Country risk, company performance, ROE, machine learning algorithms
Managing risk is important. Organizations are starting to see the value of, or asking for strategic solutions to managing the risk. Risk refers to a deviation from what the organization plans or expects. Risk has an upside (opportunity), as well as a downside, the potential negative impact to an asset. This type of risk (loss) can prevent companies from achieving strategic goals. Organizations can turn risks into opportunities through effective risk management.
For public companies which have subsidiaries in many countries, one of the risks should be managed is country risk. Country risk is defined as the risk a foreign government will default on its bonds or other financial commitments. Country risk also refers to the broader notion of degrees to which political and economic unrest affects the securities of issuers that do businesses in a particular country.
In this research, we analyze the effect of country risk on company performance. Moreover, we employ linear regression to model the effect and the result shows country risk has a significant negative influence on Return on Equity (ROE). We also build nine models to predict country risk ratings based on country risk reports by utilizing machine learning algorithms. Furthermore, decision tree algorithm has the highest accuracy 31.25% on our dataset. Finally, our results show that, firstly, international companies who have overseas subsidiaries can benefit from using country risk as a tool to measure returns. Secondly, decision tree algorithm should be utilized to help decision makers determine country risks based on country reports; however, the effect of time-series data set into the machine learning algorithms still needs more investigations.
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