Retail trade turnover represents one of the most fundamental socio-economic indicators. Constant monitoring of such indicators has a pivotal role in modernization of the Russian economy. Retail trade turnover reflects countries’ economic capacity and standard of living. This paper proposes a statistically significant econometric model of the retail trade turnover dependence with respect to different factors. Such factors as consumer prices index, unemployment level and average monthly designed salary were taken as the explanatory variables. The variables were selected based on Granger causality test and time series analysis of several socio-economic indicators. For each explanatory variable, a statistically significant model ARIMA (1, 1, 0) was constructed, with the help of which the predicted values of the explanatory variables for October, November and December 2019 were calculated, which were used to forecast the retail turnover for these months.

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Assessment of financial sustainability is a key instrument that every company should use to successfully operate in the contemporary marketplace. In this paper profit was chosen as one of the sustainability indices and binary choice model logistics regression model (logit model) was built for that index. The research data for this study is drawn from accounting statements of a textile industry business in Samara city. A combination of econometric approaches was used in the data analysis. Binary choice models were adopted in this research. Then those models were estimated for validity. Also scenario forecasts methodology was employed in this study. Several logit models with a set of explanatory variables were constructed. After the comparison of those models the preferred one was determined. Based on that model a scenario for profits was forecasted including both the worst-case and the best-case ones. The average-case scenario forecast was also made.

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