Subject. The article deals with investment activity. Objectives. We focus on the analysis of investment potential of metal companies in Russia. Methods. The study applies regression and correlation techniques. Results. We developed correlation matrices and regression models, enabling to select risk-dominant factors and macroeconomic indicators to assess investment attractiveness. Conclusions. The model of forecasting the stock quotes will help comprehensively assess the situation and make rational and effective management decisions that contribute to improving the competitiveness of companies.
Endovitskii D.A., Babushkin V.A., Baturina N.A. et al. Analiz investitsionnoi privlekatel'nosti organizatsii: monografiya [Analysis of investment attractiveness of the organization: a monograph]. Moscow, KnoRus Publ., 2014, 374 p.
Popov M.S., Lutovinov P.P. [The usage of categories of potential in decision-making system on management of the industrial-innovative activity of the enterprise]. Vestnik Yuzhno-Ural'skogo gosudarstvennogo universiteta. Seriya: Ekonomika i menedzhment = Bulletin of South Ural State University. Series: Economics and Management, 2010, no. 20, pp. 57–62. URL: Link (In Russ.)
Drucker P.F. Praktika menedzhmenta [The Practice of Management]. Moscow, Vil’yams Publ., 2007, 397 p.
Kaplan R.S., Norton D.P. Sbalansirovannaya sistema pokazatelei: ot strategii k deistviyu [The Balanced Scorecard: Translating Strategy into Action]. Moscow, Olimp-Biznes Publ., 2004, 294 p.
Matyash I.V. [Problems of the analysis of market systemic efficiency of the enterprises]. Izvestiya Altaiskogo gosudarstvennogo universiteta = Izvestiya of Altai State University, 2010, no. 2-1, pp. 253–262. URL: Link (In Russ.)
Larionova I.A., Rozhkov I.M., Kostyukhin Yu.Yu. et al. [Formation of the estimated vector for the diagnosis of the economic situation in the enterprise]. Ekonomika promyshlennosti = Russian Journal of Industrial Economics, 2019, vol. 12, no. 1, pp. 56–68. (In Russ.) URL: Link
Antunes F., Ribeiro B., Pereira F. Probabilistic Modeling and Visualization for Bankruptcy Prediction. Applied Soft Computing, 2017, vol. 60, pp. 831–843. URL: Link
Daemi A., Kodamana H., Huanga B. Gaussian Process Modelling with Gaussian Mixture Likelihood. Journal of Process Control, 2019, vol. 81, pp. 209–220. URL: Link
Zelenkov Yu., Fedorova E., Chekrizov D. Two-step Classification Method Based on Genetic Algorithm for Bankruptcy Forecasting. Expert System with Applications, 2017, vol. 88, pp. 393–401. URL: Link
Liang D., Lu C.-C., Tsai C.-F., Shih G.-A. Financial Ratios and Corporate Governance Indicators in Bankruptcy Prediction: A Comprehensive Study. European Journal of Operational Research, 2016, vol. 252, iss. 2, pp. 561–572. URL: Link
