Crossing Networks and Dealer Markets: Competition and Performance

Abstract

This paper studies the interaction between dealer markets and a relatively new form of exchange, passive crossing networks, where buyers and sellers trade directly with one another. We find that the crossing network is characterized by both positive~"liquidity"! and negative~"crowding"! externalities, and we analyze the effects of its introduction on the dealer market. Traders who use the dealer market as a "market of last resort" can induce dealers to widen their spread and can lead to more efficient subsequent prices, but traders who only use the crossing network can provide a counterbalancing effect by reducing adverse selection and inventory holding costs.COMPETITION BETWEEN EXCHANGES for order f low is a growing phenomenon in financial markets. From London to Paris to Tel Aviv, exchanges and trading systems are introducing new trading mechanisms that compete for order f low. In the United States, the SEC promulgated new rules that redefine the regulation of Alternative Trading Systems and intensify the competition between existing exchanges and new electronic markets. Indeed, new electronic trading venues are cited as the reason for a decline in the value of seats on major exchanges, even though trading volumes are growing rapidly.What will be the impact of new trading mechanisms on market participants and on existing dealer markets~DMs!? In this paper we study the effect of introducing a passive call market that competes with an existing traditional DM. The DM is based on competing market makers as in Nasdaq, the London Stock Exchange, the Foreign Exchange market, and the U.S. government securities market. An important benefit provided by the DM is the assurance of immediate execution. An important disadvantage is the cost: the bid-ask spread, which can be substantial. Traders who do not place a high value on immediacy and assured execution can try to reduce their trading costs by searching for counterparties on their own. As computing and communication costs have declined, electronic communication networks have been increasingly deployed to reduce search costs, making it less costly for traders to find one another and reducing the demand for DMs.

Do Dark Pools Harm Price Discovery?

“…Consequently, the exchange spread S/σ shrinks, encouraging more informed traders to move from the dark pool to the exchange. This result is opposite to that of Hendershott and Mendelson (2000), where long-lived information implies that all informed traders use the dark pool, as well as a higher exchange spread. 27 As in Section 2, we use the signal-to-noise ratio, I(β, α e ) = (1 − β)µ I /(α e µ z ), and scaled root-mean-squared error (RMSE), E[(P 1 − v) 2 | v = σ]/σ, to measure price discovery at the end of period 1.…”

“…Finally, my results complement those of Hendershott and Mendelson (2000) (HM), who model the coexistence of a dealers' market (similar to the exchange in this paper) and a "crossing network" (similar to the dark pool in this paper). HM have the important insight that traders in dark pools impose positive ("liquidity-begets-liquidity") and negative ("crowding out") externalities on each other.…”

“…A shorter information horizon makes delay more appealing for liquidity traders, driving up exchange spread; therefore, informed traders would use the dark pools more aggressively in order to save on the spread. This prediction is opposite to that of Hendershott and Mendelson (2000), where a shorter information horizon means that all informed traders send orders to the exchange immediately, and that the exchange spread becomes narrower. This distinction can be tested empirically.…”

“…(If two venues give equal payoff to a trader, sending the entire order to one venue is also optimal.) While this result relies on the assumption of finite order size per capita, a finite order size is a standard assumption in existing models of dark pools, such as Hendershott and Mendelson (2000), Degryse, Van Achter, andWuyts (2009), andWerner (2011a), as well as the classic model of Glosten and Milgrom (1985) and extensions. A finite order size can also be viewed as a substitute for (unmodeled) credit limit or capital constraint.…”

“…In Case 1 of Proposition 1, for example, if an extra fraction > 0 of liquidity traders are perturbed into the dark pool, the dark-pool execution probability stays the same,r, because there are infinitely many infinitesimal traders. In the model of Hendershott and Mendelson (2000), a larger number of uninformed traders in the dark pool implies a higher execution probability because there are finitely many traders.…”