Technology transformed the trading of financial assets but has been slower to come to corporate bond trading. Combining proprietary data from MarketAxess with regulatory TRACE data, we investigate how electronic request for quote (RFQ) trading affects bond dealers and trading more generally. We demonstrate that electronic trading remains fairly small and segmented, but has wide-ranging effects on transaction costs and execution quality in both electronic and voice trading, and the interdealer market. We identify features particular to bond markets that have and could continue to limit electronic bond trading growth. We provide an intriguing portrait of a market in transition.

We examine the microstructure of liquidity provision in the COVID-19 corporate bond liquidity crisis. During the two weeks leading up to Federal Reserve System interventions, volume shifted to liquid securities, transaction costs soared, trade-size pricing inverted, and dealers, particularly non-primary dealers, shifted from buying to selling, causing dealers’ inventories to plummet. Liquidity provisions in electronic customer-to-customer trading increased, though at prohibitively high costs. By improving dealer funding conditions and providing a liquidity backstop, the Primary Dealer Credit Facility and the Secondary Market Corporate Credit Facility (SMCCF) stabilized trading conditions. Most of the impact of SMCCF on bond liquidity seems to have materialized following its announcement. We argue that the Federal Reserve's actions reflect a new role as market maker of last resort.

We empirically examine the impact of industry exchange-traded funds (IETFs) on informed trading and market efficiency. We find that IETF short interest spikes simultaneously with hedge fund holdings on the member stock before positive earnings surprises, reflecting long-the-stock/short-the-ETF activity. This pattern is stronger among stocks with high industry risk exposure. A difference-in-difference analysis on the ETF inception event shows that IETFs reduce post-earnings-announcement drift more among stocks with high industry risk exposure, suggesting that IETFs improve market efficiency. We also find that the short interest ratio of IETFs positively predicts IETF returns, consistent with the hedging role of IETFs.

We investigate the new reality of exchange-traded funds (ETFs). We show that most ETFs are active investments in form (designed to generate alpha) or function (serve as building blocks of active portfolios). We define a new activeness index to capture these dimensions, finding that the cross-section of ETFs is now increasingly characterized by highly active investment vehicles. Active-in-form ETFs have positive flow-performance sensitivity, charge the highest fees among ETFs, and have high within-portfolio turnover. Active-in-function ETFs have more concentrated holdings, less within-portfolio turnover, but higher turnover in the secondary market. We show how more active ETFs are gaining market share over less active ETFs, leading to competitive fee pressure both within the ETF space and across the investment management industry. We suggest that the growing activeness of ETFs may assuage concerns about ETFs harming price discovery.

Understanding modern market microstructure phenomena requires large amounts of data and advanced mathematical tools. We demonstrate how machine learning can be applied to microstructural research. We find that microstructure measures continue to provide insights into the price process in current complex markets. Some microstructure features with high explanatory power exhibit low predictive power, while others with less explanatory power have more predictive power. We find that some microstructure-based measures are useful for out-of-sample prediction of various market statistics, leading to questions about market efficiency. We also show how microstructure measures can have important cross-asset effects. Our results are derived using 87 liquid futures contracts across all asset classes.