Credit Information in Earnings Calls

61 Pages Posted: 9 Aug 2022 Last revised: 25 Sep 2022

See all articles by Harry Mamaysky

Harry Mamaysky

Columbia University - Columbia Business School

Yiwen Shen

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management

Hongyu Wu

Yale School of Management

Date Written: July 27, 2022

Abstract

We develop a novel technique to extract credit-relevant information from the text of quarterly earnings calls. This information is not spanned by fundamental or market variables and forecasts future credit spread changes. One reason for such forecastability is that our text-based measure predicts future credit spread risk and firm profitability. More firm- and call-level complexity increase the forecasting power of our measure for spread changes. Out-of-sample portfolio tests show the information in our measure is valuable for investors. Both results suggest that investors do not fully internalize the credit-relevant information contained in earnings calls.

Keywords: Corporate credit, credit default swaps, return forecasting, NLP

JEL Classification: G11, G12, G14

Suggested Citation

Mamaysky, Harry and Shen, Yiwen and Wu, Hongyu, Credit Information in Earnings Calls (July 27, 2022). HKUST Business School Research Paper No. 2022-075, Available at SSRN: https://ssrn.com/abstract=4174416 or http://dx.doi.org/10.2139/ssrn.4174416

Harry Mamaysky (Contact Author)

Columbia University - Columbia Business School ( email )

3022 Broadway
New York, NY 10027
United States

Yiwen Shen

Hong Kong University of Science & Technology (HKUST) - Department of Information Systems, Business Statistics and Operations Management ( email )

Clear Water Bay
Kowloon
Hong Kong

Hongyu Wu

Yale School of Management ( email )

165 Whitney Ave
New Haven, CT 06511

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