Modelling the financial stability of an enterprise taking into account macroeconomic indicators

Abstract

Davit S. Bidzhoyan - Postgraduate Student, Department of Business Analytics, Doctoral School of Economics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: bidzhoyan_david@mail.ru

Tatiana K. Bogdanova - Associate Professor, Department of Business Analytics, National Research University Higher School of Economics
Address: 20, Myasnitskaya Street, Moscow, 101000, Russian Federation
E-mail: tanbog@hse.ru

      Nowadays enterprises operate in a rapidly changing macroeconomic environment, and this factor should be taken into account when forecasting a company’s financial statement as a whole, or some of its particular aspects. However, development of the company’s financial stability assessment model taking into account macroeconomic factors is hampered by the problem of inclusion in the model of some factors with frequency of measurement different from that of the internal financial performance. For example, currency rates and crude oil prices can change on a daily, weekly, or monthly basis. Changes in the key interest rate cannot be characterized as systematic, since the Central Bank can vary the key interest rate depending on market conditions. Meanwhile, financial indicators of the company are published in the semi-annual and annual reports.
      This paper proposes an approach that aggregates macroeconomic factors, which means presenting a time series of each variable for each year, followed by the inclusion of polynomial coefficients in the final model as reference variable characteristics. The weighted average is calculated for the key interest rate, where the weights are the days during which the rate is operated. Based on the data of 291 metallurgical industry enterprises of the Volga federal district for the period 2012–2014, a financial stability assessment model has been built relying on the decision tree model using CRT (Classification and Regression Tree). The accuracy of the model is approximately 86%. The decision tree structure has served as a basis for recommendations to optimize certain financial indicators of operations to reach financial stability.