THE EFFECT OF ENTERPRISE RISK MANAGEMENT (ERM) FRAMEWORK IMPLEMENTED VIA HEDGING STRATEGIES ON VALUE AND PERFORMANCE OF GAS PRODUCERS, IMPORTERS, AND RESELLERS
Posted: August 27th, 2021
THE EFFECT OF ENTERPRISE RISK MANAGEMENT (ERM) FRAMEWORK IMPLEMENTED VIA HEDGING STRATEGIES ON VALUE AND PERFORMANCE OF GAS PRODUCERS, IMPORTERS, AND RESELLERS
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Abstract
Enterprise Risk Management (ERM) incorporates strategies and activities that help a company identify, measure, reduce, monitor, control, and exploit the presence of compliance and corporate risks to increase the organization’s value. The fundamental objective of ERM is to create and implement strategies to establish and safeguard the shareholder’s value. Therefore, for ERM to bring value to a business, it should be integrated with business operations such as strategic planning, strategic management, and risk management to ensure consistent evaluation of risks from business and initiatives. Consequently, this research employed concepts and empirical studies to explore ERM levels in gas producers by developing hedging strategies to determine value and performance in these companies. Furthermore, the review analyzed the theoretical frameworks of different risk management philosophies that support the relevance of incorporating ERM techniques in business operations. Additionally, literature review and empirical research were conducted on ten gas companies listed in London Stock Exchange that were evaluated in the study. Secondary data was also used in precisely implementing the analysis. The research findings revealed low ERM levels in the gas companies as managers focused more on operative and financial risk management. The regression analysis in the review indicated that the studied risk management rationales could not be strongly predicted to explain material decisions on corporate risk management. Therefore, the risk management system is dependent on company value and growth strategies.
Keywords: Enterprise risk management (ERM), Gas companies, Hedging strategies, Firm performance
Table of Contents
1.2. Importance of Risk Management 6
Chapter 2: Literature Review.. 9
2.2. Theoretical Framework of ERM… 9
2.2.3. New Institutional Economics Theory. 10
2.3. Review of Previous Studies. 10
2.3.1. ERM Impact on Organizational Performance. 10
2.3.2. ERM Effects on a Firms Value. 11
2.3.3. ERM and Financial Performance. 12
2.3.4. ERM and Non-Financial Measurers. 14
Chapter 3: Data and Methodology. 15
3.5. Dependent and Independent Variables. 15
Chapter 4: Findings and Discussion. 17
4.2. Demographic Information Summaries. 17
4.2.1 ERM Implementation Tendencies. 17
4.2.2 Descriptive Statistics. 18
4.2.3 The Durbin-Wu-Hausman (DWH) Test 19
4.3. Regression Analysis and Discussions of the Analysis. 20
4.4. Summary of the Research Findings. 24
5.2. Contribution and Implication. 25
5.3. Limitations and Future Research Recommendations. 25
Measuring the Effect of Enterprise Risk Management Framework on Value and Performance of Gas Producers, Importers, and Resellers
1.1.Introduction
Businesses face many challenges that threaten their survival and sustainability, brought about by certain conditions from the internal and external business operating environment. Companies deal with these threats by ensuring that they invest in the right management tools to ensure proper risk management. Good implementation and application of risk management provide business certainty, which improves the firm’s competitiveness (Huda et al. 2019). Risk is an intrinsic part of any business. Companies should be willing to accommodate taking a certain risk amount, which will ensure that they provide the most value to their shareholders (Bower et al., 2017). To achieve that, they have to strike an optimal balance between return objectives and business growth into the unforeseeable future. Hence, companies should ensure the effective use of resources as they take risks with anticipation of profit maximization.
Risk management is an essential aspect of corporate strategy, where its implementation must be done to mitigate all risks faced by a business. Managers should ensure they improve risk management by adopting integrated risk management practices such as enterprise risk management (ERM). Therefore, they should ensure effective management of resources by the adoption of ERM practices that focuses on aligning the business strategy to risk appetite (Viscelli et al., 2016). Additionally, the risk alignment ensures that businesses enhance the risk response mechanisms and reduce operational losses, ensuring that the firm identifies and manages multiple risks while improving capital deployment. Further, ERM proposes that companies should address their risks coherently and comprehensively, rather than managing them independently (Lamine et al., 2020). Therefore, ERM creates mechanisms or systems in the company that minimize the occurrences of risks by ensuring threats are anticipated and managed appropriately, which leads to the business gaining a competitive advantage over its rivals. Hence, the study aimed to examine the effect of enterprise risk management (ERM) framework implemented via hedging strategies on the value and performance of gas producers, importers, and resellers in the European market. This is implemented through six sections divided under Chapter One, which introduces the area of focus, Chapter Two, which conducts a review of literature, Chapter Three, which presents a methodology adopted for the study and Chapter Four and Five for findings and discussion and conclusion, respectively.
1.2.Importance of Risk Management
Organizations have continuously changed risk management policies previously based on focusing on distinct business units, to a holistic view referred to as ERM. ERM is an essential tool that assists businesses to deal with uncertainties that face enterprises through the identification and determination of the significant risks that may hinder a company from achieving its vision and missions (Altanashat et al., 2019). Besides, organizations are easily distracted by minor risks, which could be a facilitator of failure in the upsurge of more significant or complex risks. Therefore, the ERM system ensures that managers can identify all the risks that may negatively affect business operations by using multiple risk management systems, which considers the interrelations or connections between various risk exposures. Equally, ERM concentrates on all forms of risks facing a business, not only the traditional risk management methods such as liability reduction, insurance buying, and financial risk management. (Guliyeva, 2020) holds that strategy such as diversifying investments and the purchase of derivatives are crucial in ERM as business risks are interrelated. Therefore, companies can create efficient plans to deal with the overall business risks.
Moreover, companies nowadays face huge risks due to complexity in business operations, which necessitates the need for risk management strategies. Besides, the adoption of ERM practices is more popular since companies are pressured from the internal and external environment to manage risks holistically. Additionally, the businesses’ profits will reduce if companies do not control the risks holistically and separately. Therefore, a firm’s performance improvement and stability depend mostly on the productive roles of risk management and corporate governance (Braumann, 2018). ERM is a crucial aspect of corporate governance, mainly in the agent-principal relationship, which is essential in ensuring that companies can achieve their vision (ElGammal et al., 2018). Hence, ERM is designed to increase the board of directors’ ability to oversee, manage, and mitigate business risks that threaten its survival.
Consequently, Callahan and Jared (2017) argue that risk management consultants and ERM researchers identify four risk areas: operational, financial, hazard, and strategic. Likewise, ERM strategies ensure that firms optimize risk exposure to maximize value, which is different from the traditional approaches adopted by businesses that attempt to minimize the cost of risk through retention, reduction, transfer, and avoiding risk exposure (Callahan and Jared, 2017). Also, ERM aims to protect and enhance a firm’s competitive advantage, improve business performance, and optimize risk management. Therefore, ERM adoption in any firm is not easy. It requires communication and coordination in the company, which will not be achievable if there are insufficient investments in financial resources and human resources.
1.3.Research Objectives
The primary objectives will for the study were;
- To establish whether the implementation of ERM frameworksby hedging strategies have a positive impact on the companies’ values involved in gas in Europe.
- To determine whether the ERM frameworksaffect the companies’ accounting performances in Natural Gas trading in Europe.
1.4.Research Questions
The research questions in the review are;
- Does the implementation of ERM frameworks by hedging strategies positively impact the companies’ values involved in gas in Europe?
- Do ERM frameworks have an effect on the companies’ accounting performances in Natural Gas trading in Europe?
1.5.Plan of the Study
The report contains chapters one to five that are arranged in chronological order. The remainder of this review follows the following outline;
- Literature Review
The section gives an analysis of previous studies related to the subject under discussion. The literature review summary includes previous studies’ aims, methodology, results, and further recommendations.
- Data and Methodology
The section highlights data sources, techniques in data analysis, and results.
- Data Analysis
The section is about a discussion of the results and findings in the data and methodology section.
- Conclusions
The conclusion offers the study findings and conclusions obtained from the study results. Additionally, this section will present the study limitations and provide acclamations for future exploration.
1.6.Summary
This is
the introduction of the research that has provided an in-depth analysis of the
background of the study, objectives, research questions, and research plan.
Chapter 2: Literature Review
2.1.Introduction
The section covers previous literature that explains the impact of ERM on firms trading in natural gas in Europe. Companies invest in risk management measurers due to capital market imperfections that necessitate the use of derivatives in managing risks. Thus, companies manage risks according to the exposure level, with high exposure leading firms to adopt more sophisticated actions than low exposure levels.
2.2.Theoretical Framework of ERM
Researchers have added to the body of knowledge concerning ERM through the use of various theories. The theories include agency theory, stakeholder theory, and new institutional economics approach.
2.2.1. Stakeholder Theory
This model states that organizations interact with other business playersboth internally and externally, and the interaction affects how businesses conduct their operations. The stakeholders identified by the theory are mainly the employees and customers, suppliers and shareholders of the company as well as government and the overall community where the firm operates. According to Kaur and Lodhia (2018), the approach states that businesses should focus solely on stakeholder’s interests, as this will ensure business sustainability.Therefore, businesses will experience financial distress if the management fails to consider stakeholder’s interests, which will lead to an increase in default costs, reducing business profitability. However, the distress can be mitigated if there is the adoption of hedging policies to reduce risks facing business operations.
2.2.2. Agency Theory
The approach highlights issues between principals and agents in the management of a business, with the principals being the shareholders while the agents are the management. Additionally, the approach analyses the separation concept that the shareholders should exercise to ensure that the business is successful in its operations, as interfering in the company’s control may expose it to more risks (Shi et al. 2017). Furthermore, the manager’s attitude towards risk or hedging actions is affected by agency matters, as management will expose a company depending on shareholders’ interests and goals. Besides, it states that managers get their salary from their employment, which is why they should protect their earnings by being risk-averse. Therefore, a conflict of interest arises when managers are reluctant to ensure that shareholder’s interest comes first, as they usually allocate resources to specific projects to hedge diversifiable risk.
2.2.3. New Institutional Economics Theory
2.3.Review of Previous Studies
Previous studies have been done by various scholars regarding the impact of ERM adoption in energy trading companies. Most studies have indicated that its adoption led to success in managing its internal and external risks.
2.3.1. ERM Impact on Organizational Performance
Researchers have studied the influence of ERM on the performance of organizations. A study was done by Teoh and Muthuveloo (2017) on the impact of ERM frameworks on the publicly listed companies in Malaysians performance (222). A sample of one hundred and thirty-seven respondents within the Bursa Malaysia market was used in the study. Additionally, the researchers used the COSO frameworks of 2004 to ensure that they get all the facts relating to ERM adoption. The non-financials indicators were used to measure the firms’ performance. Thus, the findings indicated that ERM adoption in the country had positive outcomes for the companies that implemented its use. Still, the regulatory authorities needed to ensure that they continued monitoring the firms’ ERM practices. Likewise, another study was done by Ugwuanyi and Imo (2014) to investigate the effects of ERM adoption on Nigeria’s brewing industry, with the survey adopting the cross-sectional design. The research used 375 respondents to fill the questionnaires. The results equally revealed that the adoption of the ERM had positive effects on Nigeria’s brewing industry, as illustrated by improved governance systems of the respective companies.
Besides, Maingot et al. (2018) investigated the relationship between a firm’s performance and the ERM. The study relied on a sample size of 156 companies that are listed on the Toronto Stock exchange. The factors used in measuring firms’ performance included tax margins, revenues, and earnings before interest and tax. Equally, Tobin’s Q was used to measure performance changes from 2006 to 2009 based on the analysis of the yearly financial reports content of respective companies. The researchers noted that the financial crisis’s occurrence had a direct consequence on the financial market’s performance, which led to a differed result on the operational and accounting performance. More so, firms having variations in returns failed to report average levels about their economic risk exposure or outcomes in the market outcomes, yielding statistically considerable variations in the results. The scholars revealed that ERM structure practices’ adoption did not have any meaningful impact on an organization’s performance.
2.3.2. ERM Effects on a Firms Value
Tahir and Razali (2011) analyzed Malaysia’s listed firms’ value and the effects of ERM implementation. During the study, a total sample of five hundred and twenty-eight companies from the OSIRIS database was used for 2007. Tobin’s Q formula was used to help calculate the company value. Likewise, the control variables adopted in the study included the majority share ownership and the internal diversification as well as and profitability and the company size. The data analysis approach included panel and correlation. The researcher noted a positive link between the adoption of ERM and a firm’s value, but that was not significant. The scholars noted that ERM adoption does not necessarily mean that they will have higher Tobin’s Q value than those firms that do not support an ERM framework.
Similarly, a study by Li et al. (2014) examined the impact that ERM adoption had on Chinese insurance companies’ value. The researchers used Pearson correlation and regression models to analyze the data collected from 135 insurance firms listed in Tokyo’s stock exchange. The findings showed the existence of a meaningful and positive correlation between firms’ value and ERM adoption through the use of the correlative matrix. However, the regression analysis revealed a negative relationship between the company’s value and the implementation of ERM. Therefore, the researchers recommended that organizations develop an all-inclusive risk management structure that will ensure that they deal with all the risks that threaten the organization’s survival and sustainability.
Furthermore, research by Gates et al. (2012) in the UK examined the ERM practical value implementation. The study involved sampling271 risk and audit management officials who were also members of the UK’s Conference board. The sampling method involved using a questionnaire, with the study aiming at assessing the measures such as risk assessment, response, information, and monitoring activities in their organizations. The study results indicated there was a positive relationship between the improved perceived performance and enchanted management. Besides, the study also noted that managers were willing to adopt ERM, which will be crucial in improving the performance of the companies. Thus, the researchers noted that ERM adoption resulted in enhancing risk management and profitability besides reducing earning volatility.
Lastly, research by Onafalujo and Eke (2012) established that the implementation of ERM contributed to bolstering a company’s competitive advantage and the creation of value in the market. The study focused on Nigeria’s manufacturing sector, taking a sample size of 350 respondents supplied with a questionnaire for the collection of primary data for analysis. The data were analyzed using regression and Pearson correlation models that yielded a statistically significant relationship between competitiveness and ERM adoption among the companies.
2.3.3. ERM and Financial Performance
Sae-Lim (2017) examined the correlation on the performance measurement systems, enterprise risk management systems, and financial performances of all listed corporations in the Thailand Stock Exchange (81). The enterprise risk management systems’ measurement included the processes, infrastructures, cultures, risks, and clear responsibilities. A 5-point scale questionnaire was applied in the assessment. The performance measurement system’s sizes were done using performance indicators and the drivers and aims of the surveys. The review’s financial performances were also done using financial ratios such as return on equity, earnings per share, and return on assets source from the online database of the Thailand Stock Exchange. The study findings indicated that the relationship between firm financial returns and enterprise risks management systems and performance measurement systems were not statistically significant.
Furthermore, Kolapo et al. (2018) investigated the effects of credit risk on Nigerian commercial banks within a seven-year-old from 2005 to 2011 using a panel model method. The value at risk (VAR) approach was used to analyze the credit risk exposure with the return on asset (ROA), and the return on capital employed (ROCE) used as the proxy for performance. According to the researchers, the findings showed that a negative relationship existed between the banks’ performance and credit risk, attributed to the banks’ failure to adhere to risk management practices. Thus, the researchers noted that if the banks had strong ERM measures in their systems, the non-adherence would not exist.
Mojtaba & Davoud (2017) investigated ERM’s influences on the financial performance of listed firms in Iran.During the study, about sixty-six companies’ data between 2001 and 2015 listed on the Tehran Stock Exchange. The basis of selection was the risk assessment unit (RAU). The study employed the logit model and the multivariate approach in analyzing the firms’ variations regarding the use of RAU and lack of it. The risk assessment unit was the proxy for the ERM effects, with a return on assets being applied to measure the firm’s performance. The findings indicated a negative relationship between the firms’ performance and ERM for the firms listed in the Iranian stock exchange. The researchers noted that the adoption of ERM did not add any significant value to firms’ performance.
Grace et al. (2015) investigated the effects of ERM on revenue and cost-effectiveness in a business. The study involved measuring efficiency between various firms. Regression analysis results indicated that the ERM application is statistically and economically significant. At the same time, the study noted increases in the business revenue while also reducing the costs involved in running an organization. Nahar et al. (2016) examined the link between bank performance and risk governance in developing nations where information disclosure is nearly voluntary. Data collection for the research was done using a sample of 210 banks, covering the period from 2006 to 2012. Risk governance practices were measured using risk disclosure, the number of board risk committees, and the existence of a risk management unit.As such, the financial performance for organizations was determined using the return on assets and return on equity with market-based performances measured using buy and hold returns and Tobin’s Q. The data analysis for the research was done using regression analysis. In their conclusion, they established that bank performance and risk governance has a statistically significant relationship.
2.3.4. ERM and Non-Financial Measurers
Owojori et al. (2011) examined challenges facing post-consolidation risk management practices in Nigerian banks.The research provided a clear assessment of Nigerian banks’ risk management practices, which noted that the banks lacked appropriate governance measures that are crucial in reducing risks. Still, the researchers pointed out that the banks had low awareness on matters concerning global risk issues, which was made worse by the need for profit maximization at the expense of failure to adopt proper risk management frameworks. Therefore, the lack of appropriate functioning risk management practices exposed the banks to higher occurrences of fraudulent activities, as they could not easily detect the challenges in their systems.
Maingot
et al. (2012) conducted a study in Canada’s listed companies in the Toronto Stock
Exchange composite index for 2007, and 2008 examined the relationship of the
non-financial firms between ERM’s information content and their performances.
The findings indicated that they were minor improvements in risk management,
consequence, and exposures. The researchers observed that ERM information would
neither predict have any considerable impact on the way businesses perform. In
their work, McShane et al. (2011) compared the effects of adopting ERM with the
use of the conventional risk management strategies on firms using 82 samples
for publicly traded companies from S&P listed companies. The scholars noted
that there was a positive relationship between firm value and the level of TRM
ratings. The researchers concluded that firms with higher ERM ratings are
constrained in performing well due to cultural constraints or environmental
changes.
Chapter 3: Data and Methodology
3.1. Introduction
This section offers data and methodology applied to ascertain the topic under review.
3.2. Data Sources
Secondary data was collected for the last ten years of the historicalspot prices from Bloomberg data services. The historical sets were modeled to give daily returns of the companies under consideration.
3.3. Data Collection
There are approximately five hundred European companies involved in gas trading in producing, importing, and retailing gas (Natural Gas Market Indicators, 2019). However, this study focused on assessing the hedging strategies of the largest listed 10 companies due to the diverse characteristics. Data was collected for ten years period from 2009 to 2018 to obtain a solidly balanced panel of 100 observations. Equally, the Return on Assets (ROA) and Return on Equity (ROE) financial ratios were employed in the study of the companies’ annual financial reports. Additionally, to get hedging values, the study relied on annual reports that offer information on hedging costs and the relationship between derivatives and hedging costs. The control variables in the survey were collected from financial reports or websites like Morning Stars, such as earnings per share, liquidity, and leverage ratio.
3.4. Data Analysis
OLS Regression model was employed in data analysis in this study to determine the level of hedging according to the companies’ characteristics, hedging level, and accounting results in equations 1 and 2, respectively. The outcomes of the two equations from the models were combined to interpret the common findings. The endogeneity problem was tested using three models that were run simultaneously on two regressions models to give precise results on the companies’ hedging and performance.
3.5. Dependent and Independent Variables
This study considered dependent, independent, and control variables to achieve its objectives to determine the impact of the ERM approach on an organization’s performance. To establish the impact of the ERM approach on an organization’s value, Tobin’s Q was taken asa dependent variable. Furthermore, the study used the companies’ profitability ratios under review that were adopted, which include ROA and ROE. The control variables include earnings per share, gas spot prices, and liquidity and leverage ratios. Additionally, the study adopted the Durbin-Wu-Hausman tests in testing for the endogeneity problem in the model.
3.6. Model Formulation
The models formulated were as follows:
Where: i= gas company
T= time
= error term from endogeneity problem
= average return
The average returns was calculated using the hedging ratios of the sampled companies classified as low level or high-level hedging as shown below;
Chapter 4: Findings and Discussion
4.1. Introduction
This chapter offers findings and discussions of the review. The section adopted statistics techniques in data analysis to achieve the study objectives. Regression analysis is applied in the review to determine the correlation between ERM and gas companies’ performance and value. Initially, the review used descriptive statistics to familiarize with the data; then, regression analysis is applied to understand the models and correlation analysis for determining the study hypothesis. Finally, the hypothesis was tested using the best-fitted panel method. Furthermore, this chapter will be organized as follows; 4.1 represents the introduction, 4.2 demographic information summaries, 4.3 impacts of ERM on company value and performance using regression model, and discussions of the results, and 4.4 summaries of the research findings.
4.2. Demographic Information Summaries
4.2.1 ERM Implementation Tendencies
ERM has a diverse 4h
Figure 1: ERM implementation of the sampled companies
Chart 1 above indicates ERM implementation among the ten sampled energy companies. It can be noted that approximately 80% of the energy corporations had adopted ERM strategies in their operations in the year 2009. This was after the 2007-08 global financial crises that led to a drastic drop in the value of the assets, triggered by unregulated derivatives use by banks. This caused the subprime mortgage crisis that caused panic in the financial institutions and spiral effects in the financial markets. Besides, these financial effects led the energy companies to implement ERM in their systems to cushion themselves using hedging strategies. The trend is observed in the next three years were the implementation rates are more than 60%. However, ERM implementation declined between 2012 and 2016, as shown in the chart, but started to rise in 2017 and 2018.
4.2.2 Descriptive Statistics
Descriptive statistics were used to analyze data properties such as mean, mode, median, standard deviation, and skewness to test the study hypothesis and achieve objectives. Furthermore, ERM implementation and Tobin’s Q to represent the company’s value are evaluated using descriptive analysis.
Table 1 Descriptive analysis
| Variables | Sample size | Mean | Median | Std. deviation | Maximum | Minimum | Range |
| ROA | 10 | 7.848 | 7.421 | 10.843 | 37.803 | -21.67 | 59.473 |
| ROE | 10 | 11.9534 | 15.8723 | 55.0074 | 92.3658 | -450.985 | 543.351 |
| Low-level hedging | 10 | 0.2574 | 0.2478 | 0.1875 | 0.87592 | 0.000542 | 0.875 |
| High-level hedging | 10 | 0.7512 | 0.7481 | 0.6865 | 0.27596 | 0.001472 | 0.274 |
| Size | 10 | 7.80251 | 7.87452 | 0.336 | 8.865 | 7.0254 | 1.8396 |
Table 1 shows the descriptive statistics of ten gas companies based on ten years of data from 2009 to 2018 that were adopted in regression analysis. On average, the companies’ low hedging levels is 25.74%, with a median of 24.78% and a range of 0.8754. Additionally, the high hedging levels have a mean of 75.12%, the median of 74.81%, and a range of 0.274488. In this case, it implies that low and high hedging levels of the gas companies have substantial impacts on financial performance. Tobin Q’s presented by the companies’ ROA and ROE has a mean of 7.848 and 11.9534, median of 7.421 and 15.87, and a range of 59.473 and 543.3508, respectively. Additionally, the mean values of the companies’ size are 7.80251, a median of 7.87452, and a range of 1.8396. Furthermore, the standard deviation of the Tobin Q’s is high, implying that the sampled size contains companies with diverse characteristics.
4.2.3 The Durbin-Wu-Hausman (DWH) Test
The study adopted the Durbin-Wu-Hausman tests to examine the endogeneity of the data, B, which was applied in Tobin’s Q on the performance measures, that is, ROA and ROE two models. The following outcomes are produced from the test;
Table 2: The Durbin-Wu-Hausman Test on ROE
| Variables | Coefficient | T-statistics | P-value |
| ROE | -5.23 | -0.41 | 0.81 |
| ROA | 58.142 | 3.985 | 0 |
| Low-level hedging | 1.7845 | 0.9235 | 0.3652 |
| High-level hedging | -24.4132 | -6.6675 | 0 |
| Size | -1.4578 | -2.14 | 0.041 |
As indicated in Table 2, DWH tests demonstrate that coefficients of ROE are below zero, with an at-statistics value of -0.41 and a p-value of 0.8 at a significance level of 0.01. The endogeneity of the companies’ ROE is evident, and by using OLS in the equation will generate inconsistent and biased regression coefficients. Thus, the application of 2SLS in the firm performance model is justified.
Table 3: The Durbin-Wu-Hausman Test on ROA
| Variables | Coefficient | T-statistics | P-value |
| ROA | -41.895 | -0.4187 | 0.667 |
| ROE | 290.12 | 3.214 | 0.002 |
| Low-level hedging | -10.154 | -0.835 | 0.406 |
| High-level hedging | -0.8903 | -0.2001 | 0.8412 |
| Size | -226.07 | -2.231 | 0.0172 |
As portrayed in Table 3, the DWH tests on ROA indicate that the coefficients of ROA are significantly different below zero, with a t-statistics value of -0.4187 and a p-value of 0.667 at a significance level of 0.05. This implies that the endogeneity of the companies’ ROA is evident, and by using OLS in the equation will generate inconsistent and biased regression coefficients. Thus, the application of 2SLS in the firm performance model is justified.
Table 4: The Durbin-Wu-Hausman Test on Low-level hedging
| Variables | Coefficient | T-statistics | P-value |
| ROE | 0.2341 | 0.756 | 0.4897 |
| ROA | 0.0075 | 1.564 | 0.123 |
| High-level hedging | 0.147 | 1.085 | 0.25 |
| Size | 0.752 | 1.865 | 0.238 |
| Low-level hedging | -0.003 | -5.895 | 0.654 |
As portrayed in table 4, the DWH tests on Low-level hedging indicate that the coefficients of Low-level hedging are not significantly different below zero with a t-statistics value of -5.895 and a p-value of 0.654 at a significance level of 0.05. It implies that the endogeneity of the companies’ low hedging level is not evident in the data. Therefore, using OLS in the model is sufficient and will generate consistent and unbiased regression coefficients. Thus, the application of 2SLS in the hedging level model is not justifiable.
Table 5: The Durbin-Wu-Hausman Test on high-level hedging
| Variables | Coefficient | T-Statistics | P-value |
| ROE | -0.085 | -0.214 | 0.882 |
| ROA | 0.003 | 1.567 | 0.013 |
| Size | 0.03 | 0.653 | 0.515 |
| Low-level hedging | 0.138 | 1.542 | 0.294 |
| High-level hedging | -0.001 | -1.121 | 0.2698 |
As indicated in Table 5, the DWH tests on high-level hedging indicate that high-level hedging’s coefficients are not significantly different below zero with a t-statistics value of -1.121 and a p-value of 0.2698 at a significance level of 0.05. It implies that the endogeneity of the companies’ high hedging level is not evident in the data. Therefore, using OLS in the model is sufficient and will generate consistent and unbiased regression coefficients. Thus, the application of 2SLS in the hedging level model is not justified in the model.
4.3. Regression Analysis and Discussions of the Analysis
Regression analysis involves using statistical tools and techniques to determine the correlation between independent and dependent variables in a model or an equation. In this research, we employed both 2SLS and OLS regression to establish the correlation of ERM frameworks implemented through hedging strategies and energy companies’ performance. Therefore, the regression results obtained on the effects of ROE in the firm’s performance models are as follows;
Table 6: Regression analysis on ROE
| Dependent Variable: ROE | ||||||
| OLS | 2SLS | |||||
| Variables | Coefficient | T-statistics | P-value | Coefficient | T-statistics | P-value |
| ROA | 9.856 | 0.6589 | 0.551 | -5.246 | -0.214 | 0.8321 |
| ROE | 12.1475 | 3.065 | 0.003 | 57.81 | 2.663 | 0.0201 |
| Size | 1.965 | 0.966 | 0.336 | 1.7841 | 0.6061 | 0.546 |
| Low-level hedging | -29.956 | -8.7854 | 0 | -24.412 | -4.448 | 0 |
| High-level hedging | -2.829 | -4.475 | 0 | -1.547 | -1.417 | 0.165 |
| F-statistics | 25.86 | 13.006 | ||||
| Adjusted R2 | 0.446 | 0.279 |
Table 6 above indicates the results of the OLS and 2SLS model; the Tobin’s Q has a positive and substantial impact on ERM, as shown by the ROE that has t- the value of 3.065 and a p-value of 0.003 at0.01 significance level. Equally, 2SLS regression is applied in the model, considering that ROE has an endogeneity problem, as shown by the DWH test. The 2SLS regression indicates that ROE has a positive and significant correlation on ERM with a t-statistics value of 2.663 and a p-value of 0.0201. The companies’ size has a positive but immaterial impact on ERM in both 2SLS and OLS regression models. Likewise, the firm’s size has t- the value of 0.966 and a p-value of 0.336 in OLS regression and t- the value of 0.6061 and p-value of 0.546 in the 2SLS regression model. Additionally, the low-level hedging has a significant but negative impact on ERM t- the value of -8.7854 and p-value of 0.000 in OLS regression and t- the value of -4.448 and p-value of 0.000 in the 2SLS regression model. The high-level hedging has a significant but negative impact on ERM t- the value of -4.475 and p-value of 0.000 in OLS regression and t- the value of -1.417 and p-value of 0.000 in the 2SLS regression models. The adjusted R2 in the firm’s performance model in ROE is 0.446 in OLS and 0.279 in 2SLS regression, implying that Tobin’s Q in a firm’s performance as indicated by ROE is 44.6% in OLS and 27.9% in 2SLS. Furthermore, the F-statistics under ROE is 25.86 and 13.006 in OLS and 2SLS, respectively, at a significance level of 0.01. Thus, the firm’s performance model is statistically significant.
Table 7: Regression analysis on ROA
| Dependent Variable: ROA | ||||||
| OLS | 2SLS | |||||
| Variables | Coefficient | T-statistics | P-value | Coefficient | T-statistics | P-value |
| ROA | 110.754 | 1.142 | 0.273 | 42.372 | 0.334 | 0.736 |
| ROE | 80.632 | 3.421 | 0.002 | 290.214 | 2.464 | 0.014 |
| Size | -9.03 | -0.75 | 0.5441 | -10.214 | -0.623 | 0.514 |
| Low-level hedging | -74.124 | -3.621 | 0 | -49.425 | -1.668 | 0.097 |
| High-level hedging | -6.881 | -1.771 | 0.0063 | -0.9124 | -0.151 | 0.787 |
| F-statistics | 7.251 | 4.342 | ||||
| Adjusted R2 | 0.177 | 0.872 |
Table 7 above shows the OLS model; the Tobin’s Q has a positive and material impact on ERM, as shown by the ROA as it has t- the value of 1.142 and a p-value of 0.273 a significance level of 0.01. Furthermore, 2SLS regression is applied in the model, considering that ROA has an endogeneity problem, as shown by the DWH test. The 2SLS regression indicates that ROA has a positive and material correlation on ERM with a t-statistics value of 0.334 and a p-value of 0.736. The companies’ size has a negative and immaterial impact on ERM in both 2SLS and OLS regression models. Furthermore, the firm’s size has t- the value of -0.75 and p-value of 0.5441 in OLS regression and t- the value of -0.623 and p-value of 0.514 in the 2SLS regression model.
Additionally, the low-level hedging has a significant but negative impact on ERM t- the value of -3.621 and p-value of 0.000 in OLS regression and t- the value of -1.668 and p-value of 0.097 in the 2SLS regression model. Also, the high-level hedging has a significant but negative impact on ERM t- the value of -1.771 and p-value of 0.0063 in OLS regression and t- the value of -0.151 and p-value of 0.787 in the 2SLS regression models. The adjusted R2 in the firm’s performance model in ROA is 0.177 in OLS and 0.872 in 2SLS regression, implying that Tobin’s Q in a firm’s performance as indicated by ROE is 17.7% in OLS and 87.2% in 2SLS. Furthermore, the F-statistics under ROA is 7.251 and 4.343 in OLS and 2SLS, respectively, at 0.01significance level. Hence, ROA in the firm’s performance model is statistically significant.
Table 8: Regression analysis on low-level hedging
| Dependent Variable: Low-level hedging | |||
| OLS | |||
| Variables | Coefficient | T-statistics | P-value |
| ROA | 0.22 | 0.594 | 0.634 |
| ROE | 0.005 | 2.643 | 0.008 |
| Size | 0.801 | 0.971 | 0.334 |
| Low-level hedging | -0.0085 | -0.21 | 0.842 |
| High-level hedging | 0.941 | 2.137 | 0.036 |
| F-statistics | 4.926 | ||
| Adjusted R2 | 0.1214 |
Table 8 above demonstrates the OLS model’s outcomes in the hedging model under low-level hedging on the impact of ERM. As shown in Table 8, the low-level hedging has a negative and insignificant impact on ERM with a t- the value of -0.21 and a p-value of 0.842 at a significance level of 0.01. However, the hedging model does not have an endogeneity problem as shown by the DWH test, and is not subjected to the 2SLS regression. Additionally, the firm’s size has a significant and positive impact on ERM with a t- the value of 0.97 and a p-value of 0.334. Furthermore, the high-level hedging has a substantial and positive effect on ERM with a t- the value of 2.137 and a p-value of 0.036. The organization’s performance, as indicated by ROA and ROE, has positive and material impacts on ERM with a t-value of 0.594 and 2.643 and a p-value of 0.634 and 0.008, respectively. In the OLS model, the F-statistics value is 4.926, and the adjusted equation of 0.1214.
Table 9: Regression analysis on high-level hedging
| Dependent Variable: High-level hedging | ||||
| OLS | ||||
| Variables | Coefficient | T-statistics | P-value | |
| ROA | 0.0945 | 0.284 | 0.7742 | |
| ROE | 0.001 | 2.872 | 0.005 | |
| Size | 0.014 | 0.3126 | 0.734 | |
| Low-level hedging | 0.015 | 0.225 | 0.8213 | |
| High-level hedging | 0.11 | 2.351 | 0.021 | |
| F-statistics | 5.245 | |||
| Adjusted R2 | 0.119 | |||
Table 9 above indicates the results of the OLS model in the hedging model as it does not have an endogeneity problem, as shown by the DWH test, and is not subjected to the 2SLS regression. As shown in the table, high-level hedging has a positive and substantial impact on ERM with a t- the value of 2.351 and a p-value of 0.021 at a significance level of 0.01. Additionally, the firm’s size has a material and positive impact on ERM with a t- the value of 0.3126 and a p-value of 0.734. Furthermore, the low-level hedging has a substantial and positive impact on ERM with a t- the value of 0.225 and a p-value of 0.8213. Also, the company’s performance, as indicated by ROA and ROE, has a positive and significant impact on ERM with a t-value of 0.284 and 2.872 and a p-value of 0.7742 and 0.005, respectively. In the OLS model, the F-statistics value is 5.245, and adjusted equation of 0.119.
4.4. Summary of the Research Findings
The research findings
show only a few numbers of gas companies incorporate ERM in their operations as
most managers are more focused on operative and financial risk management.
Furthermore, the regression analysis indicates that the company size is not a
strong indicator of taking enterprise risk management by firms as size has a
positive but insignificant impact on ERM. Additionally, Tobin’s Q, presented by
ROA and ROE, is highly correlated with ERM as firms seek to integrate risk
management in their operations. Besides, the hedging level has a strong
positive and substantial relationship with ERM, and companies with high Tobin’s
Q integrates high-level hedging in their business operations. Therefore, we can
conclude that the risk management system is dependent on company size, value,
and growth options from the study findings.
Chapter 5: Conclusion
5.1. Introduction
This chapter provides research conclusions on the effect of ERM frameworks implemented through hedging strategies on gas companies’ value and performance. The section will be organized in chronological order as follows; 5.1 the introduction, 5.2 contributions and implication of the study, 5.3 limitations of the research and future research recommendations, and 5.4 overall conclusions.
5.2. Contribution and Implication
The research is anticipated to fill knowledge gaps in finance and risk management. The study focused on the impact of ERM frameworks implemented through hedging strategies on gas companies’ value and performance that sampled ten companies for ten years between 2009 and 2018. The findings indicated a significant and positive correlation between the company’s ROA and ROE with ERM. This is consistent with a previous review by Nasr et al. (2019) that determined how ERM affects short and long-term organization’s performance. Additionally, the study outcomes observed that a company’s Tobin’s Q is significantly positively associated with ERM strategies. Therefore, this strongly supports research by Farrell and Gallagher (2019) that holds that influencing the ERM strategies of a company increased its performance relationship.
Furthermore, the study observed that the hedging level has a strong positive, and significant correlation with ERM, consistent with a survey by Iqbal (2017). The research by Iqbal (2017) revealed that when firms adopt hedging in their operations, they reduce costs related to operations, supply, and uncertainties leading to effective ERM systems. Additionally, the study indicated that the company size is not a strong indicator of taking enterprise risk management by firms as size has a positive but insignificant impact on ERM. This is consistent with Bohnert et al. ‘s review on the value and drivers of ERM, which established that companies’ size is not the primary determinant in the adoption of ERM in business operations. Hence, the risk management system is dependent on company value and growth strategies.
5.3. Limitations and Future Research Recommendations
The study used secondary data for ten energy companies for ten years between 2009 and 2018. Lack of primary data and face-to-face interviews limited the review. There would be a broad opinion on the companies’ managers and stakeholders’ perspectives on the importance and implementation of ERM in their organizations. Equally, the study recommended two types of policy development and conducting future research. Under areas of future research, there is a need to study factors that hinder the implementation of ERM strategies in their firms as it has been established that ERM has a positive and material correlation with the firm’s performance. Additionally, a further review should be made to determine the impact of economic performance as a determinant of financial performance incorporating more firms and a more extended period to generate more accurate results in the findings. Finally, research on company-specific variables such as internal risk factors and exogenous shocks such as labor market turbulences should be analyzed in regards to the ERM of companies listed on the London Stock Exchange. In policy development, the study recommends that the UK government create a unit with powers like risk auditors and Audit directors to ensure that all gas producers, importers, and resellers implement and comply with ERM in their business operations.
5.4. Overall Conclusions
Companies
should focus more on hedging levels to gain competitive advantage and improve
their financial performance, as presented by Tobin’s Q. The research showed few
energy companies incorporated ERM in their operations, and most managers
focused on operative and financial risk management. The regression analysis
indicated that the company size is not a reliable indication of taking
enterprise risk management by firms, which was similar to low-level hedging
strategies. Additionally, Tobin’s Q, presented by ROA and ROE, is highly correlated
with ERM as firms seek to integrate risk management in their operations.
Besides, high hedging level has a strong positive, and significant relationship
with ERM and companies with high Tobin’s Q combines high-level hedging in their
business operations. Therefore, we can conclude that the risk management system
is dependent on company value and growth strategies.
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