# Impact of cross-country mergers and acquisitions on EBITDA margin

Mergers and acquisitions are activities undertaken by businesses for its advantages such as:

- wider geographical presence,
- consolidation of operations,
- technological up-gradation,
- economies of scale and,
- improving profitability.

It has a number of effects as far as the financial performance is concerned. The previous article established the impact of **mergers and acquisitions (M&A)** activities on the acquiring firms’ operating cost. Dataset containing the values of 100** M&A** deals from Europe and Asia from 2007-2017 was chosen. It was found that in the pre-merger period leverage ratio had significant impact on the operating cost while in post-merger period, geographical expansion and R&D innovation had significant impact on the operating cost.

In this article, the impact of **M&A** on the acquiring firms’ *EBITDA* margin is tested. *EBITDA (Earnings before interest, tax, depreciation and amortization)* is the financial measure that is used for a variety of analytical purposes. However, the primary purpose is to provide a measure for the raw operating earnings of the business (Luciano, 2017). In this article quantitative analysis using regression is applied to the dataset to show the acquirer’s pre-merger and post-merger *EBITDA* margin performance.

## Proposed hypothesis

In order to examine the impact of mergers and acquisitions on the *EBITDA* margin of the firms, the following hypothesis is prepared:

H

_{0}: There is no effect of cross -country mergers and acquisitions on EBITDA margin in the period 2007-2017.

The data set is divided into two periods.

- The time period t-1 represents the pre-merger period and,
- the time period t+1 represents the post-merger period.

All data was specific to the acquiring firms only.

## Impact of geographical expansion on *EBITDA* margin in the pre-merger period

The dependent variable is *EBITDA* margin and the set of independent variables include:

- size of the firm
- geographical expansion
- R &D innovation
- leverage ratio.

The following table represents the regression results of the acquirer firm before the **M&A** deal took place. It is represented by ‘t-1’. The results were obtained through STATA. The results are represented below.

Dependent variable | Independent variables | Coefficient | P-value | R^{2 }– value | Adjusted R^{2}-value |
---|---|---|---|---|---|

EBITDA Margin | Size of firm | 0.7555 | 0.895 | 0.7215 | 0.6932 |

Geographical expansion | 3.5242 | 0.026 | |||

R&D innovation | 7.41250 | 0.452 | |||

Leverage ratio | 0.6528 | 0.7852 |

R square and the adjusted R square values are 0.72 and 0.69 respectively. This indicates that the independent variables explain about 69% variation in the dependent variable. This means that the model is quite good enough to explain variation in the dependent variable (Sarstedt and Mooi, 2014). Thus, there is a probability that the null hypothesis will be rejected.

The results clearly indicate that only geographical expansion has a significant impact on *EBITDA*. This is because the ‘p’ value of geographical expansion (0.026) is less than the significance value of 0.05. The other variables such as size of the firm, R&D innovation and leverage ratio has P-value greater than 0.05 indicating a non-significant impact on the *EBITDA* margin.

The coefficient of geographical expansion indicates that with one unit of increase in the geographical expansion, the *EBITDA* margin increases by about 3.52 units. (White, 2019) showed that as the firm expand geographically and widen their reach their *EBITDA* margin also increases positively.

## Impact of M&A on *EBITDA* margin

The dependent variable is *EBITDA* margin and the set of independent variables include:

- size of the firm
- geographical expansion
- R &D innovation
- leverage ratio.
- Deal size
- Industry relatedness

The following table represents the regression results of the acquirer firm after the merger & acquisition deal took place.

Dependent variable | Independent variables | Coefficient | P-value | R^{2 }– value | Adjusted R^{2 }-value |
---|---|---|---|---|---|

EBITDA margin | Size of firm | 3.52412 | 0.425 | 0.7525 | 0.7125 |

Industry relatedness | 4.041252 | 0.504 | |||

Deal size | 0.3561 | 0.105 | |||

Geographical expansion | 4.4807 | 0.624 | |||

R&D innovation | 5.2369 | 0.352 | |||

Leverage ratio | 3.425 | 0.000 |

R square and the adjusted R square values are 0.75 and 0.71 respectively. This indicates that the independent variables explain about 71% variation in the dependent variable. This means that the model is quite good enough to explain variation in the dependent variable (Mooi, 2014). This means that there is a probability that the null hypothesis that there is no impact of merger/acquisition on EBITDA will be rejected.

The regression results for the post-merger performance indicate that only the leverage ratio has a significant impact on the *EBITDA* margin of the firm. This is because the ‘p’ value of only leverage ratio (0.000) is less than the significance value of 0.05. The other variables such as the size of the firm, industry relatedness, deal size, geographical expansion and R&D innovation have a ‘p’ value greater than 0.05. This indicates a significant impact on *EBITDA*.

The coefficient of leverage ratio indicates that with one unit of increase in the leverage ratio, the *EBITDA* margin increases by about 3.42 units. Akhtar (2012), in his article, provides the relationship between leverage ratio and the *EBITDA* margin. The study specifies that firms with a higher *EBITDA* margin will opt for a higher leverage ratio in order to improve their financial performance. Bienz, (2016), supported the following results and showed that firms that had a higher *EBITDA* margin, the stakeholders and firm had a positive experience in terms of profit margins.

*EBITDA* an important indicator of the financial health of a company

The *EBITDA* margin is a crucial indicator of the financial well-being of a business. Firms need to maintain to this financial ratio in order to provide the benchmark of earnings. In this study, the pre-merger period the geographical expansion led to an increase in *EBITDA* margin while in the post-merger period it was the leverage ratio that impacted the *EBITDA* margin. Therefore, in the post-merger period, the firms’ position to pay off their obligations with their assets greatly determine their *EBITDA*.

#### References

- Akhtar, S. (2012).
*Relationship between Financial Leverage and Financial Performance : Evidence from Fuel & Energy Sector of Pakistan*.*4*(11), 7–18. - Bienz, C. (2016).
*Leveraged Buyouts in Norway*. (I), 1–17. - Luciano, R. (2017).
*MARKETS EBITDA as an indicator of earnings quality*. 29–34. Retrieved from https://www.finsia.com/docs/default-source/jassa-new/jassa-2003/1_2003_ebitda.pdf?sfvrsn=8 - Sarstedt M, Mooi, E. (2014). Conducting a Cluster Analysis. In
*A Concise Guide to Market Research*. https://doi.org/10.1007/978-3-642-53965-7 - White, R. (2019).
*WTC revenue + 68 %, EBITDA + 52 %, NPAT + 48 %, on track to deliver FY19*. (February), 1–9.

I am a management graduate with specialisation in Marketing and Finance. I have over 12 years' experience in research and analysis. This includes fundamental and applied research in the domains of management and social sciences. I am well versed with academic research principles. Over the years i have developed a mastery in different types of data analysis on different applications like SPSS, Amos, and NVIVO. My expertise lies in inferring the findings and creating actionable strategies based on them.

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