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The article examines the market of high-yield bonds (HYB) in Russia, which has seen steady growth in issuance volumes. The authors analyze key shortcomings of traditional rating agencies. As an alternative, they propose an automated method for analyzing financial statements using Jupyter Notebook, which enables processing large datasets with high accuracy. The advantages, challenges, and opportunities of API technology are analyzed, which can assist potential investors in selecting specific issuers. The application of the Zhdanov model for bankruptcy prediction is demonstrated on a sample of high-yield bond issuers. The advantages of this approach include flexible parameter tuning, minimized manual errors, and faster analysis compared to Excel. Additionally, Python code examples are provided. The results confirm the effectiveness of automation in assessing issuer bankruptcy risks.
high-yield bonds, Jupyter Notebook, Python, Excel, financial analysis, bankruptcy prediction model, API technologies, probability, issuer
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