MODEL BASED DECISION SUPPORT SYSTEM FOR FORECASTING FINANCIAL PROCESSES

O. Kozhukhivska, P. Bidyuk, A. Kozhukhivskyi
Abstract: 
A computer based decision support system is proposed the basic tasks of which are adaptive model constructing and forecasting of financial and economic processes. The system is developed with the use of system analysis principles, i.e. the possibility for taking into consideration of some stochastic and information uncertainties, forming alternatives for models and forecasts, and tracking of the computing procedures correctness during all stages of data processing. A modular architecture is implemented that provides a possibility for the further enhancement and modification of the system functional possibilities with new forecasting and parameter estimation techniques. A high quality of final result is achieved thanks to appropriate tracking of the computing procedures at all stages of data processing: preliminary data processing, model constructing, and forecasts estimation. The tracking is performed with appropriate set of statistical quality parameters. Examples are given for modeling and forecasting of nonlinear and nonstationary financial and economic processes. The examples show that the system developed has good perspectives for the practical use. It is supposed that the system will find its applications as an extra tool for decision making when developing the strategies for enterprises of various types.
References: 

1. McNeil A.J. Quantitative Risk Management / A.J. McNeil, R. Frey, P. Embrechts. – Princeton (New Jersey): Princeton University Press, 2005. – 538 p. 2. International Convergence of Capital Measurement and Capital Standards. A Revised Framework. Comprehensive Version. – Basel Committee on Banking Supervision, Bank for International Settlements. – Basel, 2006. – 158 p. 3. Mays E. (Ed.) Handbook of Credit Scoring / E. (Ed.) Mays. – Chicago: Glenlake Publishing Company, Ltd., 2001. – 460 p. 4. Neil M. Using Bayesian networks to model expected and unexpected operational losses / Neil M., Fenton N.E., Tailor M. // Risk Analysis. – 2005. – P. 34-57. 5. Zgurovsky M.Z. Method of constructing Bayesian networks based on scoring functions / M.Z. Zgurovsky, P.I. Bidyuk, O.M. Terentyev // Cybernetics and System Analysis, 2008.- Vol. 44.- No.2.- P. 219-224. 6. Polovcev O.V. A System Approach to Modeling, Forecasting, and Management of Financial and Economic Processes / O.V. Polovcev, P.I. Bidyuk, L.O. Korshevnyuk. – Donetsk: Oriental Publishing House, 2009. – 286 p. 7. Hollsapple C.W. Decision support systems / C.W. Hollsapple, A.B. Winston. – Saint Paul: West Publishing Company, 1996. – 860 p. 8. Tsay R.S. Analysis of financial time series / R.S. Tsay. – Hoboken: Wiley & Sons, Inc., 2010. – 715 p. 9. Bidyuk P.I Methods of Forecasting / P.I. Bidyuk, O.S. Menyailenko, O.V. Polovcev. – Lugansk: Alma Mater, 2008. – 608 p. 10. Burstein F. Handbook of Decision Support Systems / F. Burstein, C.W. Holsapple. – Berlin: Springer-Verlag, 2008. – 908 p. 11. De Jong P. Generalized Linear Models for Insurance Data / P. De Jong, G.Z. Heller. – New York: Cambridge University Press, 2008. – 197 p. 12. Gilks W.R. Markov chain Monte Carlo in practice /W.R. Gilks, S. Richardson, D.J. Spiegelhalter. – New York: Chapman & Hall/CRC, 2000. – 486 p. 13.Jensen F.V. Bayesian Networks and Decision Graphs / F.V. Jensen, Th. D. Nielsen. – New York: Springer, 2007. – 457 p. 14. Smola A.J. A tutorial on support vector regression / A.J. Smola, B. Scholkopf // Statistics and computing, 2004.- Vol. 14.- P. 199–222.