Understanding the Impacts of Dark Pools on Price Discovery

Abstract

This paper investigates the impact of dark pools on price discovery (the efficiency of prices on stock exchanges to aggregate information). Assets are traded in either an exchange or a dark pool, with the dark pool offering better prices but lower execution rates. Informed traders receive noisy and heterogeneous signals about an asset's fundamental. We find that informed traders use dark pools to mitigate their information risk and there is a sorting effect: in equilibrium, traders with strong signals trade in exchanges, traders with moderate signals trade in dark pools, and traders with weak signals do not trade. As a result, dark pools have an amplification effect on price discovery. That is, when information precision is high (information risk is low), the majority of informed traders trade in the exchange hence adding a dark pool enhances price discovery, whereas when information precision is low (information risk is high), the majority of the informed traders trade in the dark pool hence adding a dark pool impairs price discovery. The paper reconciles the conflicting empirical evidence and produces novel empirical predictions. The paper also provides regulatory suggestions with dark pools on current equity markets and in emerging markets. *

The Bright Side of Dark Markets: Experiments

“…ment is consistent with the predictions of both Zhu (2014) and Ye (2016). Although informed traders in our experiment do not have perfect information, when the information precision of signals is high, the strongly informed traders have close to perfect information.…”

“…On the other hand, liquidity orders are less correlated, are less likely to cluster on the heavy side of the market, and have higher execution probabilities in the dark pool. The sorting effect that we observe under high information precision environ- 30 We emphasize that while there are several similarities in the basic framework between our laboratory market and the theoretical formulation considered in Zhu (2014) and Ye (2016), like the existence of parallel lit and dark venues for trading, and both informed and uninformed traders being present in the market, there are other features of the models that are absent in our experimental markets. For example, the theory assumes immediate execution in the lit exchange guaranteed by a market maker, explicit delay costs for liquidity traders, differences in costs of information acquisition among informed traders, for-profit traders not acquiring information not trading, etc.…”

“…Table III displays the results of regressing the number of signals acquired in the market (S mkt. ) on the variable Dark (which takes a value of 1 if the treatment inlit exchange before the execution of the dark trade, and (3) negotiated pricing similar to the OTC trading where a pair of buyer and seller trade directly (Ye (2016)). In their study, Nimalendran and Ray (2014) find that while not all trades are at the mid-point of National Best Bid and Offer (NBBO), about 57% of transactions are within .01% of the price around the mid-point.…”

“…In contrast, Zhu (2014) demonstrates that adding a dark pool alongside an exchange results in the self-selection of venues by informed and uninformed traders, concentrates price-relevant information into the exchange, and improves price discovery. Ye (2016) further finds that, in equilibrium, traders with strong signals trade in exchanges, traders with moderate signals trade in dark pools, and traders with weak signals do not trade. As a result, whether adding a dark pool enhances or impairs price discovery depends on the information precision of signals in the market.…”