Economic sanctions are among the most powerful instruments of international policy. However, this study, using the example of the so-called anti-Russian sanctions, shows that in the global economy, countries are rapidly using other alternatives, and sanctions in the case analyzed act as a catalyst for balance of trade between the Russian Federation and the People’s Republic of China. The study is based on a highly topical sophisticated model of neural networks, which provides clear results confirming the unintended positive effect. The time series and aggregated data became inputs into multilayer perceptron networks, while the methodology used enabled eliminating of both too large averaging and extreme fluctuations of the equalized time series. Out of 10,000 networks created for each variable and each time lag, five showing the best characteristics given by correlation coefficients and absolute residual sums were retained. Thus, the created equalized time series were able to describe the basic trend of the actual development of export and import, while also capturing their local extremes. The interpolation of the two time series shows that the sanctions imposed on the Russian Federation in 2014 have clearly strengthened its balance of trade with the People’s Republic of China. The results of the study also predict further growth in the balance of trade between the Russian Federation and the People’s Republic of China, although this development may be delayed by current events.
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