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Forecasting the annual revenues of Russian large and medium-sized companies in the trade industry

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Revenue is the paramount parameter in financial forecasting. This indicator determines the dynamics of some financial parameters. The object of work is the future financial condition of Russian trade companies. The sub-ject of the research is the revenue of these companies. The aim of the work is to create a method for forecasting the revenue of trading companies, which will show the minimum possible error. The main research method is re-gression analysis. There are three main approaches to the formation of forecast values for the revenue of trading companies - the use of macroparameters, the previous value, and annual growth. The combined model of the existing revenue forecasting methodologies makes it possible to most accurately predict the future annual values of this indicator for companies in the trade in the Russian market. The correct approach to forecasting revenue should be considered not the one that accurately predicts the actual value of the parameter, but the one at which the forecast error is the minimum acceptable. We have at our disposal four historical periods - 2015, 2016, 2017 and 2018, 43 selected INNs for testing forecasting methods. The two periods for testing forecasts are 2017 and 2018. Forecasts will be built one year ahead, so we get 86 (43 INN * 2 test years) points for comparing forecast results. To test and compare the results, the MAPE forecast quality metric and graphical analysis will be used. The result of the study is the successful use of the hybrid method - the number of predicted revenues, the error of which is less than 10% and 20%, is higher among the selected ones. This suggests that 54% of the forecast points have an error of less than 20%.

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