Subject. The article deals with predicting the corporate bond spread in the primary corporate bond market of BRICS countries. Objectives. The study aims to choose a suitable configuration of artificial neural network and make a list of input variables to predict yield spread, to test the ability of the neural network to forecast the yield spread of corporate bonds based on the data for BRICS countries. Methods. The study rests on two unrelated data sets obtained from different sources. At the baseline, I chose a configuration of neural network, which enabled to obtain reasonable forecasts on the data from corporate bond markets of BRICS member States and separately of Russia. Then I selected models (lists of independent variables), which provided the best forecasts. The neural network was trained on the data for five years, and then a forecast was made on the data for the sixth year. The quality of the forecast was thereafter assessed. Results. In the model, which was calculated using the biggest number of observations and which disregarded the specifics of the issuer, the error of prediction does not exceed 200 basis points in seventy five percent of observations. Other models demonstrated even better results. Conclusions. The chosen method of prediction is applicable to the corporate bond market data for Russia and for BRICS countries.
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