According to most analysts, the era of extensive growth in the telecommunications market has almost fi nished. The ongoing competition between leading telecommunications companies is bringing the problem of developing a rational telecommunications policy to the forefront.The ever-changing telecommunications market, subscribers' preferences, the expanding variety of services, the need for updating user data, the inadequate effi ciency of the existing systems to form exact subscriber defi nitions demonstrate the need for more fl exible tariff methods and policy. In spite of Russian and foreign scientists taking into consideration the pricing problems in forming tariff plans, the main accent is placed on price formation according to the profi ts either of the whole telecommunications fi eld or company expenses in most attempts. The problem of diff erentiation of tariff plan characteristics with the purpose of subscribers' preference calculations has not been suffi ciently explored. Moreover, the structural problems of tariff plans, where phone subscribers' preferences should be taken into consideration, and the whole tariff policy, in which formation of the entire complex of existent and prospective tariff plans should be taken into consideration, have not been properly researched. For solving these problems, we have off ered a model of forming telecommunications company tariff policy using methods of intellectual data analysis and taking into consideration discovered preferences of subscribers and investors.
Valuating the position of a controlled object using indicators which are management and control tools is widely used in many areas of the economy. Usually such indicators are based on internal data, however, as the volume of available open information grows, algorithms for valuation of the position of certain control objects and on open structured data are appearing. The disadvantage of these models is their narrow specialization and binding only to structured, and sometimes strictly official data, which, as a rule, have a rare publication frequency. This does not allow you to track the change in the position of the object at different times. The authors have proposed a concept for constructing a universal complex indicator (UCI) for express valuation of the position of a controlled object in various types of activity: banking, educational, industrial, etc. Another difference in the construction of the UCI is that the concept presented in the article assumes, as a reference point, to take into account the requirements of regulatory authorities, while in most Russian and foreign studies, indicators are built for the needs of investors. It is also proposed to use, along with structured and unstructured data, tracking the dynamics of changes in the position of the control object. To determine the UCI values on the basis of various econometric models and methods, the components that characterize the requirements of the control bodies to the control object are calculated; using them the UCI value is determined from the truth table. The concept proposed was tested to build an express valuation of the financial position of 108 banks for the period from 1 January 2018 to 1 February 2020. In accordance with the requirements of the Central Bank of the Russian Federation, the values of the three UCI components were obtained, and the value was calculated for each bank. The predictive ability of the constructed model, tested on three banks of the test sample, was confirmed by the consistency of the express valuation with their actual position in March 2020.
Nowadays enterprises operate in a rapidly changing macroeconomic environment, and this factor should be taken into account when forecasting a company's fi nancial statement as a whole, or some of its particular aspects. However, development of the company's fi nancial stability assessment model taking into account macroeconomic factors is hampered by the problem of inclusion in the model of some factors with frequency of measurement diff erent from that of the internal fi nancial performance. For example, currency rates and crude oil prices can change on a daily, weekly, or monthly basis. Changes in the key interest rate cannot be characterized as systematic, since the Central Bank can vary the key interest rate depending on market conditions. Meanwhile, fi nancial indicators of the company are published in the semi-annual and annual reports. This paper proposes an approach that aggregates macroeconomic factors, which means presenting a time series of each variable for each year, followed by the inclusion of polynomial coeffi cients in the fi nal model as reference variable characteristics. The weighted average is calculated for the key interest rate, where the weights are the days during which the rate is operated. Based on the data of 291 metallurgical industry enterprises of the Volga federal district for the period 2012-2014, a fi nancial stability assessment model has been built relying on the decision tree model using CRT (Classifi cation and Regression Tree). The accuracy of the model is approximately 86%. The decision tree structure has served as a basis for recommendations to optimize certain fi nancial indicators of operations to reach fi nancial stability.
Stress testing as an instrument of risk evaluation is actively used in many international organizations, as well as by central banks in many countries. Some organizations (including the Bank of Russia) when conducting stress testing do not publish results of the tests, though they are interesting for the business community. They are reticent so to avoid causing panic on markets which could lead to a massive outflow of deposits from the banking sector as a whole or from some individual banks in particular. As a rule, stress testing is conducted relying on huge number of unpublished reporting forms, but the business community has no access to them. Only four reporting forms are presented on the Bank of Russia's website. In this paper we propose a simplified algorithm of credit risk stress testing of a banking cluster based on the four officially published reporting forms. The algorithm provides modelling of median values of banking variables depending on macroeconomic indicators, and subsequent retranslation of the received values for assessing the financial position of each bank included in the cluster. It is assumed that growth rates of banking indicators obtained from the econometrics models relying on median values are the same for each bank in the cluster. As of 1 January 2018, credit risk stress testing was conducted for 26 banks, nine of which are system-significant credit institutions. Within the stress testing, eight econometric time series models were developed. As a result, it was discovered that 11 out of 26 banks in the cluster will face certain difficulties regarding statutory requirements related to capital ratios or buffers.
The solution of the housing problem for many decades has been and remains one of the most important tasks facing the nation. The problem of modeling the value of residential properties is becoming more and more urgent, since a high-quality forecast makes it possible to reduce risks, both for government bodies and for realtors specializing in the purchase and sale of residential properties, as well as for ordinary citizens who buy or sell apartments. Predictive models allow us to get an adequate assessment of both the current and future situation on the residential property market, to identify trends in the cost of housing and the factors influencing these changes. This involves both the qualitative characteristics of the particular property, and the general condition and the dynamics of the real estate market. Russia is characterized by significant differences in the level of development of regions, therefore, by differences in trends of supply and demand prices for real estate. Valuation of residential properties at the regional level is particularly important, since all of the above determines the social and economic importance of this problem. This article presents a comprehensive model for estimating the value of residential properties in the secondary housing market of Moscow using decision tree methods and ordinal logistic regression. A predictive model of the level of housing comfort was built using the CRT decision tree method. The results of this forecast are used as input information for an ordinal logistic regression model for estimating the value of residential properties in the secondary market of Moscow. Testing the model on real data showed the high predictive ability of the model we generated.
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