VOLUME IN DARK POOLS
by Mark Ready, Jeffrey Diermeier Professor of Finance, University of Wisconsin.
Today’s markets are certainly complicated and fragmented, so it might sound appealing to ask regulators to try to impose more consolidation. Before deciding to go down this path, however, it is important to understand whether and how the variation in market structures provides services to different market participants. In my paper “Determinants of Volume in Dark Pools,” I investigate the factors that determine institutional traders’ use of three market centers: Liquidnet, POSIT and Pipeline.
These three market centers are called “dark pools” because traders do not publicly reveal their orders in advance. All three systems rely on quotes from the rest of the market to determine execution prices. What makes these three market centers different from other dark pools is that they are primarily venues where buy-side traders can trade directly with each other. For the most part, these systems exclude sell-side firms, although Pipeline is connected to Lava Trading, which is owned by Citigroup and is open to sell-side firms. In contrast, the reported volumes from some large dark pools such as SIGMA X (owned by Goldman Sachs) include many different types of trades, including much of Goldman’s traditional block trading business.
The term “dark pool” certainly sounds scary, and regulators have recently raised several concerns. One of the concerns is that some dark pools, although none of the three that I study, show “indications of interest” to their member firms, effectively advertising a commitment to trade at the inside of the current quoted spread. The SEC is concerned that this gives an unfair advantage to member firms, and in November 2009, the SEC proposed rules that would require these IOI’s to be included in the public quotes. Interestingly, most dark pools stopped using these IOI’s shortly after the rules were proposed. Another concern is market fragmentation with dark pools is that they harm price discovery, because they divert volume from the exchanges but then use the quotes from the exchanges to determine trade prices. The exchange quotes are set by potential liquidity providers, and these liquidity providers have less incentive to quote aggressively because they are less likely to capture a trade when some of the potential order flow is diverted to other venues.
So what is good about dark pools? The main point is that they may help institutions reduce their trading costs. In the past, institutional traders largely relied on one or more intermediaries, including market makers and sell-side brokers. Of course, intermediaries must be compensated for providing their services, and this compensation is reflected in some combination of commissions and market impact cost. Along with these direct costs, institutions worry that the intermediaries may not carefully guard the information associated with their orders. Some opportunistic traders attempt to use “order anticipation” strategies, which means that they hope to trade in advance of the institutional orders and in the same direction. Given these potential costs, it is not surprising that institutions are interested in finding ways to bypass market intermediaries and trade directly with one another.
Given the apparent benefits from using Liquidnet, POSIT or Pipeline, it is reasonable to ask why they are not used exclusively for all institutional trading. The first obvious answer is that for trading to occur, the counterparties must enter their orders in the system at the same time, and when both buyers and sellers are present, the maximum volume is the smaller of the total buying and the total selling interest. The second answer is the focus of this paper: Sometimes it may not be optimal to use the system. To investigate the factors driving the traders’ choice to use the three venues, I use a sample of quarterly volumes by stock for each of these venues. Obviously, the most important determinant of volume in any one of these venues will be the level of institutional trading during that period. To measure institutional trading, I use the trades from the institutions captured in the Ancerno database (formerly Abel/Noser) and I use changes in quarterly institutional holdings (13-F reports) to adjust for the fact that the Ancerno database does not cover all institutions.
Use of these systems generally entails waiting, at least if the trader wants to get a substantial probability of an execution. This waiting can be costly if the price moves unfavorably. Thus, depending on the characteristics of the stock or market conditions, traders may sometimes prefer other strategies to get faster executions. Consistent with this idea, I show that institutional traders are more likely to use the three dark pools when the percentage spreads are high or the total dollar spreads are high, and less likely to use the three dark pools when the stock has relatively high price volatility. My results also suggest that institutional traders tend to favor ECNs (which would give faster executions at certain prices) for stocks with relatively high volatility.
I also show that institutional traders appear to be less likely to use the three dark pools for orders in stocks like Microsoft with very low spreads per share, even after controlling for the percentage spreads and dollar spreads associated with the orders. In addition, the traders do not appear to be using ECNs as an alternative for these orders. I think these results suggest that stocks like Microsoft are used to satisfy soft dollar agreements. These agreements generally require that a pre-specified number of shares be executed with a particular broker over a quarter. If the institutional trader worries that the soft-dollar broker may give relatively poor execution prices, then it makes sense to send orders in stocks like Microsoft which can be executed at favorable prices with little skill.
In summary, my results are consistent with traders attempting to use the dark pools to save transaction costs. If my soft-dollar explanation is correct, then the tendency to send stocks like Microsoft elsewhere may raise some regulatory concerns, because it suggests that the soft dollar agreements may entail some hidden costs in the form of worse execution prices. On the other hand the story also suggests that institutional traders are attempting to minimize these costs, and given that even unskilled brokers should be able to provide reasonable results for stocks such as Microsoft, the resulting costs might be very small.