Textual Analysis of Corporate Bond Market

Project: Research

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On the one hand, the academic literature on the corporate bond return market is relatively short, although extensive research in predicting stock returns has been conducted for the last century. Though investment in corporate bonds plays a more critical role in current institutional investors' portfolios, the best-known pricing metric is still the bond credit rating. Many modeling techniques and methods well developed for equities are waiting to be tested for the bond market. On the other hand, digital text data is widely accessible in the financial market, such as economic news articles, corporate earnings reports, analyst reports, investment blogs, and even tweets. Analyzing these digital texts has been the daily research for financial analysts, yet modeling text as data is a breakthrough in the recent decade. Because text data are entirely different from traditional data often used by financial economists, they provide a relatively untapped data source.Understanding the text is a valuable and challenging question for researchers. In the field of computer science, natural language processing (NLP) is a machine learning technique that allows computers to break down and understand text much as a human would. Financial economists' textual analysis is to extract information and uncover actionable insights from the unstructured text data. However, given the low signal-to-noise ratio in the financial market, It is necessary to incorporate structural economic restrictions into machine learning methods to improve out-of-sample prediction and economic interpretation.The current economics and finance literature mainly focuses on the sentiment analysis for the macroeconomy and the equity market (Tetlock, 2007; Hassan et al., 2019). Starting with one co-investigator's recently published work, we find a large room for textual analysis in the corporate bond market. We have listed three potential research questions related to the initial bond offering, bond return sentiment, and the credit risk sentiment.One recent publication of the co-investigator (Gao et al., 2020) studies media coverage's impact on offering corporate bonds' yield spreads. They find very positive results by simply counting the number of news articles. Our first research proposal is about the news sentiment on the offering yield spreads in corporate bonds, a sophisticated extension. Second, with the news data on individual firm coverage, we would like to investigate news articles' sentiment analysis and the cross-section of corporate bond returns in the secondary market. Finally, we are also interested in the sentiment analysis for credit risk in the corporate bond market.


Project number9043231
Grant typeGRF
Effective start/end date1/01/22 → …