Archives

  • 2018-07
  • 2019-04
  • 2019-05
  • 2019-06
  • 2019-07
  • 2019-08
  • 2019-09
  • 2019-10
  • 2019-11
  • 2019-12
  • 2020-01
  • 2020-02
  • 2020-03
  • 2020-04
  • 2020-05
  • 2020-06
  • 2020-07
  • 2020-08
  • 2020-09
  • 2020-10
  • 2020-11
  • 2020-12
  • 2021-01
  • 2021-02
  • 2021-03
  • 2021-04
  • 2021-05
  • 2021-06
  • 2021-07
  • 2021-08
  • 2021-09
  • 2021-10
  • 2021-11
  • 2021-12
  • 2022-01
  • 2022-02
  • 2022-03
  • 2022-04
  • 2022-05
  • 2022-06
  • 2022-07
  • 2022-08
  • 2022-09
  • 2022-10
  • 2022-11
  • 2022-12
  • 2023-01
  • 2023-02
  • 2023-03
  • 2023-04
  • 2023-05
  • 2023-06
  • 2023-07
  • 2023-08
  • 2023-09
  • 2023-10
  • 2023-11
  • 2023-12
  • 2024-01
  • 2024-02
  • 2024-03
  • To explain the corporate debt maturity following Brockman et

    2022-06-24

    To explain the corporate debt maturity, following Brockman et al. (2010), the time-varying control variables we include are: firm size (LSIZE), the square of firm size (LSIZE2), leverage (LEVERAGE), asset maturity (ASSET_MAT), managerial ownership (OWN), market-to-book ratio (MB), term structure of interest rates (TERM), abnormal earnings (ABNEARN), asset return standard deviations (STD_DEV), and dummy variables for firms with S&P credit ratings (RATE_DUM), firms with a high Altman (1977) Z-score (ZSCORE_DUM), and firms from regulated industries (REG_DUM). To explain the CEO compensation structure, following the compensation literature, e.g., Guay (1999), Hayes et al. (2012), Gormley et al. (2013) and Ellul et al. (2016), we include some additional variables: return on assets (ROA), tangibility of assets (TANGIBILITY), CEO tenure (TENURE), and the proportion of salary and bonus in CEO compensation (CASHCOMP). All the variables, except the dummy variables, are winsorized at 1st and 99th percentile. The detailed construction of these variables are in Appendix A.
    Empirical analysis In this section, we study the effect of the reduction in vega induced by FAS 123R on corporate debt maturity choices. Section 6.1 presents the summary statistics. Section 6.2 presents the baseline regression results. Section 6.3 offers several tests on internal validity.
    Potential mechanisms In the previous analyses, we show the decline in vega induced by FAS 123R results in an increase in corporate debt maturity. Both the manager and creditor Telaprevir predict this negative relationship. Now we turn to explore which channel is the main underlying mechanism and test the three mechanism hypotheses in Section 4 by studying the treatment effect on net debt issuances and investigating the cross-sectional variations.
    Conclusion Our results may be able to help explain why US firms use more short-term debt in the past decades, raised by Custódio et al. (2013). Custódio et al. (2013) document a secular decrease in firms' debt maturity in the past three decades, with the median percentage of debt maturing in more than 3 years dropped from 64% in 1976 to a record low of 21% in 2000. They find Highly repetitive DNA the decline in maturity can be partly explained by firms with higher information asymmetry and new firms issuing public equity in 1980s and 1990s. Meanwhile, during the same period, we also notice that firms experienced a dramatic explosion in the use of option compensation: options were almost negligible in the beginning of 1980s, while became the largest single component of CEO compensation by 1996 and accounted more than half of the total compensation by 2000 (Hall and Liebman, 1998; Murphy, 2013). Our results imply that the dramatic increase in the risk-taking incentives due to the explosion in option compensation might be an important driver behind the secular decrease in debt maturity in the past three decades.
    Acknowledgements I would like to thank the two anonymous referees for insightful comments. I also thank Petit-Romec Arthur (discussant), Laurent Bach, Romain Boulland, Xin Chang, David Cicero (discussant), Angie Low, Shan Zhao, and participants at 2018 FMA annual meeting and 2018 Paris December Finance Meeting for helpful discussions and useful comments. I also appreciate Fabrizio Ferri and Nan Li for providing me the Bear Stearns Equity Research Report (McConnell et al., 2004).
    Introduction Arsenic, a well-established human carcinogen, is widely distributed in the environment. Millions of people around the world are exposed to arsenic through air, food, medicinal use and drinking water (Kapaj et al., 2006; Nriagu et al., 2007; Argos et al., 2010; Li et al., 2015; Bundschuh and Bhattacharya, 2016; Shen et al., 2016; Bhattacharya et al., 2017; Keil and Richardson, 2017; Vithanage, 2017). Thus, arsenic is a major worldwide public health issues (Khaleghian et al., 2014; Keil and Richardson, 2017; Kuo et al., 2017; Wei et al., 2017a; Wei et al., 2017b). Workers are exposed to arsenic compounds in arsenic plants which produce arsenic trioxide (Wen et al., 2011). Epidemiologic evidence indicates that long-term arsenic exposure has been reported to be associated with several types of cancer (Smith et al., 2000; Gamboa-Loira et al., 2017; Hsu et al., 2017; Kuo et al., 2017).