Importance In connection with the unstable processes in the Russian economy, which have the negative impact on the Russian stock market performance represented by the Russian Trade System (RTS) index dynamics, we consider the experience of the original economic and mathematical tools application aimed to make stock markets analysis and forecasting. Objectives The experience enabled us to use the development of economic and mathematical methods to identify the trends of the market concerned and forecasting its performance in the medium-term perspectives (two to three years). Methods The integrated approach used in the paper is based on the synthesis of various research methods (econometric and neural network models of the market dynamics) and the subsequent integration of the obtained results, which we have achieved on the basis of the proposed algorithm, as well as the use of the Hurwitz Stability Criterion. In this paper, we have analyzed the stock market in two stages of its development: the pre-crisis phase (prior to 2008) and the crisis phase (after 2008). Depending on the period in question, while making the analysis, we have used various macroeconomic indicators affecting the RTS index, and applied different simulation time-step, that is conditioned upon the specifics of each of the studied phases of the market development. Thus, the developed economic and mathematical tool of the considered period differs significantly. We provided the medium-term forecasting experience of the Russian stock market using the proposed research techniques. Results According to the models, we make forecast calculations, which in the course of time were updated. The updated calculations confirmed a good predictive ability of the developed instruments applicable not only to stable, but also to the crisis market phases. Taking into account that the study of crisis process modeling in the economic and mathematical direction is poorly presented, the obtained results possess the significant novelty and represent the undeniable interst to both economists and mathematicians, and to financial analysts, as well. Conclusions and Relevance The modeling technique of the stock market described in the article, as well as the application of the developed economic and mathematical tools, give evidence of its sufficient productive efficiency and the possible extension of the lessons learned in modeling of the stock markets of other countries.
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