Abstract
Credit rating agencies evaluate corporate risks and assign ratings to companies. Each rating grade corresponds to certain boundaries of default probability. KMV is a popular model to assess the default probability of a company. In this paper, a method to predict the default probability of a company is proposed. This method is based on the main concept of the KMV model; however, financial statements are applied instead of stock prices, i.e. time-series of EBIT (earnings before interest and taxes), net debt, sales, and the last year value of WACC (weighted average cost of capital). Default probabilities for 150 companies are evaluated. Results and limitations are discussed.
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Romanyuk, K., Ichkitidze, Y. (2020). Time Series Analysis of Financial Statements for Default Modelling. In: Arai, K., Kapoor, S., Bhatia, R. (eds) Intelligent Computing. SAI 2020. Advances in Intelligent Systems and Computing, vol 1228. Springer, Cham. https://doi.org/10.1007/978-3-030-52249-0_19
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DOI: https://doi.org/10.1007/978-3-030-52249-0_19
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