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According to the classical theory of banking development and traditional idea of practical matters of bank management, lending is one of the main directions in banking. It is connected with that realization of credit transactions should be considered as a defining component of bank management. According to this defining component each separate bank will organize loan of resources and their subsequent arrangement on proper conditions and on proper risk. Hence, taking into account the retrospective review, the work analyzes the fluctuations of rating values of the bank lending efficiency in the real sector of the economy. The analysis is carried out on the example of the banks of Ukraine within the period of 2011 to 2014 years. For the purpose of examining the values of bank lending efficiency in the real sector of Ukraine’s economy, the article considers the efficiency stochastic boundary model. There were analyzed in detail the forms of distribution by particular efficiency value levels of bank lending in the real sector of Ukraine’s economy, which were obtained for 16 different time dates from the interval analyzed. There was made the conclusion about necessity of balanced use of real resources of banks for growth of crediting efficiency.

One of the conditions for stable and dynamic development of the real sector of the economy is the availability of financial resources from the different sources of their attraction. The importance of fulfillment of such condition is determined by the necessity of manufacture modernization and also by the opportunity for full-scale realization of innovative and investment projects acceptable for this kind of manufacture.

As the examples of such studies there can be noticed the works of Mario Quagliariello [

By analyzing the questions of financial resources attraction for the needs of different subjects of management in the real sector of economy, it is important to take into account the fact that among the main sources of such an attraction can be: banking system resources, stock exchange resources and also assets of potential investors that can be in their turn, as a rule, placed either in the stock market or in the banking system. So, in the system of modern economic interrelations the main sources of lending for different subjects of management in the real sector of the economy are resources of the banking sector of the economy and resources of the stock market. The key role of banking sector of the economy will consist in forming and maintenance of necessary conditions for free redistribution of financial resources among various subjects of managing for the purpose of their sufficing (subjects of managing) in need of such resources. Thus such necessity consists in security of steady and stable functioning of all subjects of the managing which is taking part in such redistribution of financial resources and their further forward development.

At the same time, using the materials of studies, presented in works of Graham Smith [

The topicality of observing the chosen direction of the research is also determined by the fact that bank lending is carried out not only at the particular bank’s own risk, but also by the influence of different factors, in the surrounding of which both the bank and the potential borrower of bank resources are functioning, which leads to necessity of observing different credit questions as some valuations of such a process. Especially study of such questions is sharpened in the periods of unstable economic development, manifestation of economic and financial crises influence consequences, reformation of the existing system of economic interaction between different subjects of market interaction.

At the same time it is important to emphasize that at this time period Ukraine tends to go through instability of economic development, which influences the processes of bank lending. So, for example, at the present time in the practice of bank lending in Ukraine there are critical sharp questions of direct stimulation of the real sector of the economy and maintaining of the demand on credit resources from the population for revitalizing the domestic market of consumption, which directly determines the mechanisms of maintenance for manufacturers. So, determining, discovering and generalizing of any possible valuables of bank lending are an important practical task not only from the sphere of banking activity, but economic development on the whole, which allows using such valuables and also studying credit efficiency of the real sector of the economy.

Assessment of bank lending efficiency can be made by studying a set of banking activity indicators using different approaches for obtaining such valuables. Especially among the indicators, used for study of bank lending efficiency valuables, one can pick out (see for example works by A. Sinan Cebenoyan and Philip E. Strahan [

The value of the effective credit rate, which depicts the real relative income, obtained on the whole within a year;

The net resulted income, which generalizes absolute meanings of the result obtained from bank credit activity;

The domestic norm of profitability, reflecting debit percentage rate, according to which loans are viable and many others.

At the same time it’s worth mentioning, that the most wide spread approach to assessment of bank lending efficiency commonly used lately, are methods, operating with the concept technical efficiency―efficiency, which is according to Dennis J. Aigner, Knox C. A. Lovell and Peter Schmidt [

For the purpose of uncovering technical efficiency in the field of banking activity analysis one constructs the so called efficiency border, which is typical for the methodology of stochastic boundaries analysis. The essence of such a methodology, according to studies of Aigner, Lovell and Schmidt [

Constructing the efficiency boundaries of the process or the phenomenon under research using the methods of statistic analysis in the form of some regressive dependence between the variables, chosen for such an analysis;

Positioning the process, phenomenon or object under investigation relative to the efficiency boundary obtained;

Evaluating the efficiency rating of the process, phenomenon or object under investigation using the methods of statistic analysis in the form of a certain regressive dependence between the variables chosen for such an analysis;

Positioning of the process, phenomenon or object under investigation with regard to the efficiency boundary obtained;

Evaluating the efficiency rating of the subject matter under study in the form of a function, characterizing the attainability of the efficiency boundary constructed, which, according to research of James Jondrow et al. [

in which

where y is a vector of the results under research, x is a vector of resources, used for obtaining results under research, f is a function of the efficiency boundary under research. C is a vector of function f parameters,

As the analysis literature sources shows [

So, to carry out the further analysis, first of all, it’s necessary to study the model of efficiency stochastic boundary of functioning banks under study. To build a model of such efficiency boundary we will proceed from the reasonability of intermediary approach usage for description of bank activity on the basis of study of the asset approach. Also to build an appropriate model we will base on the results of prior studies of other authors, where the similar model of banking activity analysis was used. In particular as a similar model of stochastic boundary of bank lending efficiency in the real sector of the economy, the model used in the work of Oleg Vasyurenko, Vyacheslav Lyashenko and Valeriia Podchesova [

Then the similar model of the efficiency stochastic boundary for assessment of lending efficiency in the real sector of the economy can be generalized as following:

where

On the whole the chosen variables of the presented above model corresponds totally the variables of the model of the banking activity description in accordance with the intermediary approach, based on the asset approach.

For further realization of the model of the efficiency stochastic boundary for the purpose of obtaining the corresponding ratings of bank lending efficiency in the real sector of the economy in Ukraine there were studied the indicators of different Ukraine’s banks activity that were taken from the official site of the National Bank of Ukraine―bank.gov.ua. This research covers the period from 2011 to 2014 taking into account their quarterly distribution. In other words, we study a quarterly bank lending efficiency in the real sector of Ukraine’s economy within 2011-2014 years. Such a selection of data allows to analyze the dynamics of changes (in some aspect so called retrospective) of bank lending efficiency based on 16 separate time periods that were chosen for the research. At that, in every time period a different number of banks are used (see in the

The general dynamics of bank lending in the real sector of the economy of all Ukraine’s banks in the investigated periods of time presented in the

The general dynamics of funds of other banks in the investigated periods of time presented in the

Тable 1. Parameters and statistic values of calculations results for the presented model of the efficiency stochastic boundary.

(Source: National Bank of Ukraine).

The general dynamics of volume of the funds attracted in the form of deposits from natural and legal persons in the investigated periods of time presented in the

The general dynamics of volume of administrative and other costs in the investigated periods of time presented in the

To determine the parameters of the efficiency boundary models in each selected periods of time and to calculate the values of bank lending efficiency in the real sector of the economy CEPA’s (Centre for Efficiency and Pro- ductivity Analysis) software was used―the programme FRONTIER 4., which is free and has an open access.

The results of calculations of programme FRONTIER 4.1 for determining parameters of the investigated efficiency stochastic boundary model on the basis of selected data are presented in

In particular,

a value of full dispersion inaccuracy

a value of inefficient constituent share

a ratio of a logarithm function of maximal likelihood (LR) for certain periods of time;

a number of banks under study in certain periods of time.

First of all, the analysis of data in

At the same time, data in _{3} coefficients with such a parameter of the investigated model as

At the same time it should be noticed, that an inessential statistic dependence of values C_{3} coefficients is observed in the periods of considerable excess of rate of growth of administrative and other operational costs in comparison with rates of growth of lending volumes in the real sector of the economy. In other words, the costs of the real resources of the banks didn’t correspond to the rate of lending volumes alteration. By the way, in these periods of time you can observe the decrease of values C_{3} of coefficients, which is an objective evidence of decrease of the influence of administrative and other operational costs on the dynamics of bank lending volumes in the real sector of the economy. So, it can be concluded, that a part of a share of an inefficient constituent in the processes of transformation of borrowed funds into lending resources in respect to Ukraine’s banks functioning is determined by the irrelevant tendencies in changes of volumes of administrative and other costs of the banks to the real tendencies of changes of lending volumes in the real sector of the economy.

At the same time, reasoning from the data of _{2} coefficients, it should be noticed a considerable influence of the volumes of the attracted funds in the form of physical and legal persons’ deposits on the dynamics of bank lending volumes in the real sector of the economy, which is explicable fact from the objective point of view. At that, the dynamics of values of C_{1} coefficients can be the evidence of a considerable variability of the influence of other banks’ resources, attracted by the means of the interbanking lending market, on the dynamics of bank lending volumes of the real sector of the economy. Nevertheless, this fact has quite a logical explanation, which is based on the fact, that resources of other banks are mainly used for a maintaining of a proper liquidity rate with the purpose of meeting the requirements of the bank in necessary resources.

Another result of the programme product FRONTIER 4.1 is a direct calculation of efficiency ratings―in this case the ratings of bank lending efficiency in the real sector of Ukraine’s economy. Like a calculation of parameters of the model of efficiency stochastic boundary, the calculation of the ratings of bank lending efficiency in the real sector of Ukraine’s economy is performed for particular quarters from the investigated period from 2011 to 2014 years. The generalized results of such calculations are presented in

Substantial changes in the distribution levels of investigated bank lending efficiency ratings took place in

2011. Here, as it can be seen from the data of

So, according to the results:

the first quarter of 2012 a number of banks with the rating of bank lending efficiency in the real sector of the economy decreases by 0.6 in comparison with the data of 2011,

the second quarter of 2012―the prevailing is the efficiency rating at the level 0.4,

the third quarter of 2012 the efficiency rating at the level 0.6 prevails slightly in comparison with the efficiency ratings at levels 0.4 and 0.5,

the fourth quarter―the prevailing is the efficiency rating at the level 0.5.

Substantial changes in the distribution levels of investigated bank lending efficiency ratings took place and in 2013 and in 2014 (

The dynamics of the average rating of the bank lending efficiency of the real sector of Ukraine’s economy presented in the

First of all, on the assumption of the data of

If we compare the dynamics of changes of average rating values of bank lending efficiency in the real sector of economy with the dynamics of volumes of bank lending of the real sector of economy for all the Ukraine’s banks, it should be noticed the presence of a particular lag in such dependences. Manifesting such lag dependence of the investigated values is the inertness of reaction of bank lending processes, which shows itself in corresponding lending volumes, on a change of levels of such lending efficiency. The explanation of such a fact is a general inertness of bank system reaction, which is a complex polyhierarchic, but dynamic mechanism of modern market relations.

Thus, the considered model of stochastic boundary of efficiency of crediting can be used for derivation of aver-

age estimations of efficiency of bank crediting of real sector of economy in the context of separate banking groups. Especially, use of the offered model of stochastic boundary of efficiency of crediting allows drawing a conclusion about decrease of estimation of efficiency of bank crediting of real sector of economy in Ukraine for investigated period of time. In the end it doesn’t contribute to substantial growth of the dynamics of rating values of bank lending efficiency in the real sector of Ukraine’s economy.

One of the factors that influence on the decrease of the efficiency of the process of transformation of attracted resources into lending resources, and consequently bank lending efficiency, is inconsistent with high rates of changes of volumes of administrative and other operational costs of the investigated banks as compared with the dynamics of bank lending volumes in the real sector of the economy.

Also no less important problem is the question of increase of efficiency of processes of bank crediting of real sector of economy which is in a plane of adequacy and equation of use of real resources of banks.

So, the key issue of increase of efficiency of processes of bank lending efficiency in the real sector of Ukraine’s economy is a matter of adequate usage of the real resources of banks.

Mohammad AyazAhmad,Grigorii P.Kots,Vyacheslav V.Lyashenko, (2015) Bank Lending Efficiency in the Real Sector of the Economy of Ukraine within the Period of 2011 to 2014 Years. Modern Economy,06,1209-1218. doi: 10.4236/me.2015.612114