The aim of this study is to create models of a decision tree and neural networks for problem prediction and classification movement of stock Zvijezda d.d., to explain the main concept of the model neural networks and decision trees, and the concept of stocks. The study will be created on concrete examples made in the software package Statistica, where it will get and describe the results of the movement of the stock Zvijezda d.d. and it's rise and fall. This research will be used by students and others interested in the application methods of neural networks and decision trees in business. Results showed that the successful model classification is made using a hyperbolic tangent function and the overall classification rate is 100%. An increase of 7.14%, compared to the results obtained from the model decision tree. The problem of classification with the overall rate of classification showed the results of the sensitivity analysis of output variables to the input. The results found that the greatest impact of the input variables to the output variable have the "Last" with a coefficient of sensitivity 32339602, while the smallest effect has the input variable "Quantity" and the coefficient of sensitivity 146039.8. The results of sensitivity analyses obtained a model decision tree that shows that "The number of transactions" variable is the least significant, with relation to the output variable with a coefficient of sensitivity 0.074827. The greatest impact of the input variables to the output in the decision tree model has a variable "Change" with a coefficient of sensitivity 1.000000.