Факторы принятия инвестиционных решений в условиях неопределенности: пример российских компаний
The article discovers results of empirical estimators of uncertainty, rational and behavioral factors influence on investment policy (capital investment) of Russian companies. On the base of theoretical models of Sandmo, Bo & Sterken it was made an assessment using Ordinary Least Squares method, models considering data panel structure, Generalized Method of Moments on a sample of Russian companies.
State Capitalism could be characterized by a triple role of the state: the state performs as a “programmer” to guide economic activity; it acts as a “protector” to safeguard national economic interests; and it also plays the role as a “producer” to create national wealth through its state-owned enterprises (SOEs). However, the influences of State Capitalism in a country are not only limited to the domestic sphere. They often extend internationally, either through the globalization of SOEs, or through Sovereign Fund investments, or by means of other influences. Many recent acquisition projects by SOEs, often in strategic sectors, highlight the importance of understanding this new geopolitical investment which has created special relations between State Capitalism and the free market. They also raise the question of the need for updating national economic security concerns in the context of globalization. As the value of Sovereign Funds reaches several trillion dollars, the controversy surrounding these Funds is evolving. For many, these Funds do not necessarily always look for maximizing business performance, but are sometimes also accompanied by political and strategic ambitions of the respective states from where they originate. The phenomenon of State Capitalism has gained prominence in recent years especially in several emerging markets. It appeared, firstly, because of multiple government interventions in the economy,and secondly, emphasis given to the globalization of their SOEs / economic organizations in international markets (China, Russia, Brazil, Malaysia, Saudi Arabia, India, Korea, etc.). In January 2012, The Economist published another special article on State Capitalism and wondered if the new balance of power that is being built-up with the emergence of market oriented SOEs will pose a challenge to the liberal capitalist model. The objectives of this conference are manifold: to examine the characteristics of State Capitalism in the world economy, especially in emerging countries, to assess its real impact on economic development, to identify its scope to other developing countries, and also to explore the major challenges that it poses to the liberal capitalist model in the world of free-markets.
This conference proceeding includes selected full papers from the 11th EBES Conference – Ekaterinburg. We have accepted papers among resubmitted full papers after the conference ended. In this proceeding you will find a snapshot of topics that are presented in the conference. As expected, our conference has been an intellectual hub for academic discussion for our colleagues in the areas of economics, finance, and business. Participants found an excellent opportunity for presenting new research, exchanging information and discussing current issues. We believe that this conference proceeding and our future conferences will improve further the development of knowledge in our fields.
In the article we study the reasons and character of economic growth in Russia in the beginning of the XXI-st century. The analysis of the features of economic development is a key to understanding of depth of modern crisis in Russia. This article exhibits institutional preconditions for an overcoming the crisis and acceleration of economic growth.
Supply chain management is rather new scientific field that reflects the concept of integrated business planning. This concept should be experts and practitioners in logistics and strategic management. Today, integrated planning to become a reality thanks to the development of information technology and computer technology. At the same time to achieve a competitive advantage is not enough high-speed, low-cost data transfer process. In order to effectively apply information technology tools necessary to develop a quantitative analysis of the effectiveness of supply chain management. The mam element of this tool are optimization models that reveal the complex interactions, the wave and the synergies that arise in supply chain management. In this article we consider one of the classes of such models - the so-called dynamic models of conveyor systems, processing of applications.
The paper consists of three main sections. The first is devoted to a discussion of the "state capitalism" concept and the reasons for the growing interest to this phenomenon. It is proposed here to consider the state capitalism not only in terms of the state ownership in major national industrial enterprises and banks, but also taking into account the efficiency of SOEs. In the second section, the new data on the state involvement in the Russian economy are represented, including the shares of the state in the authorized capital of the largest industrial enterprises and banks. Their economic indicators are compared. Contrary to some assumptions P / E values for national champions are lagging behind the average for emerging markets. The third section examines the hypothesis that one of the major challenges faced by the state capitalism is the development of investment incentives for SOEs and their performance. It is shown that the interests of the state as an owner of business enterprises are often in conflict with the interests of the state as a social institution. A number of examples are demonstrated. In order to solve this problem the state should reduce its stakes in SOEs except for those that are of strategic importance. The output of the analysis is that the state capitalism as a social phenomenon has no a long-term perspective. Most of so called “state capitalist” countries will take in future the path of traditional mixed market economy.
Uncertainty is a concept associated with data acquisition and analysis, usually appearing in the form of noise or measure error, often due to some technological constraint. In supervised learning, uncertainty affects classification accuracy and yields low quality solutions. For this reason, it is essential to develop machine learning algorithms able to handle efficiently data with imprecision. In this paper we study this problem from a robust optimization perspective. We consider a supervised learning algorithm based on generalized eigenvalues and we provide a robust counterpart formulation and solution in case of ellipsoidal uncertainty sets. We demonstrate the performance of the proposed robust scheme on artificial and benchmark datasets from University of California Irvine (UCI) machine learning repository and we compare results against a robust implementation of Support Vector Machines.