"Let Me Get Back to You" — A Machine Learning Approach to Measuring NonAnswers
Andreas Barth, Sasan Mansouri, Fabian Wöbbeking
Management Science,
No. 10,
2023
Abstract
Using a supervised machine learning framework on a large training set of questions and answers, we identify 1,364 trigrams that signal nonanswers in earnings call questions and answers (Q&A). We show that this glossary has economic relevance by applying it to contemporaneous stock market reactions after earnings calls. Our findings suggest that obstructing the flow of information leads to significantly lower cumulative abnormal stock returns and higher implied volatility. As both our method and glossary are free of financial context, we believe that the measure is applicable to other fields with a Q&A setup outside the contextual domain of financial earnings conference calls.
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Ownership Structure and the Cost of Debt: Evidence From the Chinese Corporate Bond Market
Sris Chatterjee, Xian Gu, Iftekhar Hasan, Haitian Lu
Journal of Empirical Finance,
September
2023
Abstract
Drawing upon evidence from the Chinese corporate bond market, we study how ownership structure affects the cost of debt for firms. Our results show that state, institutional and foreign ownership formats reduce the cost of debt for firms. The benefits of state ownership are accentuated when the issuer is headquartered in a province with highly developed market institutions, operates in an industry less dominated by the state or during the period after the 2012 anti-corruption reforms. Institutional ownership provides the most benefits in environments with lower levels of marketization, especially for firms with low credit quality. Our evidence sheds light on the nexus of ownership and debt cost in a political economy where state-owned enterprises (SOEs) and non-SOEs face productivity and credit frictions. It is also illustrative of how the market environment interacts with corporate ownership in affecting the cost of bond issuance.
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