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Environmental effect on mergers and acquisitions efficiency in the telecommunications industry

Fedorova E.A. Financial University under Government of Russian Federation, Moscow, Russian Federation ( ecolena@mail.ru )

Medvedeva A.A. Financial University under Government of Russian Federation, Moscow, Russian Federation ( bzzz93@mail.ru )

Fedorov F.Yu. Moscow State Technical University of Radio Engineering, Electronics and Automation (MSTU MIREA), Moscow, Russian Federation ( fedorovfedor92@mail.ru )

Journal: Economic Analysis: Theory and Practice, #1, 2016

Importance The article addresses development processes of the Russian M&A market in the telecommunications sector, and the effect of macroeconomic factors on efficiency of M&A transactions.
Objectives The aim is to analyze the impact of macroeconomic factors on mergers and acquisitions efficiency in the telecommunications industry.
Methods The study draws on the analysis of transactions in the Russian telecommunications industry over the period from January 2005 to March 2015. The analysis identified factors of the telecommunications sector and the entire economy, which influence the transactions. We calculated efficiency of transactions using the index of cumulative abnormal return. The analysis is based on econometric models of linear regression, probit regression; the volatility is calculated by using the GARCH model.
Results The analysis leads to the conclusion about the influence of macroeconomic factors on transactions’ efficiency. There is a weak correlation between the yield of the transaction and the condition of financial markets and entire economy, while the level of industry development has a direct impact on the deal.
Conclusions and Relevance The telecommunications sector has become one of main sectors of M&A development. Over the recent year, Russia has been experiencing economic turmoils, and enters a crisis phase. Companies are looking for the most efficient ways of development, therefore, they need to competently analyze deals in the M&A market.


The method of neural network forecasting of box-office grosses of movies

Yasnitskii L.N. Perm State National Research University, Perm, Russian Federation ( yasn@psu.ru )

Beloborodova N.O. Higher School of Economics, Perm, Russian Federation ( natasha09.12@mail.ru )

Medvedeva E.Yu. Higher School of Economics, Perm, Russian Federation ( win.mail.ru95@inbox.ru )

Journal: Financial Analytics: Science and Experience, #4, 2017

Importance The article focuses on the neural network forecasting in the film-making industry.
Objectives The article examines what opportunities economic and mathematical modeling provides to forecast revenue and profit from coming movie distribution and identifies factors that determine whether film-making business becomes a commercial success.
Methods The economic and mathematical model relies upon the neural network trained with available historical data on movie distribution and including 20 input parameters. Computer experiments were performed with the ‘freezing’ method. We used the neural network for computations if any of input data changes, meanwhile the rest of them remain the same.
Results Root-mean-square relative error of the model accounted for 13.8%, with the determination criterion being 0.86%. We refer to The Da Vinci Code, Star Wars to demonstrate what the model is capable of.
Conclusions and Relevance Virtual increase in the film budget influences projections of box-office grosses and revenue differently. Other aspects of films also have an effect on the success of film-making business. Having conducted computer experiments, we provide our recommendations, which could boost box-office grosses of films. The proposed economic and mathematical model can be used to optimize financial costs and choose parameters to plan new films to come. The model allows for forecast of box-office grosses and profit from film-making, and examine how various aspects influence the commercial result of film-making.


The Method for Forecasting Box-Office Grosses of Movies with Neural Network

Yasnitskii L.N. Perm State National Research University, Perm, Russian Federation ( yasn@psu.ru )

Beloborodova N.O. Higher School of Economics, Perm, Russian Federation ( natasha09.12@mail.ru )

Medvedeva E.Yu. Higher School of Economics, Perm, Russian Federation ( win.mail.ru95@inbox.ru )

Journal: Digest Finance, #3, 2017

Importance The article focuses on the neural network forecasting in the film-making industry.
Objectives The article examines what opportunities economic and mathematical modeling provides to forecast revenue and profit from coming movie distribution and identifies factors that determine whether film-making business becomes a commercial success.
Methods The economic and mathematical model relies upon the neural network trained with available historical data on movie distribution and including 20 input parameters. Computer experiments were performed with the ‘freezing’ method. We used the neural network for computations if any of input data changes, meanwhile the rest of them remain the same.
Results Root-mean-square relative error of the model accounted for 13.8 percent, with the coefficient of determination being 0.86 percent. We refer to The Da Vinci Code, Star Wars to demonstrate what the model is capable of.
Conclusions and Relevance A virtual increase in the film budget influences projections of box-office grosses and revenue differently. Other aspects of films also have an effect on the film-making success. Having conducted computer experiments, we provided our recommendations, which could boost box-office grosses of films. The proposed economic and mathematical model can be used to optimize financial costs and choose parameters to plan new films to come. The model allows for forecasting box-office grosses and profit from film-making, and examines how various aspects influence the commercial result of film-making.


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