobin’s Q general theory, which suggests that if a firm is able to

generate net income, then value is created;

- there is empirical evidence for the positive impact of return on assets on firm value [25; 16;

14; 32; 39].

In view of the arguments above, we put forward a third hypothesis:

H3: A positive relationship exists between return on assets and firm value (i.e. the cumulative

effects of financing and risk management reflect first on profitability and then on firm value).

d) Firm size, assessed in terms of the size of its assets. Two reasons supported the inclusion

of this variable in the analysis.

On the one hand, firm size is a factor that exerts a large influence on the financial structure of

firms. Regarding the theoretical underpinnings, in early research there are common views on the

relationship between firm size and leverage (trade-off theory and pecking order theory).

Subsequently, examining the link between firm size and leverage, some authors (Prahalathan,

2010) reported conflicting points of view [36]. Acknowledging that large firms may have a higher

debt-carrying capacity, Byoun (2008) argues that large firms, being generally more transparent,

tend to have higher debt levels, while the diversification of debt alternatives may enable a reduction

in debt issuance costs [11]. On the contrary, for smaller firms, as financial institutions must allocate

more resources towards monitoring them, they may “penalise” them, by demanding higher interest

rates [33]. Starting from the assumption that, for large firms, size is positively correlated with

leverage, other authors [9], based on empirical research, have concluded that, in the case of small

firms, firm size is negatively correlated with leverage. The results of research done by Hidayah

(2014) shows that firm size have positive and significant impact on the firm value [16]. A big

company is able to access the capital market more easily, thus the company is flexible and has an

ability to obtain funds. Other studies [34; 19] have also highlited that firm size has a week positive

impact on the value of the firm.

On the other hand, firm size is closely correlated with the firm’s ability to implement risk

management programs; empirical evidence (arguing that large firms are more likely to engage in

risk management programs as they are more complex, face a wider range of risks and possess the

institutional dimension to be able to bear the costs of risk management programs) has been

presented by Hoyt and Liebenberg (2011). [18]

Given that most of the companies listed on the Bucharest Stock Exchange fall under the large

company heading, the fourth hypothesis that we submit is the following:

H4: A positive relationship exists between the size and value of the firm.

3.3 Defining the statistical model

To illustrate the effects of financing and risk management on firm value, we shall use the

following regression:

TOBIN’S Qi = β0 + β1 x ERMi + β2 x LEVi + β3 x ROAi + β4 x SIZEi + εi,

where: TOBIN’S Qi – dependent variable, proxy for firm value; ERMi – extent of engagement

in risk management policies; LEVi – leverage (as illustration of the financial structure); ROAi –

return on assets; SIZEi – firm size; εi – error term.

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4. Results and discussions

4.1 Descriptive statistics. The first step of the modelling was to analyse the distribution of

values for the examined variables (table 1).

Table no. 1 Descriptive statistical indicators

Variable Median Average Mode Standard error Coefficient of

skewness

Coefficient of

kurtosis

SIZE 118298 631673 1878 2895878.321 8.840 84.451

ROA 0.01980 0.04883 0.0000 0.1268313 12.357 206.251

LEV 0.665 1.311 0.18 2.57464 6.799 63.817

Tobin’s_Q 0.9577 1.3370 0.8239 1.3176781 3.829 20.968

Source: Data processed by the author (R-program)

Examining the results in Table 1, one can notice that the values of the three fundamental

average measurements (average, median, mode) are not equal; consequently, we consider that the

data series is skewed. For the analysed variables, the standard error is higher than the average. In

this situation, there is a lower concentration of data around the mean, the distributions being non-

homogeneous. The skewness coefficient has positive values. These positive values indicate the

presence of positively skewed distributions, skewed to the right. For the kurtosis coefficient too,

values above 0 were recorded. They indicate the presence of leptokurtic distributions. Because

Skewness and Kurtosis statistics are sensitive to distribution anomalies, we deem appropriate to

examine the histogram and the normal Q-Q Plot (Figure 1); this step is required to confirm the

validity of the previous assessments.

Figure no. 1 Histograms and Normal Q-Q Plots illustrating the distribution of the values of the

analysed variables (SIZE, ROA) Source: Data processed by the author (R-program)

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Figure no. 1 (continued) Histograms and Normal Q-Q Plots illustrating the distribution of the

values of the analysed variables (LEV, Tobin’s Q) Source: Data processed by the author (R-program)

As the values of some of the examined variables do not have a normal distribution,

logarithmisation must be employed. The outcomes of logarithmisation are presented in Figure 2.

Figure no. 2 Histograms and Normal Q-Q Plots illustrating the distribution of the logarithm values

of the analysed variables

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Figure no. 2 (continued) Histograms and Normal Q-Q Plots illustrating the distribution of the

logarithm values of the analysed variables Source: Data processed by the author (R-program)

The graphical analysis of the histograms confirms the presence of skewed distributions and

leptokurtic distributions for the analysed variables.

The second step of the research was to verify the nature of the average value of the “Tobin’s

Q” indicator (whether it is representative during the entire period or differs one year to another);

such examination enables us to identify whether the influence of independent variables on

dependent variable is constant or varies during the period. To get an insight into the distribution of

the variable values ”Tobin’s Q” of the firms under review, during the 2006-2011 period, we built

the Box-Plot diagram for the 2006-2011 period (Figure 3).

Figure 3 Box Plot


We observed that the Box-Plots are different in size and that there are some outliers. This

indicates that there are significant differences in the values for the “Tobin’s Q” variable in the first

part of the examined period. The median for the year 2006 is significantly different from the

medians of other years. It also shows that the medians of the last 3 years (2009, 2010, 2011) are

equal. In other words, we can argue that there are significant differences between the average

values of the “Tobin’s Q” variable at the start of the period (2006, 2007, 2008). The variation in the

average value of the “Tobin’s Q” indicator can be assigned to the dynamism of the business

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environment in the period prior to the crisis; in a dynamic environment, influences not only

multiply, but also amplify; once the crisis set in, the business environment became more rigid,

reducing the amplitude of changes in the examined variable.

4.2 Regression analysis. To perform the analysis of the effect of independent variables

(ERM, LEV, ROA, SIZE) on the dependent variable (Tobin’s Q), we opted for the LMEM (Linear

Mixed Effects Models). The arguments supporting the decision to use this model were: a)

compared to other models, the model captures more effectively the effects of independent variables

on the dependent variable and b) it allows not only the identification of effects but also their

quantification.

The estimation of this model was carried out taking into account the ownership structure of

the examined firms, assessed in terms of the proportion of share capital held; given the low

homogeneity of the shareholder structure of the Romanian companies listed on the Bucharest Stock

Exchange, to assess the shareholder structure we considered that a holding of at least 30% of the

shares issued denotes a majority shareholder (which is why we defined the following values: 1 for

majority shareholder firm; 0 for firms with unrepresentative (fragmented) shareholding. The results

of the estimated model are summarised in Table 2.

Table no. 2 Detailed of the estimated model Coefficients Estimate Std. Error DF t value Pr (>|t|)

Intercept -0.1413299 0.05607981 534 -2.520157 0.0120

SIZE 0.0000000 0.00000001 534 -0.612129 0.5407

ROA 0.7531134 0.24028599 534 3.134238 0.0018

LEV 0.0604353 0.01185574 534 5.097556 0.0000

ERM 0.1392702 0.06420670 534 2.169092 0.0305

AIC 1210.723 σu = 0.04526406 BIC 1240.699 σe = 0.7014136 logLik -598.3613


The preliminary results (produced by processing the original variables) show that return on

assets (ROA), leverage (LEV) and the extent to which firms are engaged in implementing risk

management policies (ERM) exert a significant effect on firm value (Tobin’s Q). Based on these

findings, the first three hypotheses are verified.

H1. Considering the results presented in table 2, one can note that the financial structure has

a significant and positive effect on the value of firms listed on the Bucharest Stock Exchange

(6.04% of the variation in firm value being attributed to the financial structure). Compared to the

other examined independent variables, this variable yields much more significant effects.

Consequently, the first hypothesis (Leverage yields positive effects on the value of the firm) can be

accepted.

H2. The results in table 2 show that risk management policies present a positive and

significant correlation with firm value (13.92%). Therefore, the second hypothesis is also validated

(risk management results in enhanced firm value).

H3. Based on the data in the table referenced to validate the previous hypotheses, the third

hypothesis too is accepted as valid. Return on assets has a significant and positive effect on firm

value. As a result, the third hypothesis too is accepted (a positive relationship exists between return

on assets and firm value).

H4. Compared to the other hypothesis, the one stating that a positive relationship exists

between the size and the value of the firm is rejected, since the findings of the analysis reveal a

negative and insignificant correlation.

To identify the model that best accounts for the data structure (and estimated effects), we

propose to also analyse the correlation between the independent variables (SIZE, ROA, LEV and

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ERM) and the dependent variable using a joint factor model (ROA-SIZE, ROA-LEV and ROA-

ERM). The results for this new model are presented in Table 3. The following interdependent

arguments served to steer the analysis towards this model: a) the ultimate goal of such analyses is

forecasting; b) the quality of predictive formulas depends on the quality of research results; c) the

aim of enhancing the scientific value of the research.

Table no. 3 Details of the model that incorporates joint factors Coefficients Estimate Std. Error DF t value Pr(>|t|)

Intercept -0.1438220 0.0484419 531 -2.968962 0.0031

SIZE 0.0000000 0.0000000 531 -1.342086 0.1801

ROA 0.5587218 0.2978546 531 1.875820 0.0512

LEV 0.0636463 0.0122183 531 5.209114 0.0000

ERM 0.0976228 0.0762785 531 1.279822 0.2012

ROA*SIZE 0.0000003 0.0000003 531 1.169872 0.2426

ROA*LEV 0.1246861 0.3076311 531 0.405310 0.6854

ROA*ERM 0.7100372 0.6968657 531 1.018901 0.3087

AIC 1241.356 σu = 0.02665756

BIC 1284.122 σe = 0.7015454 logLik -610.678


According to the second estimated model, the results yielded indicated that the return on

assets (ROA) and leverage (LEV) alone have a positive and significant effect on firm value (Tobin’s

Q). In this case, only two of the four hypotheses are accepted:

H1: Leverage yields positive effects on the value of the firm – validated hypothesis;

H2: Risk management results in enhanced firm value – rejected hypothesis;

H3: A positive relationship exists between return on assets and firm value – validated

hypothesis;

H4: A positive relationship exists between the size and the value of the firm – rejected

hypothesis.

We use the ANOVA function to determine which of the two models (the first, which

processes the original variables and the second, which processes the associated variables) better

explains the data structure. The results are presented in Table 4.

Table no. 4 The results yielded by the comparison of the two models Model df AIC BIC logLik Test L.Ratio p-value

Model 1 7 1210.723 1240.698 -598.3613 - - -

Model 2 10 1241.356 1284.122 -610.6780 1 vs 2 24.63339



Figure no. 4 Plots used to verify the normality of errors for the chosen model Source: Data processed by the author (R-program)

The condition of normality of the distribution of errors within the groups can be verified

using the third plot. The plot indicates that the normality condition for the distribution of errors is

met.

The last representation in Figure 4 allows the examination and assessment of the distribution

of the corresponding random effects. The chart does not indicate any deviation from the normality

condition of the distribution of random effects.

4.3 Estimating the compatibility of the findings of the study with the findings of previous

research. Because, during the documentation, we did not identify any studies exploring Romanian

enterprises exclusively (and those listed on the Bucharest Stock Exchange, respectively), we will

refer to the findings of studies of firms listed on the stock exchanges of other countries. A useful

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tool in this respect was the summary drawn up by Aretz and Bartram (2010) [5], comprising the

theoretical arguments and the empirical evidence in the literature on the financial structure and

corporate risk management as levers for value creation.

a) The hypothesis which states that the financial structure (interpreted in terms of leverage)

yields positive effects on firm value has also been confirmed by:

- Highland Global (2008), which showed that capitalising on the beneficial effects of

leverage (in the context of financial structure adequacy) aims to minimise the weighted average

cost of capital; as a result, high leverage enhances firm value [17];

- Chowdhury and Chowdhury (2010), who investigated the impact of the various variables

(including capital structure) on firm value, yielding evidence in support of the positive effect of

leverage on firm value [12];

-Tongkong (2012) who has observed that higher profitability firms tend to have less debt and

firms with higher growth opportunities tend to have greater leverage [41];

- Adenugba, et al. (2016) have highlighted the positive significant relationship between

financial leverage and firms’ value for Nigerian listed firms [1].

b) The hypothesis stating that risk management results in enhanced firm value has been

confirmed by:

- Hoyt and Liebenberg (2011) confirmed this correlation for a sample of 275 U.S. firms (over

a period of 11 years) [18];

- Woon et al. (2011) demonstrated that the implementation of risk management programs

contributes to boosting firm value (through the low cost of capital thanks to the decreasing risk

premium) [42];

- Pagach and Warr (2011) showed that firms embark on risk management programs as they

grow aware of direct economic benefits (and not necessarily to comply with any particular rules)

[31];

- other authors examining the correlation between risk management and firm performance

have found evidence in support of the positive correlation [18];

- Ping and Muthuveloo (2015) showed that the implementation of a risk manangement

system has significant positive influence on firm performance [35];

- more recent studies [13; 21] show that firms with advanced levels of ERM implementation

present higher performance, both as financial performance and market evaluation.

In light of the fact that, by using the second model (based on joint factors), the second

hypothesis was rejected, we emphasise that similar results (i.e. rejection of the hypothesis) were

reported by Tahir and Razali (2011) and also by Quon et al (2012).

c) The positive relationship between return on assets (ROA) and firm value has been proven

empirically as part of the following studies: Andersen (2008), who analysed the performance of

multinational firms (premised on low financial leverage); Mohamad and Saad (2010); Hidayah

(2014); Gamayuni (2015); Paminto et al (2016); Sudiyatno et al. (2017) [25; 16; 14; 32; 39]. This

result is in keeping with Tobin’s Q general theory, which suggests that if a firm is able to generate

net revenue, then value is created.

d) The positive relationship between size and firm value has also been invalidated by

Allayannis and Weston (2001) [4] and Mule et al (2015) [26].

5. Conclusions

The analysis of the effect of independent variables (ERM, LEV, ROA, SIZE) on the dependent

variable (Tobin’s Q) was performed based on linear mixed effects model (LMEM). The findings of

the analysis resulted in the confirmation of the first three hypotheses and rejection of the fourth.

Subsequently, to identify the model that best accounts for the data structure, we analysed the

relationship between the independent variables (SIZE, ROA, LEV and ERM) and the dependent

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variable, using a model that incorporates a combination of factors (ROA-SIZE, ROA-LEV and ROA-

ERM). The results of this new model showed that only the first and third hypotheses were

validated, while the other two were rejected.

To determine which of the two models better explains the structure of data, we used the

ANOVA function, which showed that the second model (incorporating factors and joint factors)

better explains the data structure. We tested the normality conditions of errors for the joint factor

model and showed that the normal distribution condition of errors is met (which validated the

second model). Analysing the distribution of random effects, we have shown that there are no

deviations from the normal distribution condition of random effects (which marked the second

validation of the second model).

The findings reveal that two of the four examined variables yield significant positive effects

on the value of firms (financing structure and return on assets); for the other two independent

variables analysed (risk management and firm size), the hypotheses were rejected.

Although we encountered difficulties in observing economic phenomenon, the causes and

factors that determine it, we sought to ensure that this aspect should not act a limit to our research.

The paper serves as a starting point for conducting researches future, more extensive.

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ANALYSIS OF THE EFFECTS OF FINANCING AND RISK ......Annals of the „Constantin Brâncuşi”...

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