Dark Pool Trading: The Hidden Realms of Trading

Rindi and Werner (2017) show that the larger tick size causes spreads and market depth to increase. Albuquerque, Song, and Yao (2020) test the effect of the Tick Size Pilot Program on stock prices and show a decrease https://www.xcritical.com/ in stock prices following the Pilot. Li, Ye, and Zheng (2018) test the effect of tick size on corporate payout policies. Lee and Watts (2021) examine how an increase in tick size affects algorithmic trading and the price discovery process.

Financial reporting opacity and informed trading by international institutional investors

Some of the most popular independent dark pools are owned by Instinet, which is owned by Nomura, and Smartpool, which is owned by HSBC, JP Morgan, and BNP Paribas. In the public markets like the New York Stock Exchange (NYSE) and Nasdaq, such transactions are usually recorded and can have significant impacts on the market. High DPV suggests that the market is experiencing significant price fluctuations, which can pose risks and opportunities for traders. High DPP suggests that the trader has strong relationships with the dark pool dark pool participants and can access favorable pricing.

Dark Pool Trading Explained – How Do These Ambiguous Markets Work?

Because dark pools facilitate HFT, it can be argued that dark pools also increase market efficiency. Dark pools emerged in the 1980s when the Securities and Exchange Commission (SEC) allowed brokers to transact large blocks of shares. Electronic trading and an SEC ruling in 2005 that was designed to increase competition and cut transaction costs have stimulated an increase in the number of dark pools. Dark pools can charge lower fees than exchanges because they are often housed within a large firm and not necessarily a bank. In fact, dark pools are legal and fully regulated by the Securities and Exchange Commission. Dark pools allow traders to make block trades without having to publicize the buy/sell price or the number of shares traded to the public.

What are the benefits of Dark Pool Trading?

The lower transaction costs make lower-value information acquisition opportunities worth pursuing, which should increase information acquisition. Because of their sinister name and lack of transparency, dark pools are often considered by the public to be dubious enterprises. However, there is a real concern that because of the sheer volume of trades conducted on dark markets, the public values of certain securities are increasingly unreliable or inaccurate. There is also mounting concern that dark pool exchanges provide excellent fodder for predatory high-frequency trading. CFA Institute members have raised concerns that the incentive to display orders in public markets is being undermined by certain off-exchange trading practices.

Hidden liquidity: Some new light on dark trading

We directly identify a statistically significant decrease in information acquisition, as SEC EDGAR searches decrease along with decreases in dark pool trading. The second test aims to pin down whether the acquired information is marketwide or firm specific. We find that dark pool trading encourages the acquisition of firm-specific information.

Informed liquidity provision in a limit order market

They are be factored into the overall market price of a stock since dark pool trades are not reported to public exchanges, which lead to discrepancies between the public exchange price and the true market price. These are private exchanges operated by large broker-dealers, where institutional investors can anonymously trade large blocks of securities. ECNs are computerized trading systems that match buyers and sellers anonymously. With trades scattered across public and private venues, there is a risk that the public exchanges might lose enough trading volume, potentially reducing the quality of publicly available price information.

Trade Every Market in One Place

• Then, the seller company would need to sell these stocks in several batches of 100,000 shares each, or even less, depending on the market conditions.
• Conflict of interest and front running are the major private market pressures that concern large corporations and other investors in dark pools.
• Because large HFT orders had to be spread among multiple exchanges, it alerted trading competitors who could then get in front of the order and snatch up the inventory, driving up share prices.
• Dark pools emerged in the 1980s when the Securities and Exchange Commission (SEC) allowed brokers to transact large blocks of shares.
• Public stock exchange operators point out that off-exchange trading creates an unfair price advantage for institutional traders who might also own a significant share in the public market.
• The NBBO is a quoting method that consolidates the highest bid price and the lowest asking price from various exchanges and trading systems.

Among the most dramatic changes in financial markets in the last few decades is the rise of dark venues. Dark venues, or dark pools, are equity trading venues in which traders buy and sell stocks without publicly displaying their orders. The market share of dark pools in the United States grew from 7.5% in 2008 to 12.6% in 2015 (Rosenblatt Securities 2021). In the realm of theoretical studies, a considerable body of research focuses on non-fragmented markets. Notably, two pioneering works on price discovery by Glosten and Milgrom (1985), and Kyle (1985) lay the foundation for understanding market dynamics. Additionally, there are studies that specifically investigate fragmented lit markets, such as Pagano (1989), Chowdhry and Nanda (1991), Hasbrouck (1995), Viswanathan and Wang (2002), and Bloomfield et al. (2015).

My paper is also related but divergent from Ye (2011), Brolley (2019), and Bayona et al. (2023). While these studies touch upon similar topics, there are notable differences in my approach. Specifically, my model allows for the free selection of traders, whereas Ye (2011) assumes that uninformed traders do not have the freedom to choose between different venues. Brolley (2019) and Bayona et al. (2023) consider a sequential trading game that contains a dark pool and a limit order market. Unlike my model, they assume either an exogenous execution risk in dark pools or an exogenous price impact, or a non-rationing mechanism in dark pools.

Dynamic order submission strategies with competition between a dealer market and a crossing network

Conversely, when information precision is low, the majority of informed traders receive more modest signals and thus favor crossing networks. In this scenario, the crossing network attracts a higher fraction of informed traders relative to the liquidity traders, leading to a reduced informed-to-uninformed trader ratio within the exchange. In summary, higher information precision results in crossing networks primarily attracting liquidity traders, thereby improving price discovery in the exchange. Conversely, lower information precision leads to crossing networks drawing a larger proportion of informed traders, resulting in a diminished informed-to-uninformed trader ratio and a subsequent decline in price discovery within the exchange.

Firstly, let’s clarify that despite retail traders being excluded from directly trading in Dark Pools, there are a few ways they can gain access to them “indirectly”. Some of these types of pools are owned by famous stock exchange marketplaces like the NYSE’s Euronext and BATS, owned by the  Chicago Board of Trade. Broker-dealer-owned Dark Pools provide access to a wider range of financial products, unbiased advice, and no conflicts of interest.

The primary advantage of dark pool trading is that institutional investors making large trades can do so without exposure while finding buyers and sellers. If it were public knowledge, for example, that an investment bank was trying to sell 500,000 shares of a security, the security would almost certainly have decreased in value by the time the bank found buyers for all of their shares. Devaluation has become an increasingly likely risk, and electronic trading platforms are causing prices to respond much more quickly to market pressures. If the new data is reported only after the trade has been executed, however, the news has much less of an impact on the market. First, it adds to the emerging literature on the consequences of dark pool trading. For example, Hatheway, Kwan, and Zhen (2017) shows that less dark pool trading improves informational efficiency.

Exchanges like the NYSE, as they fight to stem market share loss, cite this as a reason that dark pools are not as compelling as they once were. We obtain dark pool trading volume for common stocks listed on the NYSE, NYSE MKT, and NASDAQ (Center for Research in Security Prices, or CRSP, share codes 10 and 11; exchange codes 1, 2, and 3) from FINRA from July 2014 to December 2017. The sample period spans September 2014 through December 2017 to allow for a three month measurement window for quarterly measures of dark pool trading. Moreover, one major issue brought to light in recent years is the impact of high-frequency traders (HFTs) on dark pool trading activity. When HFTs notice dark pool activity, they may cancel their orders in the public market, leading to increased price volatility and reduced liquidity. In fact, some banks, such as Barclays, have been accused of wrongdoing in their dealings with institutional orders.

In 2016, a large firm paid $70 million in fines for misleading investors and overriding their dark pool’s surveillance tools. A dark pool is an alternative trading system (ATS) that allows large buyers and sellers to execute orders without moving the market significantly. Dark pools solve the problem by hiding these transactions from the overall market.