Bond timing

The Excess Bond Premium (EBP – the portion of corporate bond spreads not explained by default risk), a key metric in quantitative finance for gauging credit spreads, has long been a subject of intense scrutiny. Recent research sheds new light on its dynamics, moving beyond traditional macroeconomic factors to explore the role of information flow. By analyzing news attention across 180 topics, a significant portion of the EBP’s variation can be explained, offering a novel lens to understand its fluctuations and predictive power.

Inflation significantly impacts the prices of gold and treasury bonds through various mechanisms. Gold is often viewed as a hedge against inflation, while treasury bonds exhibit a more complex relationship influenced by interest rates and investor behavior. This relationship between inflation, gold, and treasuries is well understood, but the real question is whether we can systematically capitalize on it. In this article, we explore how inflation data can be used to build trading strategies—and as our findings suggest, the answer is a definite yes.

High-yield bond ETFs represent a unique financial vehicle: they are highly liquid instruments that hold inherently illiquid securities, creating a fertile ground for predictable market behaviors. Our latest research uncovers an intriguing anomaly within these ETFs, similar to those observed in the stock market: overnight returns are systematically higher than intraday returns. This overnight anomaly in high-yield bonds is not only prevalent but also exhibits a distinct seasonal pattern, primarily from Monday’s close to Tuesday’s open and from Tuesday’s close to Wednesday’s open. Additionally, this anomaly displays a reversal characteristic, where overnight performance is typically more robust following a negative close-to-close performance in the preceding period. These findings reveal potential opportunities for trading strategies that leverage these consistent overnight return patterns, offering new insights into high-yield bond trading dynamics.

There exist two most common techniques for constructing multifactor portfolios. The mixing approach creates single-factor portfolios and then invests proportionally in each to build a multifactor portfolio. The integrated approach combines single-factor signals into a multifactor signal and then constructs a multifactor portfolio based on that multifactor signal. Which methodology is better? It is hard to tell, and numerous papers show each method’s pros and cons. The recent paper from Joris Blonk and Philip Messow explores this question from the standpoint of the credit fixed-income portfolio manager and offers their analysis, which shows that an integrated approach is probably better in this particular asset class.

The replication crisis in social sciences (and, of course, finance) is an often covered topic (see also our articles How do Investment Strategies Perform After Publication and In-Sample vs. Out-of-Sample Analysis of Trading Strategies). In vs. out-of-sample tests are usually performed on equity factors as data are available. However, the Copenhagen Business Schools, in close cooperation with AQR Capital Management, went in a different direction and built a database of realistic corporate bond data and took a closer look at the precision of corporate bonds forecasting methodologies. We applaud them for that, as working with the corporate bond data is challenging, and their work sheds a little light on this important part of the financial markets.

Exchange-traded funds (ETFs) have been recently booming in popularity and enjoy great praise for their flexibility and accessibility in terms of liquidity. They allow investors convenient exposure to less liquid assets such as corporate bonds. But liquid ETF instrument based on illiquid assets is a recipe for a lot of hidden problems (and sometimes disasters), especially in such a turbulent period on fixed income markets as it’s now. There are various certain specifics which come with creation of new ETFs and problems for buying of underling prospects to match the fund’s NAV. Chris Reilly’s paper (2022) revolves around the point that ETF managers encourage Authorized Participants (APs) to more aggressively arbitrage tracking errors to the benefit of ETF investors while simultaneously allowing APs to interact strategically with ETF portfolios at the expense of ETF investors. Underlying asset liquidity is a first-order determinant of optimal security design for ETFs. While these ETFs do underperform their benchmark by greater than their stated net expense ratios (as much as claimed 48 bps p.a.), they still offer a liquid alternative for investors that do not have the resources to manage their own fixed income portfolio. This summary could be taken as a good reminder that investors’ expenses to obtain liquidity in the fixed income space are often quite substantial.