Credit default swaps and corporate performance smoothing
Wei-Shao Wu,
Robert C.W. Fok,
Yuanchen Chang and
Chao-Jung Chen
Journal of Corporate Finance, 2022, vol. 75, issue C
Abstract:
This study examines whether the availability of traded credit default swaps (CDS) influences the referenced firms' incentive to smooth their performance. We show that with the introduction of CDS trading on their debt, CDS-referenced firms (CDS firms) lower both their earnings and cash flow volatility. Specifically, earnings volatility declines faster than cash flow volatility, which is consistent with income smoothing behavior. The effect of CDS trading on performance smoothing is qualitatively similar under different market and economic conditions. These results support the notion that CDS firms smooth their performance to avoid renegotiation with CDS-protected creditors. We also find that CDS firms smooth their cash flows via hedging with derivatives and smooth their earnings using discretionary accruals after the inception of CDS trading.
Keywords: Credit default swaps; Cash flow volatility; Income smoothing; Earnings management; Corporate hedging (search for similar items in EconPapers)
JEL-codes: G20 G21 G32 M41 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)
Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0929119922000815
Full text for ScienceDirect subscribers only
Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.
Export reference: BibTeX
RIS (EndNote, ProCite, RefMan)
HTML/Text
Persistent link: https://EconPapers.repec.org/RePEc:eee:corfin:v:75:y:2022:i:c:s0929119922000815
DOI: 10.1016/j.jcorpfin.2022.102238
Access Statistics for this article
Journal of Corporate Finance is currently edited by A. Poulsen and J. Netter
More articles in Journal of Corporate Finance from Elsevier
Bibliographic data for series maintained by Catherine Liu ().