Algos in corporate bond markets

This study investigates the effect of algorithm trading in the corporate bond market. The advancement in computer algorithm improves trading quality in two major channels: faster order submission and lower transactions cost. However, prior studies rarely distinguish these two channels. Corporate bond market provides an ideal setting to study the latter channel since trading is done in an over-the-counter market without a consolidated limit order book. Thus, this study provides a unique setting to study the effect of algorithm trading on transaction cost and liquidity as follows: First, we examine the time-series variation in transactions cost in the corporate bond market. We expect that the transaction cost to be decreasing over time and the decrease is particularly pronounced after the sharp increase in algorithm trading in the corporate bond market since 2016. Because machines are known to oust human traders in the corporate bond market, especially for odd-lot trades (i.e., trade sizes of less than $1 million), we expect the reduction in transaction cost to be larger for smaller sized trades in the corporate bond market. Next, we use TRACE--an enhanced dataset which can identity individual dealer with a masked ID--to sort dealers based on their participation in algorithm trading for odd-lot trades. Because algorithm trading is a main driver of the significant increase in odd-lot trading, we expect the dealers who involve heavily in odd-lot trades to be more likely to be users of algorithms. In a cross section test, we examine whether transactions cost is significantly lower for dealers who use algorithm trading than those who do not. We also conjecture that dealers who participate heavily in odd-lot trades also provide more liquidity in the corporate bond market in the recent periods. Last, because transaction cost for odd-lot trades has dropped significantly in the recent periods and traders can split large orders easily, we expect that Feldhutter’s (2012) measure of selling pressure is a less accurate measure. Feldhutter (2012) uses differences in large versus small orders to measure selling pressure. In equity markets, large institutional trades can be easily broken up into smaller trade sizes. Similarly, because smaller orders are easier (and less expensive) to trade with the rise of algorithm trading in the corporate bond market, we investigate an alternative measure of selling pressure by incorporating information on aggregate order flow from a large individual dealer than just using the price of each order.