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COMMON FACTORS in CORPORATE BOND RETURNS Ronen Israela,C, Diogo Palharesa,D and Scott Richardsonb,E

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COMMON FACTORS in CORPORATE BOND RETURNS Ronen Israela,C, Diogo Palharesa,D and Scott Richardsonb,E Journal Of Investment Management, Vol. 16, No. 2, (2018), pp. 17–46 © JOIM 2018 JOIM www.joim.com COMMON FACTORS IN CORPORATE BOND RETURNS Ronen Israela,c, Diogo Palharesa,d and Scott Richardsonb,e We find that four well-known characteristics (carry, defensive, momentum, and value) explain a significant portion of the cross-sectional variation in corporate bond excess returns. These characteristics have positive risk-adjusted expected returns and are not subsumed by traditional market premia or respective equity anomalies. The returns are economically significant, not explained by macroeconomic exposures, and there is some evidence that mispricing plays a role, especially for momentum. 1 Introduction predict returns in other markets, yet researchers have not studied the viability of all these charac- Corporate bonds are an enormous—and grow- teristics to predict returns in credit markets. The ing—source of financing for companies around characteristics are carry, quality, momentum, and the world. As of the first quarter of 2016, there value (Koijen et al., 2014 for carry; Frazzini and was $8.36 trillion of U.S. corporate debt out- Pedersen, 2014 for quality; Asness et al., 2013 for standing, and from 1996 to 2015 corporate bond momentum and value). Our contribution includes issuance grew from $343 billion to $1.49 trillion (i) applying these concepts to credit markets; (ii) (Securities Industry and Financial Markets Asso- studying them together in a way that shines light ciation). Surprisingly little research, however, has on their joint relevance or lack thereof; (iii) eval- investigated the cross-sectional determinants of uating their economic significance by examining corporate bond returns. both long-and-short, transaction-costs-oblivious We study the drivers of the cross-section of cor- portfolios, and also long-only, transaction-costs porate bond expected returns. To do so, we focus aware portfolios; and (iv) exploring the source on a set of characteristics that has been shown to of the return premia by testing both risk- and mispricing explanations. aAQR Capital Management, Greenwich, CT. b AQR Capital Management, London Business School, Using traditional long-and-short portfolio analy- London, United Kingdom. cE-mail: [email protected] sis and cross-sectional regressions we find pos- dE-mail: [email protected] itive risk premiums that are highly significant eE-mail: [email protected] (t-statistics of 3 or more) for all characteristics Second Quarter 2018 17 18 Ronen Israel et al. but carry. These premia are distinct from tra- for transaction costs and other potential trading ditional long-only bond and equity market risk restrictions. premia, as well as from the premium earned by long-and-short equity anomalies based on value, To establish more realistic returns we also study momentum and defensive. The strong relation long-only portfolios of relatively liquid corpo- among carry, defensive, value and momentum rate bonds with exposure to carry, defensive, and future credit excess returns can be interpreted momentum and value themes. We show that these as out-of-sample evidence for the broader efficacy portfolios generate high risk-adjusted returns, net of these characteristics. of trading costs. Relative to a value-weighted benchmark of corporate bonds, the long-only We also make a methodological contribution to portfolio yields a net (of transaction cost) active long-and-short portfolio analysis for credit mar- return of 2.20% annualized, which translates to kets. The volatility and market beta of corporate an information ratio of 0.86. While the number is bonds is tightly related to credit spreads and a point estimate out of a roughly 20-year sample, durations. Many return predictors are also cor- its exact magnitude is less important than the fact related with credit spreads. As a consequence, that it is well above zero. if one just creates portfolios by sorting on these measures, the long-and-short portfolios will have We explore possible explanations for the very different risk profiles, making their expected observed return’s patterns. We examine both risk returns hard to compare and the long-high-short- explanations–characteristic portfolios expose the low portfolio far from market neutral. In the end, aggregate investor to losses at times in which contrary to what happens in equity long-and- those losses are particularly tough to bear–and short portfolios that end up with small market mispricing theories–investors deviate from ratio- exposures, simple long-and-short credit portfo- nality because of mistakes or agency problems lios do not. Furthermore, credit spread itself is a and limits-to-arbitrage stop arbitrageurs from return predictor so it is important to understand fully correcting these mistakes and their impact whether a candidate variable simply predicts on asset prices. returns because of its correlation with spreads or whether it has any extra forecasting power. To We examine the risk hypothesis with two tests. solve these general issues, we use a double sort on First we measure the exposure of each indi- an ex-ante measure of beta (duration times spread) vidual characteristic and a combination of all and the candidate characteristic. of them to traditional macroeconomic factors (e.g., Chen et al., 1986). While the coefficients Trading costs and liquidity are also very differ- are statistically significant, they suggest that the ent in credit markets relative to equity markets. characteristics have a hedging profile. That is, Corporate bonds are difficult to trade and the the returns of the combined portfolio are higher expected trading cost is high relative to the under- when growth expectations are lower, volatility lying volatility of the asset class (see, e.g., Harris, increases and inflation expectations increase. In 2015). Thus, simple analyses of Sharpe ratios the second test, we replace the traditional macroe- based on “academic” quintile long/short port- conomic factors with changes in broker–dealer folios may substantially overstate the economic leverage. Adrian et al. (2014) found that expo- significance of any characteristic. Thus, when sures to broker–dealer balance sheets can explain studying credit portfolios, we explicitly account equity anomalies as well as government bond Journal Of Investment Management Second Quarter 2018 Common Factors in Corporate Bond Returns 19 returns. We do not find evidence that broker– Finally, the last test focuses on the investor mis- dealer leverage can explain credit characteristic take hypothesis. We test whether the returns can returns. In particular, the combined portfolio, be explained by investors’ errors in forecasting exposed to all individual characteristics, has a sales (e.g., Bradshaw et al., 2001). To proxy for positive (hedging), but indistinguishable from investors’ expectations, which are unobservable, zero, loading on leverage shocks. we use equity analyst forecasts. If these forecasts were rational and unfettered by agency conflicts, We break down the drivers of mispricing into analyst revisions should not be predictable by (i) factors that influence the likelihood that noise public information. On the other hand, if they traders (Grossman and Stiglitz, 1980) are impor- underestimate the sales of firms with high scores tant for a given security; and (ii) factors that limit and overestimate the sales of those with low the activity of arbitrageurs (Shleifer and Vishny, scores, the correction of those expectations may 1997). Weproxy for the likelihood of noise traders explain the anomaly premium. The evidence is with (i) a measure of the investor base sophistica- in the right direction, statistically significant and tion (institutional ownership of the bond); and (ii) quantitatively important for momentum, but not a measure of firm transparency (analyst coverage for the remaining characteristics. Overall, returns of the issuing firm equity). For limits to arbitrage to the momentum characteristic seem to be the we measure liquidity (bond amount outstanding) most tightly linked to mispricing, with the evi- and ease of shorting (as reflected by the shorting dence being less clear about the source of the fee). returns of other characteristics. The remainder of this paper proceeds as fol- We run two tests using with those proxies. In lows. Section 2 discusses a simple framework the first test we examine whether the bonds most for corporate bond excess returns and links our attractive to an arbitrageur–those with extreme analysis to earlier papers exploring determinants values for the anomalies–are particularly hard of cross-sectional variation in corporate bond to arbitrage or are more vulnerable to investor’s expected excess returns. Section 3 explains our errors. For the shorting fee the test is one sided: data sources, sample-selection criteria, charac- are bonds that represent the most attractive short teristic measures and research design. Section 4 from the point of view of arbitrageurs–those with describes our empirical analyses and Section 5 unusually low anomaly scores–unusually hard to concludes. short? We do not find evidence of this pattern for any of the characteristics. 2 A framework for expected corporate bond excess returns In the second test we look at long-and-short port- folios built on security universes which differ in Unlike equity markets with variants of the divi- expected mispricing and limits to arbitrage. The dend discount model to guide empiricists in their hypothesis here is that anomaly returns should measurement of expected returns, there is not be stronger among the hard-to-arbitrage-or-high- an agreed upon framework for estimating excess error bonds. Momentum returns are indeed larger credit returns. Ex post, researchers agree that among harder-to-arbitrage-or-high-error bonds credit excess returns can, and should, be mea- and the result is statistically significant. The other sured as the difference between the returns to a anomalies perform similarly across the different corporate bond and an appropriately cash flow- universes. matched treasury bond (see, e.g., Hallerbach Second Quarter 2018 Journal Of Investment Management 20 Ronen Israel et al.
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