Trade price clustering in the corporate bond market

Abstract

PurposeIn this paper, the authors document the existence of price clustering in the US corporate bond market.Design/methodology/approachUsing a sample of 8,422,593 corporate bond trades in 2014, the authors find that over 18% (1,522,284 trades) of all bond trades end in a clustered price, defined as a price ending in 00, 25, 50, or 75.FindingsOverall, the authors find that both bond rating category and risk, as measured by standard deviation of prices, play a role in price clustering; speculative grade bonds account for the majority of clustered prices. Clustered prices are more likely to have higher coupon rates, higher prices, and higher standard deviations of price than bonds with non-clustered prices. Regardless of size, both buy and sell dealer trades with customers (relative to interdealer trading) lead to an increase in price clustering. Dealers appear to use clustered prices when purchasing from and selling to institutions and, therefore, may use a clustered price to insulate themselves from the risk of asymmetric information. Additionally, the prevalence of clustered prices for retail-sized dealer sell trades suggests that dealers exercise dealer power over retail-sized traders.Originality/valueThis paper contributes to the literature on price clustering by examining trade price clustering of corporate bonds. It is different from previous papers on price clustering in equities. Given that bonds tend to be priced off of yield, it is unusual that trade prices cluster. It also demonstrates what kind of bonds cluster and with which customers dealers trade at clustered prices. It parallels other research in demonstrating dealer power over retail-sized traders.

Another look at the price clustering behavior: evidence from the Muscat stock exchange

“…Our work connects to the studies that examined the traditional factors that could explain such clustering behavior, namely price, volume of trades and volatility (see for example, Cole et al, 2022;Narayan, 2022). Given the abrupt drop in stock prices that occurred during the Covid-19 crisis as well as the fact that uncertainty rocketed upward around the globe [1], our research action comes very much timely.…”

“…In the first robustness check, we have divided the sample into two sub-samples in respect of firm activity criteria. Following Cole et al (2022), we consider financial firms and nonfinancial firms. The reason is simple: it is likely that industry explains the differences in clustering.…”

“…The price clustering behavior is well-documented in the literature for a variety of financial instruments and markets. Studies are covering for example stock markets (see, Harris, 1991;Grossman et al, 1997;As ¸çıo glu et al, 2007;Ikenberry and Weston, 2008;Chiao and Wang, 2009;Lien et al, 2019, Hu et al, 2019, bond markets (Gwilym et al, 1998;Edwards et al, 2007;Cole et al, 2022), commodity markets (see, e.g. Ball et al, 1985;Grossman et al, 1997;Narayan et al, 2011a, b;Palao and Pardo, 2012;Bharati et al, 2012;Narayan, 2022), foreign exchange markets (see, e.g.…”