Abstract: U.S. retail brokers have a “best execution” legal mandate in executing their clients’ orders, but the specific way in which best execution operates is unknown. Using data on retail order routing from three large brokers, we examine how they interact with wholesalers and establish three results. First, brokers allocate order flow based on past performance of wholesalers, though do so in various ways as they create future incentives. Second, wholesalers recognize that future order allocation depends on current performance and therefore, compete on price improvement. Finally, there are significant differences across stocks and brokers in how competition occurs.
Abstract: We study the informational value of trading networks in over-the-counter (OTC) markets. Using detailed transaction-level data from the corporate bond market, we show that investors with larger dealer trading networks make superior trading decisions before changes in credit fundamentals and yield better risk-adjusted performance. Our evidence indicates that an important mechanism for this result is that dealers reward their trading clients with private information. Consistent with this mechanism, we show that investors make superior trading decisions when they have trading relationships with dealers likely to have novel information. In addition, investors with trading relationships with deal-affiliated dealers transact more profitably before important merger and acquisition (M&A) deals are publicly announced. Collectively, our evidence highlights the importance of trading relationships for investors’ private information acquisition.
Abstract: This paper develops a new methodology for causal price impact in high-frequency financial markets to study a widespread form of market manipulation and its consequences. I identify directly from data when a trader takes both sides of the same transaction but instead of letting orders cross uses a compliance tool to prevent legal exposure. This functionality is offered by every major exchange and in US futures markets its default use option allows the tool to be exploited strategically. This form of self-trading can effectively signal demand at artificial prices and result in disproportionate liquidity removal from markets. I introduce a source of variation that generates systematic differences in information exposure to traders. This leverages an institutional feature of electronic limit order books where as-good-as random delays between when a trade happens and the market learns about it can be used to assign treatment. By comparing trades occurring almost at the same time facing an identical information set, except for the news about a reference trade, I implement an empirical approach that estimates dynamic responses robust to microstructure noise and confounders. My findings show that self-trading successfully moves prices in the direction that benefits the trader, both by making liquidity providers revise quotes and enticing others to trade. I then use these estimates to quantify the role of self-trading in flash events: brief moments of substantial price increases or declines. Using a causal attribution framework, I separate information shocks - price adjustments based on news - from manipulative price impact to be able to assess the role of each factor individually and in combination. I find that almost 10% of flash events in US futures markets are driven by attracting others to trade in the direction consistent with profitable self-trading.
