Data-driven decision-making in credit risk management: The information value of analyst reports

Other than banks, non-financial companies also continuously monitor and analyze their credit risk exposure to avoid possible counterparty defaults. Credit default swaps are commonly used financial instruments that provide information on a counterparty's creditworthiness. Although this metric can provide crucial insights, the underlying price dynamics often remain unknown and require further explanation. Data-driven decision-making is a key concept for identifying these reasons and supporting and justifying decisions. In this paper, we provide such justifications by applying sentiment and topic analysis to company-related financial analyst reports. While the contents of financial news have been analyzed in the past, analyst reports can offer additional insights, as seasoned analysts use them to disseminate in-depth research to experienced investors. This analysis examines 3386 analyst reports covering constituents of the Dow Jones Industrial Average Index in the period from 2009 to 2020. The results suggest that even when established credit risk indicators and financial news are considered, the sentiment and a subset of topics are correlated with changes in the credit default swap spread, indicating a fundamental relationship between quantitative risk metric and analyst reports. We find that analyst reports contain information related to the change in credit default swap spreads, an insight that helps to improve our understanding of existing risk assessments. The outcome indicates that banks or corporate risk managers can benefit from complementing established financial metrics and even financial news data with new insights derived from analyst reports. •Improve understanding of credit risk using the insights from analyst reports.•Complement established risk metrics with latent topics extracted using LDA.•Inverse relationship between sentiment in analyst reports and credit risk metric.•Identified topics encompass company, sector, and regulatory issues.•Supports data-driven decision-making for risk managers with interpretable topics.