A new MIT study found that dark pools (private stock markets) increase the ratio of well-informed investors who participate in public stock markets.
A “dark pool” is not a mysterious source of untapped oil or hidden water well, it is a finance term. A dark pool is a privately run trading environment that does not reveal participants’ orders to the public before trades are completed.
Dark pools are becoming more prevalent in stock trading, and currently make up at least one eighth of all stock trading volume in America, and possibly much more.
But what effects do dark pools have on “price discovery”, i.e. the ongoing setting of prices on markets, which is believed to benefit from the transparency public exchanges offer?
According to a recent survey, 71% of finance professionals believe dark pools are ‘somewhat’ or ‘very’ problematic in establishing stock prices.
Dark pools might not be problematic in establishing stock prices
A new study, however, written by Haoxiang Zhu, a financial economist at the MIT Sloan School of Management, and to be published in the Review of Financial Studies, says that this might not necessarily be the case.
The study asserts that dark pools in the right circumstances actually help price discovery. Partly because they attract less-informed traders, as better-informed ones don’t stay long because they need to execute trades rapidly, they head back to the public exchanges to do business.
“The dark pool is like a screening device that siphons off uninformed traders. In the end, on the [public] exchange, you get left with a higher concentration of the informed traders who contribute to price discovery.”
Concerns about transparency in private exchanges
Dark pools, which started off in the 1980s, have grown rapidly during the last five years. According to a Securities and Exchange Commission 2009 study, there were then about 32 dark pools, some of them run by major financial institutions. They represented approximately 8% of stock trades.
In 2011, consulting firm The Tabb Group and brokerage firm Rosenblatt Securities, estimated that dark pools handled 12% of US trading volume.
As trading volumes have grown, so have concerns regarding transparency problems.
“The usual intuition is that dark pools harm price discovery in the public venues, because people who have information [might] go hide in the dark.”
Dark pools are less costly for investors
Investors are attracted to trading in a dark pool because of the ability to make transactions without moving the market. For example, a large institutional investor who wishes to buy some shares in a public company will be able to do it more cheaply in a private exchange, because in a public one everybody knows about it immediately, this creates an impact on the price that would make executing the investors’ remaining orders more expensive.
According to Zhu’s study, the risk for investors of not being able to execute transactions in a dark pool is the main factor that limits the harm they might do to price discovery on public exchanges.
In Zhu’s model, there are less-informed investors trading, for example, due to a need to re-balance a portfolio, and well-informed ones, acting on the basis of detailed knowledge about a stock.
If several well-informed investors come to the same conclusion about a company’s shares, say that their quarterly earnings will increase and that it is a good idea to buy, they will rush to the dark pools and try to buy shares.
However, those smart investors will discover liquidity problems in the dark pool, they gather on one side of the market, and there might not be enough less-informed investors willing to take the other side of the trade. The well-informed investors, who need to trade promptly, will rush back to the public stock exchanges in a greater proportion than the less-informed ones.
Zhu says “If [well-informed traders] do not get their orders filled, their information becomes stale. The aggregate information generating price discovery on the public stock exchanges will thus be more accurate on average when dark pools are part of the process. It is basically a signal-to-noise argument.”
Study based on a model of investor behavior
Zhu’s study is based on a model of investor behavior, i.e. a simulation. He also provides some caveats regarding his findings: For example, if dark pools use opaque rules, well-informed investors might not hurry back to the public exchanges as rapidly. Moreover, better price discovery can go hand-in-hand with worse liquidity, in the form of wider bid-ask spreads and higher price impacts on exchanges.
Still, as Zhu points out, “Modeling forces us to have discipline in interpreting the data.”
According to other scholars, Zhu’s study provides valuable insights into the potential effects of investor behavior.
Charles Jones, a professor of finance and economics at Columbia University, said “I think he’s captured the essence of these dark pools. [Such finance models] really help set up hypotheses [for future empirical testing].” Jones has conducted extensive empirical research on investor knowledge and behavior.
Maureen O’Hara, a professor of finance at Cornell University, says “[Zhu’s paper] makes a real contribution by highlighting that dark pools can improve market performance, and not degrade it as has been suggested by some. His research agenda going forward will provide important insights into these market structure issues.”