Economic Value Added Analysis for Enterprise Risk Management.
Enterprise Risk Management (ERM) is a prevalent approach to risk management. ERM was introduced in 1990's and has attracted extensive global attention including by all types of organizations in Malaysia (Ghazali and Munab, 2013). The main objective of any organization is to maximize their shareholders' value. Towards this objective realization, companies endeavor in improving their shares value by increasing the company's earning capabilities. In addition, successful enterprises intentionally create new business risks; to create value for their shareholders (Lai F.W. et al., 2011). Hence, it is crucial to manage those risks.
Organizations need proper risk management program which enables identification, treatment and management of risk. According to reference Nicolas and Walker (2012), a pressure has increased on the companies in the US to identify and manage their risks. Furthermore, Security Exchange Commission of the US passed the rule 33-9089 in 2009 emphasizing risk oversight by the board of directors. The influences of external and internal factors to businesses, as well as the rapid growth of economies, have also triggered more demand and enforcement of proper risk management. Despite all of the above, nonetheless, an issue to contemplate is whether ERM implementation within the firm improves performance. Some studies stated that organizational ERM implementation do not have any effect on a firm's value (Tahir and Razali, 2011; McShane et al., 2011). On the other hand, however, studies by Shad and Lai (2015); Hoyt and Liebenberg, (2011); Lai (2014); Yazid et al., (2011) provide evidences that ERM has significant positive impact on the firms performance.
This study was undertaken under the backdrop of the contradictory findings from the literature pertaining to ERM impact on firm's performance. Specifically, this study seeks to validate if ERM implementation brings positive results to a firm's performance by employing Economic Value Added analysis. As such, this study attempts to provide an empirical evidence on the effect of ERM implementation by adopting EVA analysis as a proxy for a firm's performance measurement. EVA is a better measurement tool as it computes performance by incorporating a firm's cost of capital. The objective of this study, therefore, is to investigate the impact of ERM implementation on firm performance by employing Economic Value Added (EVA) analysis as proxy for firm performance.
Enterprise Risk Management (ERM) and Firm Performance
Current literature provides mixed empirical evidences and argument on the relationships between enterprise risk management and firm performance. Numerous studies concluded positive relationships between ERM and firm performance. Hoyt and Liebenberg (2011) provides evidences for positive impact of ERM in US insurance companies in terms of firm value proxied by Tobin's Q ratio. Hoyt and Liebenberg conclude an approximate 20 percent premium over the value of a firm due to ERM. According to Lai (2014), ERM implementation in organizations facilitate easier access to debt markets, reduce systematic risks and hence lowering risk premium from which the firm's cost of capital can be reduced. ERM implementation also enhance the firm's market valuation through price to earnings ratio of the firm's share since investors are willing to pay premium for the firm's shares due to the perceived improved risk profile. The findings of Lai (2014) is consistent with that of Hoyt and Liebenberg (2011).
ERM enhances firm's performance in terms of EVA by enhancing net operating margin and reducing capital structure costs (cost of debt and cost of equity). A company's capital structure is important as it ensures the firm's capabilities of meeting their shareholders' needs. These needs are met through dividend payments, debt servicing and other financial obligation such as paying of salaries. Moreover, capital structure is a significant part of EVA analysis. EVA comprises three main elements namely; Net Operating Profit after Tax (NOPAT), Weighted Average Cost of Capital (WACC) and Invested Capital (IC). This study shows there is a relation between ERM and firm performance enhancement whereby risk management can improve NOPAT and reduce cost of capital.
Impact of ERM on Net Operating Profit After Tax (NOPAT)
One of the advantages of enterprise risk management implementation cited by Lai (2014) is that of enhancing enterprise profitability. ERM provides an accurate guide for decision making, planning, control design and implementation. ERM improve the awareness of risks within the firm which helps in making better operational and strategic decisions (Nicolas and Walker, 2012). Better decision making enables the management to meet strategic goals, reduce earnings volatility, and increase profitability. ERM leads to higher sales by coping operational risks faced by companies. These risks have an inverse relationship with income derived by the firm. Risk monitoring and reporting can reduce operational risks, and enable the firm to focus on resources, creativity and development of different internal and environmental activities, which increases the revenue of the firm.
According to reference Hoyt and Liebenberg (2011), risk management can minimize the fluctuation of reported income. Lowering taxable income volatility will result in reducing tax payments over a complete business cycle as this will help in predicting corporate income tax to falls within an optimal and manageable range of tax brackets. The increase in sales revenue and profitability, coupled with the lowering of tax payments and cost of goods sold due to ERM shall impact the NOPAT component of the enterprise's EVA in a positive manner. Therefore, this study hypothesizes that:
[H.sub.1]: ERM implementation has positive effect on Net Operating profit after tax of the firms.
Impact of ERM on Weighted Average Cost of Capital (WACC)
Enterprise risk management enables the firms to improve the information about their risk profile. Risk profile is the description of set of risks that a company is willing to take. A firm can reduce its cost of capital by sharing the accurate and exact information about the risk profile of the firm among the shareholders. The accurate disclosure of information is significant for the firms with multifarious operations, as these firms are tough to evaluate from outside. Disclosure and distribution of improved information with shareholders assist to reduce the information asymmetries and lead to minimize the cost of capital of the firm.
According to COSO (2004), ERM reduces firm's overall risk by reducing the firm's earning volatility and improve capital structure. Based on Opoku et al. (2014), capital structure is the composition of debt and equity financing required by the firm to finance their assets. Ramly and Rashid (2010) posits that the ultimate aim of risk management mechanism is linked to creating value by reducing firm's cost of capital. The value maximization literature argue that ERM minimizes the cost associated with external financing, reduce corporate taxes, and agency costs. This in turn, will lead to the reduction in cost of capital and hence, lowering the WACC component of the firm's EVA. Therefore, we hypothesize that:
[H.sub.2]: ERM implementation has a positive effect on reducing Weighted Average Cost of Capital.
Impact of ERM on Return on Invested Capital (ROIC):
Enterprise risk management program helps business enterprises in proper circulation of funds, speeding up cash flow, reducing enterprise risks, and improving profitability. To boost the EVA it is crucial for the firm to increase the return on the invested capital. Invested capital of the firm is the fund provided for the firm to run its business. From the accounting perspective, a firm's capital is given by its long-term debts and owners' equity. ERM enables organization to avoid bankruptcy cost and improve profitability. According to Chang and Chen (2013), the bankruptcy of many enterprises are caused by poor risk management. The increase in volatility in return on invested capital increases the chance of bankruptcy. As such, to avoid the possibility of bankruptcy investors invest their capital in the firm with low business risks.
Enterprise risk management implementation ensures that the ownership of the company will not be transferred to debt holders through bankruptcy since debt holders will have the first claim of the firm's assets in case of winding up. A firm can receive the advantages of tax treatment (interest tax shield benefits) if its capital is financed by debt. Nonetheless, a high gearing level in the firm's balance sheet will add burden to servicing the debt in terms of interest expense. Hence, the firm needs to consider the bankruptcy and agency costs associated with debts financing. In the above light, the Trade-off theory argues that "firms trade off the benefits of debt and equity financing through an optimal capital structure that will minimize the cost of capital and maximize the firm value". ERM, in this context, can contribute to minimizing the negative aspects associated with debt financing while amplifying its benefits. On that basis, we develop our hypothesis that:
[H.sub.3]: ERM implementation has a positive effect on return on invested capital.
Figure 1 shows the key elements of the study. The research framework features an ERM implementation framework which highlights the positive and significant relationships with firm performance to be measured through EVA analysis. The research framework sees ERM framework as an independent variable whose implementation will have an impact on the firm NOPAT, WACC and ROIC the dependent variables.
Research Design and Methodology:
The study used both primary and secondary data. Primary data on ERM implementation (independent variable) is collected through email and drop-off survey method where the researchers went to the respondents' offices to hand over the questionnaire forms together with the return address envelopes. The study adopts ERM implementation model introduced by Lai et al. (2011) (see Table 1).
The adopted ERM implementation model comprises three dimensions namely; Governance, Structure and Process. These three dimensions are classified into seven areas. A further fourteen elements used to operationalize these seven areas and these items, are measured on a five-point Likert scale.
While secondary data for EVA factors (dependent variable), which are proxies for firms performance, are extracted through Thomson Reuters DataStream which contains all the financial information of listed companies globally, including Malaysian companies. The firm's performance is measured by employing EVA computation as:
EVA = NOPAT - (WACC x IC)
EVA = economic value added, NOPAT = net operating profit after tax, WACC = weighted average cost of capital, and IC = invested capital.
For data collection, this study used stratified sampling method. The benchmark used to stratify the sample in this study is the market capitalization. There are thirteen market sectors as per Bursa Malaysia classification. This study carefully chosen top 120 companies from thirteen sectors based on market capitalization. Pearson Correlation Analysis and regression analysis are employed to examine and validate the relationships between the variables.
Table 1: ERM Implementation Framework's Dimensions, Areas and Elements
Population of the Study
ERM is practiced by large corporations such as public listed companies (PLCs), multinational companies (Ghazali and Manab 2013). According to Lai et al., (2011), compared to non-listed companies PLCs are more aware to operationalize risk management program in their operations. As such, the PLCs become the target population for this research in Malaysia.
Statistical Model Specification
ERM implementation model have advocated a positive impact on firm's performance. To investigate the impact of the ERM practices on firm's performance, the study presents the following regression models written as:
[Y.sub.1] = [[alpha].sub.1] + [[beta].sub.1][X.sub.1] + [e.sub.1] Model 1
[Y.sub.2] = [[alpha].sub.2] + [[beta].sub.2][X.sub.2] + [e.sub.2] Model 2
[Y.sub.3] = [[alpha].sub.3] + [[beta].sub.3][X.sub.3] + [e.sub.3] Model 3
[Y.sub.4] = [[alpha].sub.4] + [[beta].sub.4][X.sub.4] + [e.sub.4] Model 4
[Y.sub.1] = EVA, [Y.sub.2] = NOPAT, [Y.sub.4] = WACC, and [Y.sub.4] = ROIC (dependent variables)
[X.sub.4] = ERM implementation (independent variable)
[[alpha].sub.1], [[alpha].sub.2], [[alpha].sub.3], [[alpha].sub.4] = intercept of line of the corresponding model
[[beta].sub.1], [[beta].sub.2], [[beta].sub.3], [[beta].sub.4] = the regression co-efficient of effect on factor,
[e.sub.i] = error terms
In order to comprehend the dynamic of variance that occurred in dependent variables which is influenced by the independent variable, linear regressions analysis is performed. Four regression models were developed to evaluate the impact of ERM implementation (independent variable) on EVA, NOPAT, WACC and ROIC (dependent variables). Table 2 and 3 present the results of the regression analysis which indicate that all the models are significant at the 0.01 significance level. The R-square value represents the variance in dependent variable predicted by independent variable. For instance, the calculated value of R-square for EVA is 0.27, indicating that 27 percent of the corresponding variation in EVA of the firms is explained by ERM implementation. The R-square value for WACC is 0.226 depicting 22.6 percent variation in WACC reduction can be explained by ERM implementation. Similarly, R-square value for ROIC is 0.191 illustrating 19.1 percent variation in ROIC can be explained by ERM implementation. Also, the R-square value for NOPAT is 0.282 interpreting 28.2 percent variation in NOPAT can be explained by ERM implementation. The analysis of regression model coefficients shown in Table 3 indicates that for Model 1 a positive beta co-efficient = 0.63 was found with p-value = 0.000 (< 0.05) and a constant = 1.479. Also for Model 2 the positive beta co-efficient = 0.65 was found with p-value = 0.000 (< 0.05) and a constant = 1.42. For Model 3 the positive beta co-efficient = 0.827 was found with p-value = 0.000 (< 0.05) and a constant = 0.734. Similarly, for Model 4 also the positive beta co-efficient = 0.615 was found with p-value = 0.001 (< 0.05) and a constant = 0.957. Based on the results from Table 3 the four models are calculated as:
EVA = 1.479+ 0.63 ERM + [e.sub.1] Model 1
NOPAT = 1.42 + 0.65 ERM + [e.sub.2] Model 2
WACC = 0.73 + 0.82 ERM + [e.sub.3] Model 3
ROIC = 0.95 + 0.61 ERM + [e.sub.4] Model 4
These finding suggests that an increase in the ERM penetration level in organization necessarily guarantee enhancement of EVA, NOPAT, ROIC and reduction of WACC. The results are consistent with Shad and Lai, F.W. (2015b); Shad and Lai, F.W. (2015c); Lai F.W., (2015) because lower cost of capital and shareholders value could be generated by the ERM implementation, which inevitably contributes to firm performance. Nonetheless, the value of R-square indicated in Table 2 is quite low (less than 30%) which suggests that firm value is not influenced to any great extent by the explanatory variable in the model (ERM implementation).
A. Predictors: ERM Implementation
B. Dependent Variable: EVA, WACC, ROIC and NOPAT
A. Dependent Variable: EVA, WACC, ROIC and NOPAT
The objective of this paper was to investigate the impact of ERM implementation on the firms value measured through economic value added analysis among the Malaysian PLCs. Data was collected for the period of (2010-2014). The study employs regression analysis to test the impact of ERM implementation on EVA and its components. The results of the study suggests that ERM implementation is an important factor to enhance the firms value through increasing NOPAT, reducing WACC, and increasing ROIC. The relationships are empirically proven to be significant. The results also suggests that ERM possesses the largest explanatory power to reduce WACC and increase NOPAT, as compared to the third factor of EVA namely ROIC. These results conclusively support the arguments made by ERM proponents. Such as, Ghazali and Manab (2013); Hoyt and Liebenberg, (2011); Lai, F.W. et al.,(2011). This study also lends credence for the usefulness of EVA analysis as a firm's performance appraisal of ERM implementation. For practitioner's and policy makers, this study provides an important input to better understand the significance of ERM implementation in managing risks and evaluating firms performance through EVA.
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Fong-Woon Lai (*)
Management and Humanities Department, Universiti Teknologi PETRONAS, Malaysia
Muhammad Kashif Shad
Management and Humanities Department, Universiti Teknologi PETRONAS, Malaysia
(*) Corresponding author
Table 1: ERM Implementation Model Dimensions Areas Elements Structure ERM Definition Provide common understanding of the objectives of each ERM initiative Provides common terminology and set of standards of risk management Performance Identifies key risk Measurement indicators (KRIs) Integrates risk with key performance indicators (KPIs) Governance Information and Provides enterprise-wide Roles information about risk Enables everyone to understand his/her accountability Compliance Reduces risk of non-compliance Enables tracking costs of compliance Process Integration of ERM strategy is aligned Business Strategy with corporate strategy and Objectives Aligns ERM initiatives to business objectives Integrated across all functions and business units Integrates risk with corporate strategic planning Risk Identification Provides the rigor to identify and Response and select risk responses (i.e. risk- avoidance, reduction, sharing and acceptance) Risk Quantification Quantifies risk to the greatest extent possible Table 2: Regression Results Model Dependen R R-Square Adjusted R- Std. Error of t Variable Square the Estimate 1 EVA 0.520 0.270 0.264 0.584 2 WACC 0.476 0.226 0.220 0.861 3 ROIC 0.301 0.191 0.183 0.792 4 NOPAT 0.531 0.282 0.276 0.58 Table 3: Model's Coefficient Model Dependent Unstandardized Variable Coefficients B Std. Error 1 EVA (Constant) 1.479 0.365 ERM 0.632 0.095 2 NOPAT (Constant) 1.42 0.36 ERM 0.65 0.09 3 WACC (Constant) 0.734 0.537 ERM 0.827 0.141 4 ROIC (Constant) 0.957 0.684 ERM 0.615 0.179 Model Standardized t Sig. Coefficients Beta 1 4.059 0.000 0.520 6.614 0.000 2 3.92 0.000 0.531 6.81 0.000 3 1.367 0.174 0.476 5.877 0.000 4 1.399 0.165 0.301 3.430 0.001
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|Author:||Lai, Fong-Woon; Shad, Muhammad Kashif|
|Publication:||Global Business and Management Research: An International Journal|
|Date:||Jan 1, 2017|
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