Influence of CEO's personal characteristics on short-term M&A performance in Russia

The definition of mergers and acquisitions term and its classification. Corporate governance as a factor of success or failure of M&A. Analyzes CEO’s personal characteristics, that describe overconfidence phenomenon, influence the outcome of the deals.

Рубрика Менеджмент и трудовые отношения
Вид дипломная работа
Язык английский
Дата добавления 17.07.2020
Размер файла 3,2 M

Отправить свою хорошую работу в базу знаний просто. Используйте форму, расположенную ниже

Студенты, аспиранты, молодые ученые, использующие базу знаний в своей учебе и работе, будут вам очень благодарны.

1.8 Managerial activity as a factor of success or failure of M&A

Top management teams, senior executives and CEOs are considered to be one of the most important factors that influence the corporate decisions that result into better or worse business performance, as these people are responsible for making decisions about financing of the company, investments and strategy (Cui & Leung, 2020). Managers and CEOs are crucial members of M&A deals processes as they make decisions about the most difficult tasks in merged companies, about their combined resources and their distribution. Furthermore, these top managers have to handle the potential cultural and personnel conflicts among employees and other managers (Bertrand and Schoar, 2003; Holcomb et al., 2009).

According to Cui and Leung (2020), managerial abilities more often consist of three main categories of knowledge that differ by the relevance to the fields of market context and by the level of transferability: firm-specific, industry-specific and general. General knowledge is the most transferable by the definition, while industry-specific knowledge narrows the field and deepens a person into the certain industry. Firm-specific knowledge is the most narrowly targeted as firm is a specific member of the certain industry and market in general. That is why horizontal M&As are considered to be less complex and demanding for managers than cross-industrial transactions because the latest occur between different market segments and thus demand industry-specific knowledge about target sector as well. Cui and Leung (2020) found out that industry-specific knowledge, experience and skills are more crucial for the management team of the company-acquirer for managing the combined operations and resources of the M&A. Managerial skills are hard to estimate precisely and it is still unclear whether there is a correlation between ability of managers to generate higher revenues and their potential ability to handle effective decisions about combined resources and activity that result with better post-transaction performance. However, Cui and Leung (2020) conduct a research which results show that acquirers whose managers have better managerial abilities have better values of financial indicators such as industry-adjusted ROA, operating cash flows, and market-to-book ratio in long-term period of 1-3 years after the M&A transaction but there was no interconnection with post-M&A stock returns. Thus, these results support the conclusion that high managerial abilities of the firm-acquirer play significant role in gaining of improved long-term performance and M&A synergy effect. However, such results are more applicable to the M&A deals within the same industry, not in cross-industry transactions as manager can apply his or her abilities better if he or she support it with using of the specific knowledge about industry he or she works in (Cui & Leung, 2020).

Nevertheless, Chen and Lin (2018) persist on the fact that superior managers have a developed ability to distinguish potentially successful and unsuccessful deals as they may be more experienced in these activities. At the same time Cui and Leung (2020) claim that qualified superior managers have better skills in resolving cultural and personal misunderstandings, work with combined assets and other resources, thus, lead to the better post M&A synergy effect. One of the reasons of M&A deal failure are wrong estimation and analysis of the target company, lack of attention to its debt and problematic integration process, that was made by not enough qualified managers (Cui & Leung, 2020). In addition, Bertrand and Schoar (2003) claim that abilities of not only superior managers (CEOs, CFOs) are important for the success of the M&A transaction but the whole management team should be included into the process in order to gain the higher M&A performance. They also say that able managers collect more needed information, both private and public, undertake non-risky target firms with valuable resources and have an ability to negotiate their further usage. Demerjian et al. (2013) also add that these managers are more aware of macro-economic conditions and their client bases and do not over- or under- estimate the risks in terms of financial indicators.

Chikh and Filbien (2011) mention that dual CEOs with higher ownership level and those, who have more experience in working with M&A deals, pay more attention to the reaction from investors, considering the M&A activity, and if the stock returns turn to be negative, these CEOs cancel the transaction as soon as possible due to their higher qualification.

Some previous studies consider educational background of the CEO as one of the variables. For instance, Malmendier and Tate (2008) take professional education as one of the CEO's characteristics, influencing the M&As. They classify this factor on several groups: technological, financial (including business and economics) and other types of professional education. The results of their research show that financial educational background positively affects the acquisition performance. Chikh and Filbien (2011) refer to educational connections as a possible way of getting the right information that is needed for correct estimation of the decisions about the preparing deal. At the same time, Kilian & Schindler (2014) find that most of the M&A deals are made by CEOs with educational background and some CEOs even have double-degrees. Betrand and Schoar (2003) note that CEOs with MBA (double degree) are more confident and thus thrive to use their knowledge even in aggressive way of behavior. Authors say that such CEOs are more active in terms of M&As as they want to maintain their reputation as a confident professional. However, Lucey, Plaksina and Dowling (2013) think the opposite - CEOs with MBA degrees are more cautious when dealing with the acquisitions as they are afraid of losing their professional reputation.

Deloitte research (2012) emphasizes that in 70% cases of successful M&A transactions the quality of CG and managers activity plays an important role. In the research of Renneboog and Vansteenkiste (2019) highlight several factors of positive impact on M&A results. They include: “CEO incentives, CEO and board connections, ownership structure, method of payment, sources of financing, target financial distress, post-merger restructuring, target acquisitiveness, political economics, and governance spillovers” (Renneboog & Vansteenkiste, 2019, p. 651).

Roberts, Wallace and Moles (2016) consider several scenarios of failure such as “an inability to agree terms, overestimation of the true value of the target, a failure to realize all identified potential synergies, external change, an inability to implement change, shortcomings in the implementation and integration processes, a failure to achieve technological fit, conflicting cultures, a weak central core in the target” (Roberts, et al., 2016, pp. 15-17). Moreover, some researches discuss another potential hazard of M&A - CG failures, and show that CG quality has a statistically significant impact on the effectiveness of M&A transactions (Bedi & Vij, 2018; Vorotilova & Kazakov, 2015; Schnatterly & Capron, 2005). From this point of view, some authors discuss the influence of managerial activity as a factor of the result of M&A transactions. For example, Yi and Xiugang (2019) claim that the managerial psychological deviation “often leads to non-efficient investment decision-making practices” (Yi & Xiugang, 2019, p. 56).

Kummer & Steger (2008) suppose that the main reason for the failure of M&A is that companies set unrealistic goals for the transaction, and managers are not able to correctly assess the complexity of the transaction process. The process of implementing M&As requires careful preparation: identifying potential goals and objectives, devising an integration plan, compatibility of corporate cultures, evaluating the combination of companies property. Hence, the confidence in potential success of M&A plays a major role (Bandura, 1977). However, there is a risk of overestimation of the capabilities of the company. In this context, managers have a tendency to strive for impossible or unlikely results. This risk of managerial overconfidence may lead to the less thorough preparation and assessment of potential transaction opportunities.

1.9 The phenomenon of CEO overconfidence

Comparing to the qualified manager whose actions positively contribute to the value of the acquisition, overconfident managers may harm the M&A activities. Such managers may inadequately examine acquisition opportunities, underestimate the potential effect and result in the value-destroying transaction (Cui & Leung, 2020). Managers' overconfidence is the psychological characteristic, arising when managers overestimate their own decisions while underestimating possible risks (Gervais, Heaton & Odean, 2002). Considering the overconfidence from the M&A perspective, Malmendier and Tate (2008) state that “overconfident managers overestimate the returns they generate internally and believe outside investors undervalue their companies” (Malmendier & Tate, 2008, p. 24). The definition emphasizes the possibility of overestimation the managers' abilities to create value and capabilities to be above average level. Moore and Healy (2008) define overconfidence as situation when “individual overestimates abilities, performance, level of control, and the probability of success” (Moore & Healy, 2008, p. 507). Managers' overconfidence leads to overvaluation of financial indexes and performance of the company and underestimation of financial risks specifically. Such managers tend to prove their excellent performance by investing into deals and projects with high risk and thus jeopardizing the whole company (Yi & Xiugang, 2019). Doukas and Petmezas (2007) mention frequency as possible indicator of CEO overconfidence. Big number of M&A deals, made during the short period of time, may signal about lack of CEO's ability of adequately assessment of the risks and opportunities of the company. There could be misunderstanding between real value of the firm and its estimation by overconfident CEO, what leads to wrong conclusions and expectation of the CEO about synergy effect and profitable gains for the company as a result of frequent acquisitions. At the same time, these CEOs may not be aware of their overconfidence and sincerely think that they do everything in the interests of the company. Malmendier and Tate (2009) add that the overconfidence level increases after incentive events and contests where the CEO or a manager gets the awards like “best manager” as it gives them the illusion that he or she is a “star”. Such things lead to the lower returns of the acquisition deal. However, most studies focus on overconfidence level of the CEO of the company-acquirer, while Kose et al. (2011) pay attention to the overconfidence of the target company as well because it is also lead to the lower financial results of the transaction in long-term perspective.

Considering the psychological studies, Moore and Healy (2008) consider that the miscalculation occurs through the reassessment of people ability to predict future events. Thus, they describe three types of overconfidence: overestimation (belief that abilities of a person are better than they are really are), overplacement (belief that one person is better than others) and overprecision (belief that a person knows the truth even if this is not so). The authors develop a theory of reverse dependence between overestimation and overplacement and personal performance. The theory suggests that people, who perform worse, often overestimate themselves while whose, who perform better, in most cases underestimate their abilities and skills. Considering overplacement, the theory suggests that if a person performs better than he or she expected, that person often overplace his or her performance comparing to the others performance and vice versa.

Hilton et al. (2011) discuss another type of overconfidence: judgemental overconfidence (overestimating the precision of one's judgments), self-enhancement biases and overoptimism toward social risks. Talankova, Tokareva and Shamigerdyanova (2019) give the typology of overconfidence as the following: miscalibration (revaluation by people ability to predict future events), better-than-average effect and illusion of control.

Renneboog & Vansteenkiste (2019) mention one of the traits, related to the term of overconfidence - narcissism. Aktas et al. (2016) define this trait as “egocentricity, lack of empathy, unrelenting search for the spotlight, an overdeveloped sense of entitlement, and even contempt towards others” and measure it with the proportion of CEO's using the first-person singular and plural pronoun during the meetings (Aktas et al., 2016 p. 125). The results of their research show positive relatedness of narcissism to probability of transaction completion and negative relatedness to deal returns and duration of the takeover process.

Most studies examine overconfidence as a negative trait of CEOs, leading to the unwanted results of the M&A deal. Nevertheless, there are some authors, who think differently. For example, Kolasinski and Li (2013) find positive outcomes of the overconfidence. They say that recent trading losses of overconfident CEOs lower their acquisitiveness and increase short-run returns.

1.10 The research gap identification

Due to differences in corporate governance models and some other markets and cultural aspects, the set of parameters affecting the effectiveness of M&As varies. Consequently, the gap identified based on theoretical basic analysis is that there are no studies that would investigate the influence of the CEO's self-confidence, which is formed by several personal characteristics, on the short-term results of M&As in Russian companies. In other words, the research identifies crucial factors that influence the level of CEOs' overconfidence in the foreign markets and tests them in the Russian one. Therefore, the study reveals whether adult CEOs make more efficient transactions due to their life and work experience. Moreover, it explores whether previous experience in a similar industry is an advantage for CEOs of Russian enterprises as big Russian companies that operates in the same industry are significant players in the national market. In addition, it is examined whether previous experience in M&As is significant for determining the success of a transaction. Many sources note the great influence of the government in the Russian M&A market, therefore, this work examines whether experience in government bodies is a determining factor in the CEO's successful decisions regarding M&A transactions. Finally, in previous studies a lot of attention is paid to the study of education as a factor affecting the overconfidence of CEOs. However, due to the cultural and national characteristics of Russian managers this study finds out how the availability of business education of CEOs affects short-term transaction results.

1.11 Hypotheses generation

Based on theoretical background and previous researches, it is stated that managerial overconfidence plays an important role in the result of M&A activities. In order to identify which of CEO's characteristics lead to the overconfidence and as a result have decisive impact on short-term performance of M&A transactions, five hypotheses are defined.

In the framework of overconfidence theory, the authors argue about the impact of the age on the M&A performance (Ferris et al., 2013, Graham et al., 2013, Eduardo & Poole, 2016). While ones claim that the managerial overconfidence increases with the age and leads to effective decision-making process in term of acquisitions activity due to improving of business perception, others suppose that older CEOs are less likely to make important decisions, regarding M&A activity, and demonstrate successful M&A performance. This is reflected in the following hypothesis:

H1: The acquisition conducted by older CEO has a stronger short-term performance than the acquisition conducted by a young CEO.

Some researches state that decision-making process in term of M&A activity can be affected by the previous experience (Capron, 1999; Doukas & Petmezas, 2007; Malmendier & Tate, 2008; Levi et al., 2009; Ferris et al., 2013; Raman et al., 2013; Kilian & Schindler, 2014, Renneboog & Vansteenkiste, 2019; Cui & Leung, 2020). It includes such factors as the previous CEO's experience in M&A transactions, similarity of the sphere, whether the CEO is an expert of particular sphere, and the work relations of the CEO with the governance. Hence, the following hypotheses are generated:

H2: The acquisition conducted by a CEO with previous CEO experience in a similar industry has stronger short-term performance than the acquisition conducted by CEO without previous experience in a similar industry.

H3: The acquisitions conducted by a CEO with a work-experience in the government has stronger short-term performance than a CEO without a work experience in the government.

H4: The acquisition conducted by a CEO with at least three previous experience of M&A deals has stronger short-term performance than the acquisition conducted by a CEO without previous experience in M&A deals.

Some researches confirm a strong correlation between presence of business education and CEO overconfidence, that can lead to positive M&A short-term performance (Betrand and Schoar, 2003; Malmendier & Tate, 2008; Kilian & Schindler, 2014). Therefore, it is assumed that:

H5: The acquisitions conducted by a CEO with business education has stronger short-term performance than the acquisition conducted by a CEO without a business education.

1.12 Research goal statement

The main objective of this study is to identify and analyze the characteristics of CEOs for the short-term results of M&A deals in the Russian market. The analysis of the literature showed that corporate governance, and CEOs, in particular, play a significant role in the management of the company, as well as in the decision-making process for M&A. Based on previous studies on this topic, several hypotheses involving the most common characteristics of the CEOs are generated. These characteristics are used in this study as independent variables. Since the research is aimed at assessing the characteristics of the CEOs of the Russian companies, the analysis uses transactions implemented by Russian public companies in the period from 2012 to 2019. It is expected that in the result of the study, the particular factors that have a crucial influence on short-term M&A performance along with CEOs personal features.

2. Methodology

This chapter explains the research objective, data collection process, sample generation process, sample description, applied event study methodology, and cross-sectional regressions in order to test the defined hypotheses, and describe the dependent and independent variables. In addition, it describes sample validity and limitations.

2.1 Data sample

In modern conditions, both the world and the Russian M&A markets are experiencing significant quantitative and qualitative changes: the pace and scale of transactions are growing, and the transactions themselves cover almost all sectors of the economy. The KPMG report (2019) shows an increase in activity in the M&A market in the segment of domestic transactions as well as in the segment of deals for the purchase of Russian assets by foreign companies. In 2019 the volume of foreign investments in the Russian economy increased by 49.5% compared to 2018. Despite the number of transactions in the Russian market is growing in the period from 2012 to 2019, their value tends to decrease. Ivanov and Peredunova (2017) note that nowadays management of the companies is not possible to make long-term decisions. As a result, companies tend to participate in transactions that can provide only short-term performance. In other words, managers try to increase efficiency by applying certain short-term solutions instead of fulfilling long-term development goals. Therefore, this leads to the decrease in the volume of transactions. In addition, the last time mega-transaction of more than 10 billion dollars in the Russian market was implemented only in 2016 (KPMG report, 2019). The reason for this is instability of the country's economic development.

In order to analyze the current situation on the Russian M&A market and effect of the management of Russian companies on the short-term M&A results, a sample of observations is formed based on the following criteria:

1. The acquiring company is a public company registered in Russia, that shares are traded on the Moscow Exchange (MOEX) since the analysis of short-term results is based on changes in stock prices.

2. The acquiring company is the parent company. M&A transactions of subsidiaries are excluded since decisions on transactions by subsidiaries are made by local managers.

3. The announcement of the transaction by the company occurred from January 1, 2012 to December 31, 2019.

4. Stock prices for 130 days before and 10 days after the announcement date are available on Thomson Reuters Datastream and Investing.com.

5. The sample includes companies from various industries. However, based on Doukas and Petmezas (2007) and Kilian and Schindler (2014) studies, financial institutions such as banks, insurance companies, venture, and investment funds are excluded because they have different balance sheet structure and much greater financial leverage that has different meaning for financial enterprises than for non-financial companies.

6. Based on the research of Malmendier and Tate (2008), the sample includes only those transactions where the buyer company acquired a controlling stake of the target company, at least 51%. Since the result of such a transaction is a change of the target company's ownership, and the buyer company can mainly own, use and manage the assets of the target.

Figure 6 M&As involving Russian companies Source: RBC.ru (2020)

2.2 Time frame

For several years, the Russian M&A market has been influenced by many significant factors caused by economic and political incidents. Ivanov (2017) emphasizes that from 2013 to the present, the global market for M&A transactions is rising. According to the theory of wave development of the global M&A market, from 2003 to 2013 in the global market there was a wave, characterized by the realization of transactions within the framework of the implementation of the strategy of cross-border horizontal and vertical integration, as well as a sharp increase in integration transactions in the sectors related to construction and finance. However, since 2014, according to experts, there is a new wave, which is characterized by an increase in the number of transactions in high-tech industries and the services sector. But there is a decrease in the financial sector and the mining industry. A further increase in the share of cross-border transactions (mainly between companies from developed and developing countries) is also expected.

RBC.ru (2020) notes that in recent years enlargement is taking place in many sectors of the Russian economy, large business is beginning to spread to sectors that it has not previously been interested in. However, this does not affect the number of transactions involving Russian companies. Palnichenko, Mikheeva and Kulumbetova (2015) note that the Russian M&A market has a significant decrease in the activity of companies since 2012. Moreover, there is an impact on the Russian market and operating of the Russian companies of the currency crisis in Russia in 2014. In order to take into account the possible effect of the consequences of the crisis and the wave theory effect, this study uses transactions completed in the period from 2012 to 2019.

2.3 Variables

2.3.1 Depending variables

An analysis of previous researches shows that CAR values are used as a dependent variable to test and evaluate how various personal characteristics of the CEO and features of the board of directors influence the performance of M&A transactions. The CARs that are calculated for the event windows at 21 days and 11 days are used as a dependent variable in this study. Each event window uses CARs that correspond to the most significant value of the test statistics. Such variable is used as a dependent because it reflects the change in the stock returns of companies and the market reaction to the announcement of M&As. Consequently, on the basis of calculated CARs, it can be concluded about the short-term results of M&As and also analyze whether M&As create value or destroy it. Thus, this study presents two dependent variables: CAR21days and CAR11 days.

2.3.2 Independent variables

Based on analysis of other exploratory studies on M&A and CG topics, it can be concluded that the evaluation of the influence of independent variables proceeds with the help of the regression model. For this research, the analysis of the secondary data allows to identify several CEO characteristics that are taken as independent variables. The descriptions of the variables are presented in the table below.

Table 2

The description of the independent variables

Name

Description

Age

This variable is used in numerical format as a linear variable. Hypothesis 1 suggests that the older the CEOs, the more efficient the transactions they make. In other words, age linearly affects the short-term outcome of a transaction.

CEOIndRelation

This variable is related to whether the CEOs of the takeover company has the previous working experience in the same industry at the moment of the transaction. According to Hypothesis 2, this variable also has linear influence, so that it is claimed that if the CEO has previous working experience in the related industry, he or she is more likely to conduct an effective M&A deal. This variable is also used as a dummy variable, and codes as binary: yes (1), no (0).

CEOGovExp

As the research explores the Russian market, many resources highlight the high influence of the government on the M&A market. Therefore, CEOGovExp variable is related to Hypothesis 3 and indicates whether the CEO of a takeover company has previous work experience in government organizations. It is assumed that government connections lead to more effective transactions. To use this variable in the linear regression, a dummy variable is created, and codes as binary: yes (1), no (0).

CEOM&AExp

This variable is used as a dummy variable and reflects whether a CEO undertook three M&A deals for the past ten years. According to Kilian and Schindler (2014), if a CEO has pursued three or more transactions for in the ten-year sample period, he or she can be characterized as overconfident, and it leads to the negative effect on deal's result. However, the KPMG report (2010) concludes that CEOs with previous M&A experience illustrate better performance in future deals. As for this research, Hypothesis 4 assumes that a CEO who conducted three or more transactions in the past ten periods is likely to conduct a deal with greater results. This variable is encoded as as binary: yes (1), no (0).

Business education

This variable is used to determine whether business education affects the outcome of transactions. Hypothesis 5 assumes that directors with a degree in business or management make more efficient transactions. To use this variable, a dummy variable is created and encoded as binary: yes (1), no (0).

2.3.3 Control variables

In order to take into account the influence of various characteristics of takeover companies as well as the specifics of M&A transactions in the market reaction to deals' announcements, control variables are included in the model. Following Baker et al. (2012), the calculation of financial indicators of companies is based on the annual report for the financial year preceding the announcement of the transaction. Thus, based on previous studies, the following variables used in this study are identified.

FirmSize variable can be defined by number of vacancies of revenue of the company. According to SPARK service, which ranks the size of the enterprise by revenue, large companies have revenue of more than RUB 2000 million, medium-sized - RUB 801-2000 million, small-sized - RUB 121-800 million and those whose revenue is less than RUB 121 million are defined as micro-enterprises. In order to use this variable in the model, a dummy variable is created, so that: `0' - micro, `1' - small, `2' - medium, and `3' - large.

According to Yim (2013), return on assets (ROA) assesses the profitability of the company and is included in the model as the firm-specific control variable that reflect takeover's operating performance. ROA is calculated by the formula of net income divided by total assets.

Another variable that reflects the specifics of the enterprise's performance is Tangibility. It is an indicator that estimates the proportion of tangible assets in the overall assets of the company. Tangibility is calculated as tangible assets divided by total assets (Yim, 2013).

Financial leverage is an index that estimates the riskiness of the business, based on its financial indicators. It is calculated as a ratio of debt divided by sum of equity and debt. Financial debt consists of short-term loans and long-terms debt, except other non-current liabilities (Ferris, Jayaram & Sabherwal, 2013)

The natural logarithm of Assets that is stated on the firm's balance sheet is used as to the control variable. This variable is another way to categorize a firm size (Ferris, Jayaram & Sabherwal, 2013).

2.4 Data collection

For conducting the empirical research, primary and secondary data can be used. In the process of this study primary data is not collected, and surveys, questionnaires, and observations are not conducted. Therefore, secondary data is used, that includes various journals, scientific articles and other sources that contain previous studies on this topic as well as databases of various information services. In addition, the research uses both qualitative and quantitative data. Quantitative data refers to data on stock prices of companies, Moscow Exchange Index prices and financial indicators of companies. Qualitative data means non-numeric and non-standardized data such as full names of CEOs, their age, business education, experience working as CEO, and so on. Thus, there is a classification of qualitative data. In order to conduct empirical analysis, the obtained data is encoded using binary variables. The collection of qualitative and quantitative data occurs in following stages.

The first stage is the formation of a general population of all M&As implemented in the Russian market during the period from 2012 to 2019 with Russian acquirer and target companies, that is downloaded from the Zephyr database. The total number of observations in the general population is 7472. The primary downloaded data is double filtered: firstly, individual entrepreneurs and private companies are excluded and then some companies are also deleted from the list due to the unavailability of the needed information. Then, the sample is formed from the general population based on the criteria described above.

After the sample is formed, in the second stage the data on the prices of the shares of the purchasing companies are uploaded for 130 days before and 10 days after the announcement date of each transaction, using the Thomson Reuters and Investing.com databases.

In the third stage in accordance with a specific event window and estimation window, the data of RTS index prices for each transaction is collected from the official website of the Moscow Stock Exchange.

The next step is to search the financial results of companies that are used as control and dummy variables. For this, financial statements and calculated financial ratios of purchasing companies are used based on data obtained from the SPARK information service. In addition, data on the size of companies is unloaded. The classification is based on revenue.

The final stage includes the formation of the dataset of CEOs who are in the position at the time of the transaction completion. To collect the data about personal characteristics multiple online resources are analyzed, including official websites of companies, their press-centers and archives, online media resources, magazines such as Kommersant.ru, RBC.ru, Investing.com, TAdviser.ru, and SPARK. As a result of data collection process the final sample consists of 251 M&A deals and CEOs that conducted them.

2.5 Event study methodology

2.5.1 Evaluation of M&A performance

Considering the approaches of performance evaluations of M&A, the literature distinguishes two kinds of M&A effects: short-term and long-term. As for short-term effect, an announcement of M&A transaction increases the value of the target company shares while the value of the acquiring company shares remains constant or falls. Roberts et al. (2016) claim that “the tendency for the target share price to rise has important implications for the short-term financial success of the acquisition” (Roberts et al., 2016, p. 175). Renneboog & Vansteenkiste (2019) consider that long-term effect can be measured by stock returns or accounting measures. However, it is quite complicated to isolate the M&A effect from other effects influencing the enterprise over the post-transaction years (Renneboog & Vansteenkiste, 2019). For example, in the Harvey (2015) research six categories of long-term performance indicators are used: profitability ratios, expenses ratios, liquidity ratios, financial leverage ratios, growth and investment returns.

There are several methods of evaluation the effect of M&A on different performance indicators of companies. In Krishnakumar and Sethi (2012) and Martynova (2008) researches the accounting returns method is used that involves the analysis of the accounting performance of the joint enterprise measured in terms of Return on Assets or Return on Equity in two to three years after M&A transaction. In the analysis the authors use cash flows classified as sales less CGS, marketing and administration expenses, depreciation and amortization, goodwill, and tax expenses of pre- and post-acquiring transaction. Sirower and O'Byrne (1998) use Economic Value Added (EVA) method, where the enterprise performance is measured by measuring deducting cost of capital from operating profits. This study illustrates a high correlation of the abnormal market returns and the EVA Performance returns. Questionnaire Approach is represented in Datta and Grant (1990), Homburg & Bucerius (2006) and Brock (2005) researches. This method is applying in cases of acquisition of small companies, individual divisions or private acquisitions when the M&A transactions do not have a significant effect that can be measured by objective methods. In the process of evaluation, the business executives asking to rate the extent to which they have realized their preliminary objectives in post-M&A period by using financial and non-financial ratios. Case Study Approach is represented in Applebaum, Roberts and Shapiro (2009) research. The idea of this method is to analyze a small sample of M&A deals to figure out the factors that influenced on success or failure of the particular cases.

Accounting-based approaches to measure operating performance as an indicator of failure of success of M&A have both notable advantages and disadvantages. Firstly, M&A performance consists of both financial and nonfinancial indicators, but such approaches do not cover the M&A nonfinancial performance. However, market-based approach can cover this gap. Secondly, accounting-based approach depends on manipulation with numbers in financial statement (Cui & Leung, 2020). The mentioned earlier market-based approach uses long-term stock returns for assessing the M&A performance. It can be explained by the fact that M&A performance is aimed to achieve the goal of the company, which is to maximize the wealth of shareholders (Papadakis & Thanos, 2010). Shareholders' wealth and expectations are measured by stock prices. That is why stock prices are used as a measuring value for evaluation of M&A performance. However, Cui & Leung (2020) highlight that operating measures and stock returns are different. Operating performance reflects actual or realized return, while stock returns mostly show the expectations of the shareholders about the future performance of the company and also can predict capital market inefficiency.

Despite the abundance of different assessment methods, stock-market-based or event studies is one of the most widespread method among researchers, where the firm's performance is defined by return on the market portfolio, intercept term and sensitivity of the return on the entity to market returns. This theory was developed by Fama in 1970 and suggests that the impact of an event on a company directly affects its stock returns. This method is based on the theory of an effective and rational market, which instantly responds to any changes in the external and internal environment of the company. Thuy (2015), Lips (2016) and Hoving (2017) use Event Study method to evaluate the influence of CG to performance of M&A. The feature of this method is in a statistical way of proving the fact that an event caused a change in a certain indicator of a company. Lips (2016) uses Cumulative Abnormal Returns (CARs) approach, taking the sum of all abnormal returns over the period of interest. According to Lips (2016), “CARs capture the total firm-specific stock movement for an entire period when the market might be responding to new information” (Lips, 2016, p. 7).

2.5.2 Defining the event window

In order to evaluate the performance of each M&A deals of this research sample, it is necessary to calculate CARs observed around the transaction announcement date. An important step is to define an event window, and the day the transaction is announced is taken as Day `0'. Despite there are some researches that illustrate the significant effect of the duration of the event window on the overall result (Aintablian & Roberts, 2000; Scholtens & De Wit, 2004), the difference of market and industry mechanisms does not allow to set the exact principle for defining the time horizon of the study. Some researches argue that the wider event window may lead to inaccurate results (Scholtens & De Wit 2004; Hoving, 2017). However, a small event window can cause statistically insignificant results. That is why it is decided to calculate CARs for the interval of 21 [-10; 10] trading days from the publication of the event day (t = 0) and 130 days to the start of the event window as the estimation window and for the interval of 11 [-5; +5] trading days and 120 days before an announcement.

2.5.3 Cumulative abnormal returns calculations

In accordance with the standard Event study methodology, the first step is to calculate the actual return Rit for company i and the event t is estimated by the daily percentage gain. The formula for calculations is:

(1)

where: Pt - the closing price for security i on the day t;

Pt-1 - the closing price for security i on the previous day t-1.

It is important to mention that only trading days are taken into account but not calendar days.

For estimation of the expected returns it is decided to apply Capital Asset Pricing Model (CAPM), as Pozdnyakov (2016) suggests to use this model because “it well explains the dispersion of indicators” (Pozdnyakov, 2016, p. 44). Therefore, the CAPM model is a special case of complex multifactor models that represents a stable linear relationship between market profitability and the returns of the company stocks. There are several advantages of this model. Firstly, the change in the level of expected profitability occurs during the event window. Also, it contributes to the leveling of market-related returns and a decrease in the variance of abnormal returns, as the dispersion is important for tracking the effect of an event, therefore, the lower the variance - the more accurate the estimate. Finally, investors in this model are rational and fair in evaluating the company, thus, it is possible to determine market returns, for example, using any stock index. In case of this research, the Moscow Exchange index as a result of the reaction of the Russian market to the publication of financial statements is evaluated. It also requires the calculation of actual profitability both during the event window and in the estimated interval by the formula:

(2)

In order to calculate the expected returns the following formula is used, that considers a linear relationship between market profitability and stock price:

(3)

In this case, ?? and ?? are the parameters of the CAPM model that are estimated using the OLS method (ordinary least squares), where is ?? is a constant and ?? is the linear regression coefficient of the estimated model, calculated during the forecast period in the intervals [-130 ? t ? -11] and [-120 ? t ? -6] for each transaction. The regressions are built by the Excel data analysis package. This process is carried out with each company in the sample during the defined period. Having obtained the coefficients and calculated the RTS index returns for each company, then the forecast values of expected returns for all days of the event window are calculated.

The next step is the calculation of abnormal returns. AR represents the standard deviation of actual returns from expected returns and reflects the effect of the event on the stock market, in this case, the effect of a change (increase or decrease) of the stock price and is calculated by formula:

(4)

In order to determine the total accumulated effect of the event during the event window the cumulative abnormal returns are calculated as a sum of all abnormal returns for [t1; t2] by the formula:

(5)

where: t1 = -10 and -5 - lower border of the event window;

t2 = 10 and 5 - upper border of the event window.

After that it is necessary to estimate average abnormal returns by averaging the observations of abnormal returns for all companies included in the sample for the event estimation and event window. For each of the day in the considered period the AAR is calculated by formula:

(6);

where: ARit is abnormal returns of the companies;

N is the total number of all companies in the sample.

Before proceeding to the final stage that is calculating the statistical significance of the obtained values, it is necessary to check the obtained values AAR for the normality of the distribution and whether the t-test can be applied on this data. For this purpose the distribution chart of AAR for two event windows is built. According to Walker (1995), the t-statistic can be applied only when abnormal returns have a normal distribution, their depreciation is zero equal and the sample has more than 120 observations. If the size of the sample is matched the requirements, and the received values are normally distributed, it is necessary to check whether the model can be trusted. For this purpose, the H0 and H1 are formulated, so that:

(7)

Then the statistical significance of the results is determined by the formula:

(8)

Test statistics for assessing the significance of average abnormal returns are calculated by the formula:

(9)

where: ?? is the estimated value of the checked statistics;

?? is the length of the event window;

??2 - normal dispersion of abnormal returns on shares on the event window.

The value of test statistic is compared with a critical value, which is the t-statistic of the Student Distribution for the 5% significance level with ?? - 1 = ??2 - ??1 degrees of freedom. If the value of the test statistics is less or equal the critical value (?? ? ??????????), then the null hypothesis (H0) is confirmed at the corresponding significance level. That means that the event had not the effect on the stock price, otherwise, if the test statistics is higher than the critical value (??> ??????????), then the null hypothesis (H0) is rejected and hypothesis H1 is accepted. That means that the event had a significant impact on the change in the stock price. According to Walker (1995), test statistics is considered as reliable if the two types of errors are at an acceptable level. The Type 1 error arises when the null hypothesis is erroneously rejected. The Type 2 error occurs when the null hypothesis is erroneously accepted.

2.6 Regression

The second part of the calculations is focused on the estimation of how different independent variables affect the M&A short-term performance. As the final sample consists of panel date that includes both cross-sections (companies) and time series (announcement dates) date, the cross-sectional regression is used in order to analyze whether the CARs depend on several independent variables. The literature review of this study shows that a huge number of factors affect the CEO decision-making process in M&A transactions. The regression model includes variables that are defined in the previous section. In order to evaluate the significance of the obtained regression coefficients, the ordinary least of squares method (OLS) is used. Thus, the value of each coefficient is obtained from t-statistics using the SPSS statistical package. Regression is based on the following equations. For CARs that are calculated for 21 days event window:

(10) CAR21Dayit = б + в1 Ageit+ в2 CEOIndustryrelationit + в3 CEO Governmentworkexperienceit + в4 CEOM&AExperienceit + в5 Business educationit + еit

For CARs that are calculated for 11 days event window:

(11) CAR11Dayit = б + в1 Ageit+ в2 CEOIndustryrelationit + в3 CEO Governmentworkexperienceit + в4 CEOM&AExperienceit + в5 Business educationit + еit

A separate model is built in order to take into account the influence of certain characteristics of the company on the results of M&As. To do this, the variables described in the section 2.3.2 are included in the regression model. In the model, control variables are designated as Xit. Thus, the regression model is following:

For CARs that are calculated for 21 days event window:

(12) CAR21Dayit = б + в1 Ageit+ в2 CEOIndustryrelationit + в3 CEO Governmentworkexperienceit + в4 CEOM&AExperienceit + в5 Businesseducationit + Xit + еit

For CARs that are calculated for 11 days event window:

(13) CAR11Dayit = б + в1 Ageit+ в2 CEOIndustryrelationit + в3 CEO Governmentworkexperienceit + в4 CEOM&AExperienceit + в5 Businesseducationit + Xit + еit

In order to prove the validity of the OLS method, it is necessary to conduct a multicollinearity test, a heterogeneity test, a heteroscedasticity test, testing for normality and an autocorrelation test. To assess the significance of the coefficient of determination in regressions the Fisher test is applied by using SPSS.

2.6.1 Testing for Multicollinearity

According to Brook (2008), multicollinearity is the presence of a linear relationship between the explanatory variables of the regression model. Variables that strongly affect each other, as a rule, have large standard errors, which may affect the significance of these variables. To check for the presence of multicollinearity in the studied models the Pearson correlation matrix is constructed using the SPSS statistical package. If multicollinearity is detected in a pair of variables, and the value is not greater than the threshold values equal to -0.8 or 0.8 (Brook, 2008), one of the collinear variables is discarded.

2.6.2 Testing for Heterogeneity

On the next step it is necessary to conduct a heterogeneity test in order to take into account certain forms of variable displacement in regression. According to Brook (2008), if the terms of the standard error in the measurement of the time period systematically deviate from zero, this leads to a large residual sum of squares, which violates the assumption of the OLS method that independent variables are not associated with an error. Based on previous studies by Kilian & Schindler (2014), Ferris, Jayaraman and Sabherwal (2013) and Yim (2013), the test is based on the distribution of data by the years included in the time-frame sample. For this purpose, effects fixed over the year are used to control time-varying effects that are constant in the cross section. As the determinant of the sampling time, the date of the announcement of the transaction for each observation in the sample is taken. To calculate the criterion of homogeneity of variances Levene's test is conducted using the statistical package SPSS. Generally, population variances are not equal if p <0.05.

2.6.3 Testing for Heteroskedasticity

...

Подобные документы

  • Factors that ensure company’s global competitiveness. Definition of mergers and acquisitions and their types. Motives and drawbacks M and A deals. The suggestions on making the Disney’s company the world leader in entertainment market using M&A strategy.

    дипломная работа [353,6 K], добавлен 27.01.2016

  • Detection the benefits of Corporate Social Responsibility strategies that would serve as a motivation for managers and shareholders in the context of a classical firm, which possesses monetary preferences. Theoretical framework and hypothesis development.

    курсовая работа [319,5 K], добавлен 14.02.2016

  • Major factors of success of managers. Effective achievement of the organizational purposes. Use of "emotional investigation". Providing support to employees. That is appeal charisma. Positive morale and recognition. Feedback of the head with workers.

    презентация [1,8 M], добавлен 15.07.2012

  • The main idea of Corporate Social Responsibility (CSR). History of CSR. Types of CSR. Profitability of CSR. Friedman’s Approach. Carroll’s Approach to CSR. Measuring of CRS. Determining factors for CSR. Increase of investment appeal of the companies.

    реферат [98,0 K], добавлен 11.11.2014

  • The concept, essence, characteristics, principles of organization, types and features of the formation of groups of skilled workers. The general description of ten restrictions which disturb to disclosing of potential of group staff and its productivity.

    реферат [29,7 K], добавлен 26.07.2010

  • Evaluation of urban public transport system in Indonesia, the possibility of its effective development. Analysis of influence factors by using the Ishikawa Cause and Effect diagram and also the use of Pareto analysis. Using business process reengineering.

    контрольная работа [398,2 K], добавлен 21.04.2014

  • Применение современных компьютерных технологий в делопроизводстве. Реализация документооборота лингвистической школы "Success", как структурного подразделения КГОУ СПО ХПК, в среде "MS Outlook". Решение задач учёта и контроля исполнения документов.

    дипломная работа [3,5 M], добавлен 26.05.2012

  • Origins of and reasons for product placement: history of product placement in the cinema, sponsored shows. Factors that can influence the cost of a placement. Branded entertainment in all its forms: series and television programs, novels and plays.

    курсовая работа [42,1 K], добавлен 16.10.2013

  • The audience understand the necessity of activity planning and the benefits acquired through budgeting. The role of the economic planning department. The main characteristics of the existing system of planning. The master budget, the budgeting process.

    презентация [1,3 M], добавлен 12.01.2012

  • The concept and features of bankruptcy. Methods prevent bankruptcy of Russian small businesses. General characteristics of crisis management. Calculating the probability of bankruptcy discriminant function in the example of "Kirov Plant "Mayak".

    курсовая работа [74,5 K], добавлен 18.05.2015

  • Інформація та структура підрозділів фірми. Процес виконання ділової гри. Основна задача оптимального розкрою матеріалів фірми. Виробнича функція фірми "Success". Перевірка наявності мультиколінеарності пояснювальних змінних та автокореляції залишків.

    учебное пособие [299,0 K], добавлен 09.10.2013

  • Impact of globalization on the way organizations conduct their businesses overseas, in the light of increased outsourcing. The strategies adopted by General Electric. Offshore Outsourcing Business Models. Factors for affect the success of the outsourcing.

    реферат [32,3 K], добавлен 13.10.2011

  • Six principles of business etiquette survival or success in the business world. Punctuality, privacy, courtesy, friendliness and affability, attention to people, appearance, literacy speaking and writing as the major commandments of business man.

    презентация [287,1 K], добавлен 21.10.2013

  • The main reasons for the use of virtual teams. Software development. Areas that are critical to the success of software projects, when they are designed with the use of virtual teams. A relatively small group of people with complementary skills.

    реферат [16,4 K], добавлен 05.12.2012

  • Применение современных методов менеджмента качества. Повышение эффективности производства, снижение затрат и повышение качества аудиовизуального произведения. Цифровое кинопроизводство в формате High Definition. Рост требований к техническому уровню.

    контрольная работа [1,7 M], добавлен 10.02.2012

  • Definition of management. The aim of all managers. Their levels: executives, mid-managers and supervisors. The content and value of basic components of management: planning, organizing, coordinating, staffing, directing, controlling and evaluating.

    презентация [414,2 K], добавлен 16.12.2014

  • Critical literature review. Apparel industry overview: Porter’s Five Forces framework, PESTLE, competitors analysis, key success factors of the industry. Bershka’s business model. Integration-responsiveness framework. Critical evaluation of chosen issue.

    контрольная работа [29,1 K], добавлен 04.10.2014

  • History of development the world leader in the production of soft drinks company "Coca-Cola". Success factors of the company, its competitors on the world market, target audience. Description of the ongoing war company the Coca-Cola brand Pepsi.

    контрольная работа [17,0 K], добавлен 27.05.2015

  • Понятие и сущность мотивации трудовой деятельности персонала. Особенности применения методов стимулирования в коммерческих организациях на примере Levi’s Russia. Методы нематериального стимулирования персонала. Вклад сотрудника в прибыль компании.

    курсовая работа [27,8 K], добавлен 15.05.2014

  • Mergers and acquisitions: definitions, history and types of the deals. Previous studies of post-merger performance and announcement returns and Russian M&A market. Analysis of factors driving abnormal announcement returns and the effect of 2014 events.

    дипломная работа [7,0 M], добавлен 02.11.2015

Работы в архивах красиво оформлены согласно требованиям ВУЗов и содержат рисунки, диаграммы, формулы и т.д.
PPT, PPTX и PDF-файлы представлены только в архивах.
Рекомендуем скачать работу.