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Exploiting uncertainty with market timing in corporate bond markets

Abstract

The purpose of this article is to show the usefulness of technical analysis in credit markets. We document that an application of a simple moving average timing strategy to US high-yield and US investment-grade corporate bond portfolios sorted by option-adjusted spread generates investment timing portfolios that substantially outperform the corresponding benchmark. For portfolios with high uncertainty, as measured by the option-adjusted spread, the abnormal returns generate economically and statistically significant returns relative to the capital asset pricing model, the four-factor model and additionally the bond factor model from Asness et al. (J Finance 68:929–985, 2013). Our results remain robust to different moving average formation periods, transaction costs, long–short portfolio construction techniques and alternative definitions of information uncertainty.

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Fig. 1

Notes

  1. The US bond market is considered as the largest security market in the world, and according to Federal Reserve data, the total market value of US corporate bonds had a growth rate of 8.5% per year from 1990 to 2014.

  2. Since government bond yields are on historically low levels, the demand for credit securities plays a much larger role than in the past. Usually, institutional investors such as pension funds, mutual funds and insurance companies invest in these securities.

  3. For instance, Brock et al. (1992), Lo et al. (2000) and Han et al. (2013) use simple moving average schemes to forecast the equity market.

  4. See, for example, Hong et al. (2012), reporting that stocks lead HY bonds and to a lesser degree IG bonds as well or Bao and Hou (2014), who show that the comovement between equities and bonds is stronger for firms with higher credit risk for a variety of measures of this firm characteristic.

  5. Chordia et al. (2016) state that sophisticated institutions, who in fact dominate corporate bond markets, price risk in the neoclassical sense.

  6. See Hong et al. (2012) or Bao and Hou (2014).

  7. The data on bond value and bond momentum factors are obtained from AQR’s Web site: https://www.aqr.com/library/data-sets/value-and-momentum-everywhere-factors-monthly.

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Acknowledgements

We wish to express our thanks for helpful comments and valuable suggestions to an anonymous referee, Stephen Satchell (editor), Brian Teng Yuan Cheng (discussant), Patrick Jahnke, Dirk Schiereck and Esad Smajlbegović as well as seminar participants at the Behavioural Finance Working Group Conference 2017. This paper was also accepted for presentation at the 2017 Financial Management Association (FMA) Annual Meeting, European Financial Management Association (EFMA) 2017 Annual Meetings, 3rd Symposium on Quantitative Finance and Risk Analysis (QFRA) and the 34th International Conference of the French Finance Association (AFFI), but not presented at either conference due to scheduling conflicts. Views expressed in this paper are those of the authors and do not necessarily reflect those of Deka Investment and Allianz Global Investors or their employees. Parts of this research project have been conducted while the first author was at the University of Chicago Booth School of Business.

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Correspondence to Demir Bektić.

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Bektić, D., Regele, T. Exploiting uncertainty with market timing in corporate bond markets. J Asset Manag 19, 79–92 (2018). https://doi.org/10.1057/s41260-017-0063-6

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  • DOI: https://doi.org/10.1057/s41260-017-0063-6

Keywords

  • Market efficiency
  • Market timing
  • Predictability
  • Behavioral finance
  • Technical analysis

JEL Classification

  • G11
  • G12
  • G14