Economic cybernetics [Text]: int. sci. journal / Donetsk national university; [head of ed.board Yu.G.Lysenko]. - Donetsk: DonNU, 2013. - №4-6(82-84) - 95 p.

**The distribution of the characteristics of the maximum expected utility portfolio based on VaR: the impact of investor's risk aversion coefficient**
**Zabolotskyy T.N., Vitlinskyy V.V.**
**Purpose and subject of research**
This study investigates the problem of rational choice

of portfolio structure using the expected utility based on Value-at-Risk.

**Research methodology**
The study used formal mathematical methods, the method

of economic and mathematical modeling, methods of portfolio construction, analytical methods of research.

**Value results**
We discuss the correctness of sample estimator of

rational structure weights. Specifically, we define densities of the sample

estimators for main portfolio characteristics and elaborate how the densities

depend on investors' risk aversion coefficient. Our empirical tests allow us to

give some recommendations about the rational choice of the coefficient based on

the data from PFTS Ukrainian stock exchange market. It is shown that the high

values (larger than 4) of this coefficient should be avoided as well as the low

values (in the neighborhood of one).

**Conclusions**
The paper examines properties of portfolio

characteristics with maximum expected utility based on Value-at-Risk. The use

of this risk measure in portfolio theory is fully consistent with

recommendations of the main banking documents. From the theoretical point of

view application of expected utility function for portfolio constructing is a

generalization of the portfolio constructing problem with minimum risk and given

level of portfolio return.

**Key words:** portfolio construction, Value-at-Risk, rational structure

weights, portfolio characteristics.

**References:**
1. Markowitz H. Portfolio selection / H. Markowitz //

Journal of finance. – 1952. – №7. – P. 77 – 91.

2. Merton R. C. An analytical derivation of the

efficient frontier / R. C. Merton // Journal of financial and quantitative

analysis – 1972. – №7. – P. 1851 – 1872.

3. Okhrin Y. Distributional properties of optimal

portfolio weights / Y. Okhrin, W. Schmid // Journal of econometrics. – 2006. –

№134. – P. 235-256.

4. Basel committee on banking supervision //

Operational risk consultative document, supporting document to the New Basel

Capital Accord. – January 2001. – 30 p.

5. Duffie D. An overview of Value-at-Risk / D. Duffie,

J. Pan // Journal of derivatives. – 1997. – Vol. 4, № 3 – P. 7-49.

6. Jorion P. Value at Risk: the new benchmark for

managing financial risk / P. Jorion. – New York: McGraw-Hill Professional,

2002. – 544 p.

7. Kaminskyy A. B. Modelling of risk aversion using the

Value at Risk methodology / A. B. Kaminskyy // Theoretical and practical

problems of economics. – 2005. – № 6. – P. 145-154 (in ukrainian).

8. Vitlinskyy V. V. An integrated approach to applying

the Value at Risk methodology / V. V. Vitlinskyy, A.B. Kaminskyy // Economic

Cybernetics. – 2004. – № 5-6. – P. 4-14 (in ukrainian).

9. Alexander G. J. Economic implication of using a

mean-VaR model for portfolio selection: a comparison with mean-variance

analysis / G. J. Alexander, M. A. Baptista // Journal of economic dynamics

& control. – 2002. – №26. – P. 1159 – 1193.

10. Bodnar T. Minimum VaR and Minimum CVaR optimal

portfolios: estimators, confidence regions, and tests / T. Bodnar, W. Schmid,

T. Zabolotskyy // Statistics & Risk Modeling. – 2012. – №29. – P. 281-314.

11. Bliss R. R. Option-implied risk aversion estimates

/ R. R. Bliss , N. Panigirtzoglou // The journal of finance. – 2004. – №59(1).

– P. 407-446.

12. Bollerslev T. Dynamic estimation of volatility risk

premia and investor risk aversion from option-implied and realized volatilities

/ T. Bollerslev, M. Gibons, H. Zhou// Journal of econometrics. – 2011. -

№160(1). – P. 235-245.

13. Mansini R. Conditional value at risk and related

linear programming models for portfolio optimization / R. Mansini, W. Ogryczak,

M. G. Speranza // Annals of operations research. – 2007. – №152. – P. 227-256.

14. Fama E.F. Foundations of finance / E.F. Fama. – New

York: Basic Books. 1976. – 391 p.

15. Bodnar T. Econometrical analysis of the sample

efficient frontier / T. Bodnar, W. Schmid // The European journal of finance. –

2009. – №15. – P. 317-335.

**Modeling of production and logistics systems with non-identical hardware setup**

**Medvedieva M.I.**
**Purpose and subject of research**
We consider

the problems of function modeling production and logistics systems with

unreliable equipment and non-identical readjustment before servicing the next

batch of orders, based on queuing theory models. To solve this problem we

present the flexible manufacturing system in the form of a single-channel

queuing system, where the input Poisson flow intensity of orders per unit time.

**Research methodology**
The study used formal mathematical methods, the method

of economic and mathematical modeling, queuing theory, analytical methods of research.

**Value results**
Order

processing takes place in the order of their receipt, the order processing time

has exponential distribution law with parameter . The device has a feature

consisting in the fact that after the processing of all orders that are in the

system, it goes into an idle state and to move it into working condition

necessary readjustment, which begins after the date of the request to a free system.

Duration changeover has an exponential distribution with parameter . In addition, it is assumed

that the equipment in the process of its operation may fail and recover. In the

event of equipment failure, it is repaired (recovery) work. Maintenance of

equipment (recovery, prevention and readjustment) carries a team of workers.

**Conclusions**
We assume that the device can fail at any time, as during

maintenance applications (running), the application is under maintenance at the

time of failure of the device is lost. Orders that are in the system at the

time of equipment failure (if any), are served after the repair. After the

restoration is necessary to readjust the equipment, the duration of which is

different from the duration, which began after the receipt of the request to a

free system (from the initial changeover). For the described characteristics of

the system are found, which are used to find the basic operating parameters of

the system used to construct economical criteria.

**Key words:** queuing system, the stationary probability, reliability,

prevention, rehabilitation and readjustment of equipment.

1. Radio-Electronics-Television

Manufacturers Association, 1955, Electronic. Applications Reliability Review,

v. 3, № 1, May, p.18.

2. Гнеденко

Б.В., Беляев Ю.К., Соловьев А.Д. Математические методы в теории надежности. –

М.: Наука, 1965. – 524 с.

3. Румянцев

Н.В. Моделирование гибких производственно-логистических систем. – Донецк:

ДонНУ, 2004. – 235 с.

4. Новиков О.А.

Петухов С.И. Прикладные вопросы теории массового обслуживания. – М.: «Советское

радио», 1969. – 400 с.

**Approximate analytical solution of the inflasion kinetic model**** **
**Vitlinskyi**** ****V.V., Koliada Y.V., Perten S.I. **
**Purpose and subject of research**
The aim of the article is to highlight the mathematical formalization

basic principles of the approximate inflation kinetic models solutions, the

exact solutions of which are obtained only by numerical integration.

**Research methodology**
The basis in achieving this goal is the detailed analysis of the

numerical solutions integral curves, which allowed to identify and formulate

changes in the behavior of numerical solution, and caused the changes in the

model parameters values.

**Value results**
An approach described in the article expands the range of possibilities

in the modeling of inflation processes, namely: the prediction of their quantitative

level for particular conditions; the creation of alternative development

scenarios; the definition of anti-inflation measures necessary to achieve the

optimal rate of inflation for the economy. Approximate solutions in the

analytical form based on exponential functions are offered. The estimation

error of the abovementioned solutions is implemented. The formulas of the peak

significance computation of the inflationary surge, which took place

in the result of money supply increase, are obtained.

**Conclusions**
The approximate analytical solutions of the model are made, that

facilitates the transition to the next quality level of the inflation processes

development within the inflation kinetic model.

**Key words:** kinetic

models, modeling of inflation processes, money supply increase, approximate

analytical solution.

**References**
1. Vasylieva, A.T., Hostynets,

V.S. (2009), “Pro znakhodzhennia koefitsiientiv kinetychnoi modeli infliatsii”

[On the coefficients finding of the inflation kinetic model], Economic bulletin

of National technical university of Ukraine «Kyiv polytechnical institute»,

no.6, pp.417-421.

2. Vitlinskyi, V.V., Koliada,

Yu.V., Kravchenko, T.V. and Trokhanovskyi, V.I. (2013), Adaptyvni modeli v

ekonomitsi [Adaptive models in economics], pain [electronic resource], Kyiv

National Economic University named after Vadym Getman, Ukraine.

3. Hordieiev, H.H. (2012),

“Investigation of nonlinear models of economic dynamics”, Zovnishnia torhivlia:

ekonomika, finansy, pravo [Foreign trade: business, finance, law], no. 2, pp.

133-139.

4. Koliada, Yu.V., Perten,

S.I. (2011), “Mathematical modeling of inflation in Ukraine”, Economic

cybernetics, no.1-3, pp.16-25.

5. Koliada, Yu.V., Perten,

S.I. (2007), “The synergetic effect of the inflationary process”, Proc. Int. Sch.

- Symp. “Analysis, modeling, management, development of economic systems”, DEN,

Simferopol, pp. 93-99.

6. Koliada, Yu.V. (2010),

“Fazovi ta parametrychni portrety typovykh matematychnykh modelei neliniinoi

ekonomichnoi dynamiky” [Phase and parametric portraits of the typical nonlinear

mathematical models of economic dynamics], Modeliuvannia ta informatsiini

systemy v ekonomitsi: Zb. nauk. Prats [Modelling and information systems in

economics], Kyiv National Economic University named after Vadym Getman, vol.

82, pp.74-90.

7. Nakoryakov, V.E., Gasenko,

V.G. (2004), “A kinetic model of inflation”, Economics and Mathematical

Methods, vol.40, no.1, pp.129-134.

8. Novozhylova, M.V., Koiuda,

P.N. and Chub, I.A. (2005) Modeliuvannia ekonomichnoi dynamiky [Modeling of

economic dynamics], Kharkiv National University of Construction and

Architecture, Ukraine.

9. Osechkina, T.A.,

Postanogova, E.E. (2012) “Mathematical model of an assessment of inflation”,

Prikladnaja matematika i mehanika [Applied mathematics and mechanics], Perm

National Research Politechnic University, pp. 148-158

10. Tabachnikov, Ya.A. (2008)

“Kineticheskaja model’ infljacii, uchityvajuwaja infljacionnye ozhidanija”

[Kinetic model of inflation, taking into account the inflation expectations], Applied

Statistics. Actuarial and Financial Mathematics, no.1-2, pp. 92–100.

11. Vytlynskyi, V.V., Kolyada,

Yu.V., Perten, S.Y. (2009), “Dynamics of the risk by means of watching economic

indexes rates”, Modeling and Analysis of Safety and Risk in Complex Systems:

Proceeding of the Ninth International Scientific School, Saint-Petersburg,

SUAI, pp. 99-104p

**Modeling of a flexible manufacturing system with**** ****changeover between two production cycle**
**Rumyantsev N.V.**
**Purpose and subject of research**
In this

paper we consider a model of a flexible manufacturing system based on the

theory of queues, which itself is flexible manufacturing system is represented

as a single-channel queuing system with changeover and loss requirements. It is

assumed that the system input Poisson flow intensity of orders per unit time.

**Research methodology**
The proposed approach to the modeling of time series is based on the

methodology of multivariate analysis and continuity equation, which relates the

probability density function of the state variables of the system with their

speeds.

**Value results**
Order processing takes place in the order of their

receipt, the order processing time has exponential distribution law with

parameter . The device has a feature consisting in the fact that after the processing

of all orders that are in the system, it goes into an idle state and to move it

into working condition necessary readjustment, which begins in a random time

having an exponential distribution with parameter , and the duration of its also has exponential distribution law, but with a

parameter . Requirements entering the

system, as the waiting time for the changeover and the changeover time for the

lost. Note that the delay in the onset of readjustment due to the fact that the

company needs to put new equipment or tooling for the continuation of the

process. In this case, you can save money because the repair team, performs

work on installing new equipment, can be outsourced.

**Conclusions**
For the described characteristics of the system are

found, which are used to find the basic operating parameters of the system,

namely, the probability of finding equipment changeovers, likelihood of a denial

of service and the average queue length.

**Key words:** queuing system, the stationary probabilities of the

states of the system, the average queue length, readjustment, loss of orders, delays in the impatient

customers.

1. Румянцев

Н.В. Гибкие логистические системы с переналадкой в начале периода занятости и

потерей требований / Н.В.Румянцев // - Науковий журнал «Бізнес Інформ», № 4,

2012. - Харків: ФОП Александрова К.М.; ВД «ИНЖЕК», 2012. – С. 25-27.

2. Румянцев

Н.В. Гибкие логистические системы с переналадкой в конце периода занятости и

потерей требований / Н.В.Румянцев// - Науковий журнал «Бізнес Інформ», № 5,

2012. - Харків: ФОП Александрова К.М.; ВД «ИНЖЕК», 2012. – С. 51-54.

3. Гнеденко

Б.В. Введение в теорию массового обслуживания / Б.В. Гнеденко, И.Н. Коваленко.–

М.: Наука, 1987. – 336 с.

**Modeling multivariate nonstationary time series of economic dynamics based on fokker-planck equation**
**Isaienko**** ****A****.****A****.,**** ****Glushchevsky**** ****V****.****V****., **** ****Isaienko**** ****A****.****N****.**
**Purpose and subject of research**
The actual problem of modeling of the multivariate nonstationary time

series of economic dynamics is being researched for the purpose of analysis,

forecasting and decision-making in financial markets.

**Research methodology**
The proposed approach to the modeling of time series is based on the

methodology of multivariate analysis and continuity equation, which relates the

probability density function of the state variables of the system with their

speeds.

**Value results**
Equation of

motion of a point in a multidimensional phase space of state variables derived

under the assumption that the evolution of the economic system based on the

interaction of two factors - the growth and dissipation. It is assumed that the

growth rate has a deterministic function, which means that there is a causal

link between variables, and the diffusion component of the velocity is

proportional to the gradient of the state probabilities in a local point of

phase space. In this case the state of the system is determined by multivariate

Fokker-Planck equation. On the basis of two-dimensional Fokker-Planck equations

is constructed model of the real economic process - trading on the stock

exchange. The structure of the model equations of nonlinear responsible

paradigm of financial markets and agreed with the results of empirical

research. We derive differential equations for the evolution of one-dimensional

distributions of prices, trading volume, and spread their moments that are

needed to complete the system for the unknown probability density functions.

**Conclusions**
The evolution equations are based on sample data and agreed with the

two-dimensional Fokker- Planck equation. Modeling the dynamics and forecasting

of trades carried out by numerical integration of the equations of evolution in

a sliding window of the sample. The proposed approach to modeling allows the

best use of the information contained in the multivariate time series and to

obtain high prediction accuracy. Verification of the model performed on the

rows indices trading on the Ukrainian stock market.

**Key words:** multivariate

time series, Fokker-Planck equation, forecasting,

stock exchange.

1. Mocherny S.V., 2000. Economic Encyclopedia: in three

volumes, V.1, 864p.

2. Heets V.M., Klebanova T.S. and Chernyak

A.I., 2005. Models and methods of social and economic forecasting, 396p.

3. Sergeeva L.N., 2003. Nonlinear Economy: model and methods, 218p.

4. Mantenya R.N., 2009. Introduction to

econophysics: Correlations and complexity in finance, 192p.

5. Maksishko N.K., 2009. Modeling of economy

by the methods of discrete nonlinear dynamics, 415p.

6. Vyazmin S.A., Kireev V.S., 2004.

Application of wavelet analysis in analyzing and forecasting the financial

markets. Economics and management, V.B., pp.69-70.

7. Prenter R.R., Frost A.J., 2001. Elliott

Wave Principle. The key to market understanding, 268p.

8. Sergeeva L.N., Maksishko N.K., 2005. Modern methods of

analyzing economic time series and building predictive models. Economic

cybernetics № 1-2 (31-32), 73-79pp.

9.

Ivakhnenko A.G., Myuller I.A., 1984. Self-organization of predictive

models, 222p.

10. Frank D., 2005. Nonlinear Fokker-Planck

equations fundamentals and applications, 407p.

11. Bosov A.D., Orlov Y.N., 2013. Empirical

Fokker-Planck equation for predicting nonstationary time series. Preprint Inst.

of Applied Mathematics Keldysh M.V. №3 p.30.

12. Glushchevsky V.V., Isaienko A.N. and

Isaienko A.A., 2011. The concept of modeling system characteristics of

financial assets. Modeling and

Information Systems in the economy,85, pp.129 - 139.

**The method of constructing functions rationing term-sets for displaying function of belonging **
**Bakurova A.V., Ivanov V.N.**
**Purpose and subject of research**
Development of the construction method of function rationing variables

therm-sets, representing their reflection on the interval [0;1] for modeling of

operational risk based on fuzzy sets. This will eliminate the impact of

different dimensions of the input variables corresponding to various factors,

operational risk, and display the membership functions of the input variables

in common co-ordinates.

**Research methodology**
The theoretical basis are the approaches of the theory of fuzzy sets and

fuzzy logic. The proposed method allows to establish the dependence of the

values of operational risk, regardless of the values of the input variable

metrics.

**Value results**
An analysis

of the work of scientists associated with the development of methods and models

of risk assessment in the process of bank activity has been given. The method

of constructing the functions of regulation to eliminate the impact of

different dimensions of input variables has been suggested, which has allowed

to display the membership functions of the input variables in common co-ordinates.

Prospects for further research consist in building an operational risk warning

system based on the algorithms of fuzzy inference and descriptions of possible

situations of its occurrence.

**Conclusions**
The scientific novelty of this work consists in the development of the

method of the construction of the functions of regulation to eliminate the

impact of different dimensions of input variables, which allows to display the

membership functions of the input variables in common co-ordinates.

**Key words:** operational risk, fuzzy sets, function of belonging.

Vitlinsky V.V., 2003. Conceptual aspects of risks in the economic sphere. Proceedings of

International Scientific School MA BR 2003 SPb, pp. 200-206.

Kaminsky

A.B., 2006. Modeling financial risks. Publishing-polygraphic centre "Kyiv

University", 304 p.

Pervozvansky

A.A. and Pervozvanskay T.N.,1994. Financial market: the calculation and risk.

Infra-Moscow, 192 p.

Sazykin

B.V., 2008. Operational risk management in commercial Bank. Moscow – Summit, 282

p.

Solozhentsev

E. D., 2011. I^3 technology for the economy. Science, 387 p.

Chernyak O.

I. and Peshko A.V., 1997. Determination of the optimal portfolio of securities

and accounting methods retrospective data. Banking, pp. 58-61.

Yastremsky

O.I., 1992. Modeling of economic risk. Lybid, 176 p.

Cruz M.G.,

2002. Modeling, Measuring and Hedging

Operational Risk. - John Wiley & Sons Ltd, - XV, 330 p.

Bakurova

A.V., 2010. Self-organization of socio - economic systems: models and methods.

Classic private University, 238 p.

Ramazanov S.

K. and Burbelo O.A, Vitlinsky V.V. and others. 2012. Risks, security, crisis

and sustainable development of economy: methodology, methods, models of

management and decision-making. Noulij, 946 p.

**Problem of Identification of Parameters of the Model of Management of Innovation Processes in Reprocessing Agricultural Enterprises**
**Babenko V.A.**
**Purpose and subject of research**
The main approaches in the identification of parameters of economic and

mathematical models are considered, the problem of a posteriori identification

of the dynamical model parameters of innovative process management of

reprocessing agricultural enterprises is researched.

**Research methodology**
Solutions for a posteriori identification of parameters of the dynamic

model of innovative process management of reprocessing agricultural enterprises

propose an algorithm that reduces to the implementation of solutions of systems

of linear algebraic equations, the formation of the linear regression equation

and use the method of least squares.

**Value results**
An algorithm

for solving the problem of a posteriori identification of parameters of the

dynamic model of innovative process management of reprocessing agricultural

enterprises proposed algorithm makes it possible to develop efficient numerical

procedures to implement a computer simulation of the dynamics of the system of

innovative process management of agricultural enterprises.

**Conclusions**
Results presented in this paper can be used for

economic-mathematical modeling and solving optimization processes of

forecasting and data management in a lack of information and the availability

of risk, as well as for the development of appropriate software and hardware

systems to support effective management decisions in practice.

**Key words:** posteriori identification, innovation process in

agriculture, economic and mathematical modeling, discrete dynamical system, the risks of agricultural production.

1. Jejkhoff, P. Fundamentals of Ientification Management

Systems. M.: Mir, 1975. - 686 p.

2. Grop, D. Methods of Identification Systems. M.: Mir,

1979. 302 p.

3. Sejdzh, Je.P., Melsa, Dzh.L. Identification of Control

Systems. M.: Nauka, 1974. - 248 p.

4. Sejdzh, Je.P., Melsa, Dzh.L. Estimation Theory and Its

Application in Communication and Management. M.: Svjaz, 1976. 496 p.

5. Ljung, L. Identification systems. Theory for the User.

M.: Nauka, 1991. - 432 p.

6. Cypkin, Ja.Z. Fundamentals of Information Theory of

Identification. M.: Nauka, 1984. - 320 p.

7. Rajbman, N.S. What is the Identification? M.: Nauka,

1970. - 118 p.

8. Shtejnberg, Sh.E. Identification in Management

Systems. M.: Jenergoatomizdat, 1987. - 80 p.

9. Shorikov, A.F., Minimax Positional Control of the

Identification Process in Non-linear Multistep Systems. Automation and Remote

Control. 1987. № 2, pp. 74–88.

10. Tatarkin, A.I., Kuklin, A.A., Cherepanova, A.V.,

2008. Sociodemographic Security of Russian Regions: Current Status and Problems

of Diagnosis. The Region's economy. 3 (15), pp. 153-161.

11. Tjuljukin, V.A., Shorikov, A.F., 1993. Algorithm for

Solving of the Terminal Control for Linear Discrete System. Automation and

Remote Control. 4, pp. 115-127.

12. Babenko, V.O., 2012. Information Support of Modeling

Optimization of Guaranteed Control with Innovative Technologies of Processing

of Agricultural Enterprises. Scientific journal «Agrosvit», 14, pp. 10-18.

13. Babenko, V. A. Modeling in the management of

innovation processes of processing of agricultural enterprises. Abstracts.

International Scientific Conference "Problems of Economic

Cybernetics". 15-17 October 2013 y., c. Alushta, smt. Partenit. Donetsk:

«Cifrova tipografija», 2013. - 126 p., pp. 10-11.

14. Vitlinskij, V.V., Babenko, V.V., Aspects of Modeling

Processes of Management of Innovative Technologies for Agro-industries.

Analysis, Modeling, Management, Development of Economic Systems: Collection of

Scientific Works of the V International Symposium School AMUR-2011, Sevastopol,

12-18 September 2011, Responsible editor M.Ju. Kussyj, A.V. Sigal. Simferopol: V.I. Vernadsksy TNU, 2011. - 411

p., pp. 63-69.

15. Aleksandrovskij, N.M., Dejch, A.M., 1968. Methods for

Determining of Dinamical Characteristics of Nonlinear Dynamic Objects.

Automation and Remote Control, 1, pp. 167-188.

16. Lotov, A.V., Introduction to the Economic and

Matematical Modeling. M.: Nauka, Home Edition physical and mathematical References,

1984. 332 p.

17. Babenko, V.O., A Model of Dynamic Optimization of

Control of Innovative Technologies in Enterprises for Processing Agricultural

Products. Proceedings of the International Scientific Conference "Problems

of Sustainable Development agrosphere" dedicated to the 195th anniversary

of the day the application type of V.V.Dokuchayev HNAU. 4-6 October 2011 y.

Harkiv: HNAU, 2011. - 570 p., pp. 44-47.

**The Modern Approaches to Managing Development of the Enterprise in the Field of Non-bank Financial Services**
**Zherlitsyn D. M. and Berlin V.M.**
**Purpose and subject of research**
Synthesis of the modern approaches to the management of development and

also justification of tool set implementation strategy to increase the

effectiveness of the management of the enterprise in the non-banking financial

services.

**Research methodology**
The theoretical framework is based on the methods of system analysis to

the business management of the financial sector, innovative models of financial

management, modern methods of economic cybernetics.

**Value results**
The proposed

theoretical approach is used to improve the system of development of the

enterprise in the non-banking financial services through the introduction of

complex administrative innovations.

**Conclusions**
The result of the study is the author's model of

implementation of managerial innovations to stimulate the development of the

enterprise sphere of non-bank financial services. The proposed integrated

approach combines the tools of investment management, economy and mathematical

methods, modern market theory and microeconomic analysis to a single

organizational and economic model. The proposed approach enhance the

competitiveness and sustainability of the enterprise in the field of

non-banking financial services.

**Key words:** development management, management of innovation, the

filed of non-bank financial services, enterprise management, financial

management.

1. Berlin, V.M.,

2013. Control Strategy of the enterprise competitiveness in the filed of

non-bank financial services. Ural Research Bulletin: scientific-theoretical and

practical journal. Series: Economics. – N 28 (73). - 2013. - pp. 33-40.

2. Bocharov, V.

V., 2009. Financial Analysis. A short

course. 2nd ed. St. Petersburg. Peter press. 240 p.

3. Zherlitsyn, D.

M., 2012. Innovative Management of the Enterprise Financial System. Ukraine,

Donetsk: "Yugo-Vostok", LLC. 256 p.

4. Kovalev, V.

V., 2009. Fundamentals of the theory of finance management: educational and

practical guide. Prospekt. 536 p

5. Statistics of

Ukraine's Insurance Market. Forinsurer.com – Journal of Insurance. [Online].

Available: http://forinsurer.com/stat/

6. Statistical

information, 2013. the State Statistics Service of Ukraine [online]. Available

from: www.ukrstat.gov.ua

7. Stoyanova, E.

S., 1996. Financial management. 2nd ed. Moscow: Perspective press. 405 p.

8. Delong, L.,

2005. Optimal Investment Strategy for a Non-Life Insurance Company: Quadratic

Loss. Applicationes Mathematicae, 32 (2005), pp. 263-277.

9. Forrester, J.

W., 1961. Industrial dynamics. Cambridge, MA: MIT press. 482 p.

10. Joehnk, M. D., Gitman, L. J., Smart, S. J., 2010.

Fundamentals of Investing. Prentice Hall PTR. 672 p.

11. Rudolph, M.J., 2001. U.S. Insurance Company

Investment Strategies in an Economic Downturn. U.S.A.: Rudolph Financial

Consulting, LLC. 77 p.

12. Schumpeter, J. A., 1934. The Theory of Economic

Development: An Inquiry Into Profits, Capital, Credit, Interest, and the

Business Cycle. Cambridge, Mass.: Harvard University Press. 225 p.

13. Sharpe, W. F., Alexander, G. J., Bailey, J. V.,

1999. Investments. Prentice-Hall Internat. 962 p.

14. Trott, Paul., 2005. Innovation Management And New

Product Development. Prentice Hall Internat., 540 p.

**Mechanism of prediction strategies indicators financial resources industrial enterprises**
**Serduk V.N., Zorina M.S.**
**Purpose and subject of research**
This article describes the features of strategy management of an

industrial enterprise financial resources, the conditions for increasing its

efficiency and proposed economic- mathematical model of the mechanism of

forecasting indicators strategic financial management of an industrial

enterprise, which improves the efficiency of operation of the business through

improved management of financial resources , to improve the accuracy and

timeliness of management decisions based on a scenario assess the economic

impact of the use of different measures for financial management of an

industrial enterprise.

**Research methodology**
The theoretical framework is based on the methods of system analysis to

the business management of the financial sector, innovative models of financial

management, modern methods of economic cybernetics.

**Value results**
The main of

this model are five scenarios for allocating financial resources of the

industrial enterprises in the respective activity.

**Conclusions**
According to the results of simulation experiments, we can conclude that

the application of the economic-mathematical model to minimize the cost of

financial resources and the value of their involvement. Addressing financial

management is the foundation of efficiency, competitiveness and financial

sustainability of the Ukrainian industrial enterprises.

**Key words:** industrial

enterprises, financial resources, strategies indicators, mechanism of

prediction.

1. Financial Management: Theory and Practice / Sub. eds. O.S. Stoyanova

. - M.: Perspective, 2001. - 401 p.

2. Tereshchenko A.A. Financial sector entities : Tutorial / A.A.

Tereshchenko. - K.: MBK, 2003. - 554 p.

3. Sumets A.M. Strategies of modern enterprise and its economic security

: tutorial / A.M. Sumets , M.B. Tumar . - K. "Long - TekPres ", 2008.

– 400 p.

4. Yastremskaya A.M. The quality of the formation of the financial

strategy of the enterprise / A.M. Yastremskaya , A.V. Grynyov / / Finance of

Ukraine . - 2006. - № 6. - P. 121-128 .

5. Alminova Z.F. The financial strategy of the company formation,

development and maintenance of stability . / Alminova Z.F. - Moscow: Sputnik +

Company, 2002. - 536 p.

6. Berezin A.V. Business Strategy : Manual / A.V. Berezin, M.G.

Bezpartochnyy . - K.: Lyra - 2010. - 224 p.

7. Tymokhyn V.M. Modeling economic dynamics methodology : Monograph /

V.M. Tymokhyn . - Donetsk : LLC " South- East Co., Ltd. ", 2007 . -

269 p.

**Model in managing the implementation of ERP systems **
**Poluektova N.R., Alekseevskiy D.G.**
**Purpose and subject of research**
The aim of the study is to find methods for effective

management of ERP- systems.

**Research methodology**
The theoretical framework is based on the methods of system analysis to

the business management of the financial sector, innovative models of financial

management, modern methods of economic cybernetics.

**Value results**
The complication and the increasing cost of complex

systems of operational business management (ERP-systems) cause deepening of

problems of evaluating their effectiveness. We need mechanisms that allow

manage the selection, implementation and use of such systems. As a result, the

model in managing the implementation of integrated enterprise management

systems was built.

**Conclusions**
The complication and the increasing cost of complex

systems of operational business management (ERP-systems) cause deepening of

problems of evaluating their effectiveness. We need mechanisms that allow

manage the selection, implementation and use of such systems. As a result, the

model in managing the implementation of integrated enterprise management

systems was built. The model allows to evaluate the impact of management

factors such as top management support , quality of communication , the degree

of awareness among staff and others on the speed of implementation of automated

functions. A system dynamic approach allows account of the positive feedbacks

that occur in the system in the implementation of information management

systems. Simulation experiments with the model help to formalize and present in

quantitative form some qualitative performance of ERP- systems.

**Key words:** ERP-system

implementation management, system dynamics.

1. Klaus, H.,

Rosemann, M., Gable,

G.G.(2000) “What is ERP”

Information systems Frontiers, Vol. 2, No. 2. pp. 141-162.

2. Heijkoop, G. (2005). “ ERP Systems: turning

promises into performance” Master thesis project,–TU Delft, pp.32-36.

3. Information Resource Management Glossary

(2005) – Retrieved January 13, 2014, from

http://www.cio.gov.bc.ca/other/daf/irm_glossary.htm

4. Somers, T., Nelson, K. (2001). ”The impact

of Critical Success Factors across the Stages of Enterprise Resource Planning

Implementation” Proceedings of the 34th Hawaii International Conference on

System Science (HICSS-3), January, Maui, Hawaii.

5. Esteves,

J. M., Pastor, J. A. (1999). “An ERP life-cycle-based research agenda”.

– Retrieved January 13, 2014, from http://jesteves.com/EMRPS99.pdf.

6. Poon, P. Wagner, C. (2001). “Critical

success factors revisited: success and failure cases of information systems for

senior executives” Decision Sciences Systems. 30, pp. 393–418.

7. Sumner , M. (1999). “Critical Success

Factors in Enterprise Wide Information Management Systems” Proceedings of the

Americas Conferenceon Information Systems, Milwaukee, WI. pp. 232-234.

8. Forrester,

J.(1971) “Industrial dynamics”

9. Balackii,

E.V. Model of birth and diffusion of innovation. – Retrieved January 13, 2014,

from http://www.kapital-rus.ru/articles/article/219057/

10. Mohammad,

S. A., Hosseini, S.S., Mahmoodi, J.

“New Framework of Effective External and Internal factors on the Success

of Enterprise Resource Planning (ERP) – Retrieved January 13, 2014, from www.textroad.com/

11. Powell, T. C. (1996). “How much does

industry matter? An alternative empirical est”. Strategic Management Journal.

17(4), pp 323 -334.

12. Gregorio, D. D., Kassicieh, S. K., Neto, R.

D. (2001).” Drivers of e-business

activity in developed and emerging markets” IEEE Transactionson Engineering

Management, 52(2), pp155-166.

12. Bernroider

E.W.N. Enterprise resource planning (ERP) diffusion and characteristics

according to the system’s life cycle: A comparative view of small-to-medium

sized and large enterprises/ report/ E. W.N. Bernroider M.J. Leseure // Department of information business Vienna University of

Economics and Business Administration [Електронний ресурс] – Режим доступу:

http://epub.wu.ac.at/1354/1/document.pdf — Загл. с экрана. — Яз., англ.