In this article, the problem of optimizing investments in risk management is considered through the theory of the firm and the problems arising from this theory (the problem of the “principal-agent”, the theory of contracts).
The purpose of this study is the theoretical and empirical evidence of the optimal investment model proposed by the author for corporate risk management. The object of the research is the companies of the metal and mining industry of the Russian Federation. The subject of research are the financial performance and the amount of management expenses of companies.
The theoretical significance of the study is in the ability of indirect evaluating investments in corporate risk management based on the company's financial statements. Practical significance is the ability to use the results obtained in the real conditions of corporate governance of the company. The practical significance of the study is the ability to determine the appropriate amount of investment in risk management.
The aim of the research is to develop the methodology of bankruptcy prediction applying the specified statutory values of the existing models with a glance to company’s industry and developing the author’s prediction model. Initially authors estimated the forecast accuracy of the existing models for the enterprises of 8 industries. Using CART (Classification And Regression Tree) methodology the original statutory values of the models were specified for every industry under research. The calculated statutory values demonstrated the high level of prediction accuracy and balanced the indicators of accuracy for bankrupt and non-bankrupt companies. The indicators with the maximum level of significance for bankruptcy prediction were selected from all the models. They formed a basis for a new developed model, which has demonstrated the high level of prediction accuracy on a sample under research. The statutory values for the new model were also developed. The implementation of the research’s results will increase the efficiency of bank ruptcy prediction and low the number of bank rupt companies.