Neural network technologies of investment risk estimation taking into account the legislative aspect

Abstract

The article proposes conceptual bases of formalization of the investment risk estimation process by means of mathematical and computer modeling on the basis of neural network technologies. The methodological approach to investment risk estimation has been improved. It allows identifying project risk and investment feasibility with using of Hamming neural network accurately and reasonably, reducing the cost of investment making decision and allows self-learning specialized network. The structural hierarchical model of the investment risk estimation process has been improved. It allows decomposing and simplifying the formalization procedure as well as allows simultaneous estimation of the financial ratio of the enterprise and its proposed investment project. The proposed mathematical model was verified and its adequacy was checked by comparing the results obtained on the basis of the application of existing methods and the approach developed by the authors of the article. This revealed the significant advantages of the method proposed in the article. The proposed approach was successfully implemented to estimate the investment risk of 20 domestic enterprises and projects.