The Role of Dark Pools in Financial Markets

Dark pools are private exchanges for securities, not easily accessible to the public and lacking the transparency that public exchanges provide. Dark pools emerged in the late 1980s, and have grown in popularity from 7.51% of trading volume in 2008 to 16.57% in 2015. They originally came into existence to allow institutional investors to execute large trade orders without adversely impacting the market. However the exclusivity of dark pools has been decreasing since 2011, evidenced by converging average trading size of dark pools and exchanges. According to an industry insider in Rosenblatt Securities, Inc., “it can be assumed that most pools are open to most investors connecting to the pool.” In this post, I will primarily summarize a paper by Linlin Ye published in 2016 that investigates the impact of dark pools on price discovery, or how accurately price reflects asset value.

Because dark pools lack transparency, trades executed in it do not impact price discovery. Price discovery is the process of finding the point where supply and demand curves meet, or the price in which buyers are willing to buy and sellers are willing to sell. Price discovery occurs in public exchanges where traders buy and sell assets, and is important for ensuring efficiency of capital markets. It gives confidence to investors and benefits the public by creating an efficient capital market. Given that price discovery is important for ensuring market efficiency and that it does not occur in dark pools, you might be concerned about the growing prevalence of trading in dark pools.

In the paper, Ye shows that in equilibrium there is a sorting effect for both informed traders and liquidity traders. An informed trader is one who is taking a bet on the direction of a price move such as hedge funds or insiders, whereas a liquidity trader profits from the spread between the buying and selling price without gambling on the direction of a price move. Examples of liquidity traders include market makers at big dealing desks or high frequency traders. Informed traders with the strongest signals trade in public exchanges, where traders with weaker signals trade in the dark pool. This is because traders with strong signals are very confident about making profit and prefer higher execution to a better price. For the uninformed liquidity traders, those with high liquidity demand trade in the public exchange and those with lower liquidity demand trade in the dark pool. The liquidity traders sort in this way because they are more sensitive to price than the likelihood of execution. This sorting effect is derived from the trade-off of dark pools: dark pools provide better prices than exchanges but this is offset by a higher probability of non-execution, or inability to trade.

When participants’ information is precise, the majority of informed traders receive strong signals and prefer the public exchange. In this case, the dark pool only attracts a small fraction of informed traders leading a higher ratio of informed traders relative to uninformed traders. When precision is low, the dark pool attracts a higher proportion of informed traders compared to liquidity traders, leaving a lower ratio of informed to uniformed traders in the public exchange. Ye’s main finding is that the existence of a dark pool alongside a public exchange results in an amplification effect, where dark pools enhance price discovery when precision is high and impair discovery when it is low. In other words, dark pools increase price discovery for larger, established firms with large analyst followings, and regulators should be cautious in controlling dark pool trading for these firms. However, traders generally possess less accurate information for high R&D firms, young firms, small firms and less analyzed firms and introducing dark pools to these firms might cause a decrease in price discovery.

SOURCES:

Background Information about Dark Pools

Understanding the Impacts of Dark Pools on Price Discovery