The contemporary marketing practices methodology (CMP) attracts attention of a substantial number of researchers in the field of strategic marketing. In the past two decades there were more than fifty papers published in peer-reviewed outlets addressing the analytics of usage of contemporary marketing practices in a variety of countries and industries. In this note we discuss reliability of these studies with respect to the usage of specific analytic tools. First, we demonstrate that standard clustering analysis is relatively sensitive to small changes in the datasets with companies being assigned to different clusters at frequent rates. Second, the project national teams make use of different, often incompatible settings. Therefore, to make possible comparisons between the countries and across industries, the researchers must agree on a generic setup and procedures. We conclude the note sketching the basics of these common grounds.
We present a complex analysis of business models for large, medium and small Russian commercial banks from 2006 to 2009. The Russian banks are grouped based on homogeneity criteria of their financial and operational outcomes. The banks’ structure of assets and liabilities, profitability and liquidity ratio are taken into account. The results show how the banks are adjusted their business models before and after the financial turmoil taken place in 2008. In addition, the prevailing banking business models observed for the leading banks in Russia are defined. The banks often changing their business models are found and analyzed.
In article possible approaches to clustering of large city schools of the Russian Federation by results of their educational activity are studied. The extent to which a school has entered a particular cluster is determined by a number of objective conditions in which schools operate. Significant indicators of conditions affecting the EGE-results in schools were identified. It is shown that the studied indexes of the working conditions of schools are not sufficient for the correct clustering of schools according to the aggregated EGE-indicators.
We present the basic properties of the a new pattern analysis method in the system of parallel coordinates; results of the method do not depend on the ordering of data in the original sample of objects being analyzed. We prove that clusters obtained with this method do not overlap. We also show the possibility of representing objects of one cluster in the form of monotonically increasing/decreasing functions.
The work continues the research of constructing methods for analyzing patterns in parallel coordinates independent of the sequence of input data of the results. The basic operations on objects of ordinal-invariant pattern clusters are described. The assertion that the centroid of an ordinal-invariant pattern cluster belongs to the original cluster is proved, which allows one to estimate the intracluster object - centroid distances in the multidimensional feature space. Examples of revealing the structural similarity of objects in parallel coordinates are given. The main differences between the methods of analysis of patterns and cluster analysis are noted. The methodology of the centroid detection of the ordinal-invariant pattern- cluster is described. An algorithm for combining groups of objects based on their structural similarity, on the one hand, and minimizing intracluster distances, on the other, is proposed, which makes it possible to improve the accuracy of the final results and partially solve the problem of finding similar objects in the presence of error in the original data. The proposed algorithm uses the concept of intracluster distances “object - centroid” and satisfies the following conditions: endogenous determination of the number and composition of the desired groups of objects under study; low (relatively) computational complexity; independence of the original partition from the initial sequence of input data. The work of the proposed algorithm on classical data sets is demonstrated. The results of testing are presented and the clustering accuracy is increased.
M&A transactions are realized in various economy sectors of the most developed countries. The major motive for M&A is to obtain synergy effects. That is why it is especially importantfor a company that realizes M&A to estimate future deal outcome correctly. In this study, the analysis of synergy effects in M&A transactions is considered from the point of deal efficiency in the example of food industry because of its strategic importance for any state.
The work consists of three main parts. The first chapter examines the theoretical aspects of M&A transactions. In the second chapter the main concepts and methodologies are presented for valuing synergy effects from the point of deal efficiency. The last chapter is devoted to solving practical problems of the study.
During the study of valuation concepts presented in the second chapter, two methods were chosen for further consideration: the method described in the article written by Kemenov A. V. and Chemerkin M. A. and the DCF method. In this paper, two hypotheses are put forward: 1). the efficiency of the transaction, calculated by the method proposed in the work of Kemenov A. V. and Chemerkin M. A., is an indicator of future synergy effects; 2). there are interrelations between the obtained deal efficiency of the transaction and various pre-deal financial and non-financial indicators of companies.
Thus, the result of this study is the revealed interrelations between the deal efficiency and companies’ pre-deal performance indicators by the cluster analysis method, as well as the valuation ofthe synergy effect by comparing the enterprise value of the merged company after the transaction with companies’enterprise values prior the transaction using theDCF approach on the example of a specific case, chosen on the obtained clustering results and then comparing the results of the two methods.