Влияние сделок M&A на финансовые показатели российских цифровых экосистем

Калмыков Олег Игоревич
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Работа доступна по лицензии Creative Commons:«Attribution» 4.0

В данном исследовании проанализирован эффект от сделок по слияниям и поглощениям на финансовые показатели среди 5 крупнейших игроков рынка цифровых экосистем в России. Так как M&A – один из способов захвата новых ниш и усиления позиций на рынке экосистем, мы предполагаем, что рынок реагирует на такие активности со стороны компаний. Таким образом, цель исследования – исследовать эффект от M&A сделок на финансовые показатели компаний, создающих цифровые экосистемы в России. В качестве метрик финансовых позиций компаний были выбраны метрики повышенной доходности CAR и BHAR в рамках event study подхода. Были построены линейные регрессии и рассмотрены факторы, влияющие на повышенную доходность компаний после проведения M&A сделок.
По результатам, можно установить, что активность в сфере слияний и поглощений имеет эффект на такие метрики, но только в крайне краткосрочном периоде – в периоде (-1 день; +1 день) и в день самой активности (анонс). Были выявлены факторы, имеющие связанность с повышенной доходностью в эти периоды .

Many articles are devoted to the measuring performance of M&A deals in different markets and many metrics are used. Since M&A activity is one of the key components in the strategy building in giant companies, they are studied to get an answer to one important question: How M&A deals effect on performance of the acquirer? Literature review represents that results are ambiguous, and many variables must be taken into account. There are used different metrics, including event study indicators, accounting-based indicators, subjective assessments and some mixed metrics. And many other variables are used as independent variables with an aim to find correlation between them and these indicators of company’s performance after M&A. Still, the question of finding right variable is opened and for each market there are certain specifics.
On the other hand, there is number of studies, that are devoted to the ecosystem creation processes among different companies. This term evolved as a parallelism between business and ecology and become popular in the scientific community. Many authors examined various aspects of ecosystems, and eventually there occurred term of “digital ecosystem”, close to the “multiservice platform” in business. Then, there occurred certain research gap. It is obvious and can be empirically observed that during ecosystem creation many giants such as Google, Amazon, Apple, Alibaba and other big tech companies acquire some specific niche players to expand their product lines. Still, this field is understudied.
On this point there can be provided a parallel – not only global giants build ecosystem, but some Russian companies also do it. There are 5 major players – Yandex, Mail.ru Group, Sber, MTS and Tinkoff, which take an active part in creation of digital ecosystems in Russia. They are initially affiliated with 3 industries – online service providers, banks and telecom, but they all move in the direction of multiservice platforms. As they initially had large client base, they can try different product and to build an ecosystem of products around their clients. Still, they can do it via 2 options – to do it by themselves, organically growing internal startups and launching products or to acquire certain market player that provides perspective service. And this field is also understudied.
This research connects this 3 fields and there was made an attempt to review how M&A deals effect on acquirers’ financial indicators. Author primarily concentrates on financial performance on stock market, then “financial performance” does not include here accounting-based metrics. There were built several models in the event study approach, using common methodology of CAR and BHAR analysis. There were used some commonly used event-connected financial metrics, some indicators from financial statements of companies. An author proposes usage of 3 control dummy variables in order to state some hypothesizes and 1 common macroeconomic variable.
Before creating mentioned methodology, there were stated research goal, research question and hypothesizes to be tested to reach the objectives.
Research goal: to investigate what is the effect of M&A deals on companies’ financial performance in Russian digital ecosystems.
Research question is corresponding to the research goal and can be stated as: what is the effect of M&A deals on companies’ financial performance in Russian digital ecosystems?
With an aim to answer this question, there were stated 4 research hypothesizes:
• M&A deals have an effect on long-term financial performance of the acquirers (ecosystem creators).
• M&A deals have an effect on short-term financial performance of the acquirers (ecosystem creators).
• M&A deals have an effect on financial performance of the acquirers’ rivals.
• M&A deals affiliation with ecosystem creation is positively related with financial performance of the acquirers.
Also, there must be studied some other important aspects, such as relatedness of M&A impact on performance with initial industry affiliation, with method of acquisition (directly or through subsidiary) and with some other event factors.

However, there are met some limitations. According to the regression analysis, many models have low validity. This can be justified as due to the small volume of data sample. Such point is common for specific researches in M&A sphere, but still a lot work can be done in further researches. An author states that this research provides great ground for further academic articles and proposes to include some more companies in sample – specifically, VTB, Vimpelcom and Tele2 and to conduct research after some years, when the ecosystem market will be more developed. These companies are not playing high roles in Russian digital ecosystem market yet, but they are also moving to be aimed on it. With the time there will be more data in the initial sample, and including this companies will allow to review larger sample.

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