Dynamic Bayesian Networks Application for Evaluating the Investment Projects Effectiveness

In this paper, we propose a methodology for using dynamic Bayesian networks (DBN) in the tasks of assessing the success of an investment project. The methods of constructing DBN, their parametric learning, validation and scenario analysis of “What-if” are considered. A dynamic Bayesian model has been developed for scenario analysis and forecasting the success of an investment project. The model takes into account the time component and is designed in collaboration with expert economists in the selection and quantification of input and output variables. Now, using the dynamic Bayesian model, it is possible with a certain degree of probability to assess the degree of success of the capital investment, without incurring monetary and temporary losses. This will greatly facilitate the investment forecast for identifying profitable investment sources. This is the advantage of the proposed approach.