Technology has helped financial markets become more transparent by making more information available to the traders in real time. Traders are not only able to observe the most recent prices, but they can also observe best quotes and the depth available at the best quotes. However, it has a cost associated with it.
Traders with private information are at the losing end when other predatory traders infer information from the electronic limit order book. To avoid this problem, a trader can place a hidden order. Hidden trades are used extensively on the US stock exchanges. They are a way of submitting an order to buy or sell without making it visible to other traders. Although orders that are placed as hidden lose priority to displayed orders and face a higher risk of non-execution, there are several reasons why a trader may choose to place one.
If they have private information, they may not want to disclose their orders and lose the information advantage when other traders see their order. Also, if they want to buy (sell) a large quantity of stock, they may not want to disclose their order in fear of prices moving higher (lower) in reaction to their displayed order.
Another alternative is to use dark pool. Dark pool trading occurs off lit exchanges and the orders are internalised by the brokers. Dark pools have witnessed an increase in market share over the last decade. In a study by Carlens and Higgins (2016), they find that the dark trading has grown significantly in Europe (see Automated Trader, Issue 41, page 27). Kaya (2016) uses trend in trading on alternative trading systems (ATS) in the US to suggest that the role of dark pools is becoming increasingly significant.
Figure 01: Average daily % hidden order trades and % hidden order trade volume by stock exchange
Source: US Securities and Exchange Commission's Market Information Data Analytics System (MIDAS)
In this article, we present the findings of our study conducted on hidden liquidity within the US Stock Exchange (Jain & Jain, 2017). We found that the stocks with lower market capitalisation, lower turnover and higher volatility have both higher number of trades and higher trading volume executed against hidden orders. These results have implications for a trader in terms of choosing order type. Using data from 2012 to 2015, we found that 12.7% of trades execute against hidden orders, accounting for 14.2% of trade volume (Figure 01). We plot these numbers separately for each stock exchange. Boston Stock Exchange has the highest level of hidden order trades (22.4%) and Amex has the highest level of hidden order trade volume (34.9%). Traders placing orders on these exchanges are more likely to trade against hidden liquidity.
Figure 02: Average daily % hidden order trades and % hidden order trade volume by market capitalisation rank
Smaller stocks tend to have more information asymmetry because it is harder to find information about them than about bigger firms. Also, bigger firms are more actively followed by a higher number of analysts. Traders have a higher incentive to place hidden orders in an environment with higher information asymmetry in order to avoid adverse selection cost. To confirm this statement, in Figure 02, we plot these numbers separately by market capitalisation of each stock. It shows that the level of hidden order trading is higher in smaller stocks. Stocks in the smallest rank have the highest level of trades executing against hidden orders (27.6%), while stocks in the highest rank of market capitalisation have only 12.3% of trades executing against hidden orders.
Figure 03: Average daily % hidden order trades and % hidden order trade volume by turnover rank
If a stock is very actively traded, then the trader does not have to worry about hiding their orders as lit orders will be executed very quickly without the displayed order having an adverse effect on the execution price. Thus, we expect to see more hidden orders for less actively traded stocks where a large order is likely to move the prices significantly. To confirm this assumption, in Figure 3, we plot these numbers separately by turnover of each stock. We found that the level of hidden order trading is higher in stocks with lower turnover. Stocks in the lowest turnover category have a higher percentage of hidden order trades (21.9%) as opposed to stocks in the highest turnover category that have 15.2% hidden order trades. This suggests that traders are more likely to hide their orders in stocks that are less liquid.
Figure 04: Average daily % hidden order trades and % hidden order trade volume by volatility rank
If a stock is more volatile, then there is incentive for traders to place a hidden order because the prices can move quickly in reaction to their displayed order. Thus, we expect more hidden orders for stocks with higher volatility. To confirm this, in Figure 04, we plot these numbers separately by volatility of each stock. We found that the level of hidden order trading is higher in more volatile stocks. Stocks in the highest rank of volatility have the highest level of trades executing against hidden orders (17.0%), while stocks in the lowest rank of volatility have only 11.6% of trades executing against hidden orders. This suggests that traders are more likely to hide their orders in more volatile stocks.
Figure 05: Average % hidden order trades and % hidden order trade volume for a day by VIX rank
Next, we aggregate the hidden order trades and hidden order trade volume on a daily basis by taking the average across all stocks. We divide each day in five categories by VIX level on that day. In Figure 05, we plot average hidden order trades and hidden order trade volume separately by VIX rank of each day. We find that the level of hidden order trading is higher on days with lower VIX index. Thus, the aggregate level of hidden orders decreases with increase in aggregate market volatility.
A prior study by Chakrabarty and Shaw (2008) found that the level of hidden orders increases around the earnings announcement day because of increased levels of private information acquisition. We explored the changes in level of hidden liquidity around macroeconomic announcements such as the Consumer Price Index (CPI), the gross domestic product (GDP) and unemployment announcements. We found no evidence of an increased level of hidden orders around these announcements. This result suggests that it may not be feasible for traders to acquire private information about these macroeconomic announcements.
We found a significant level of hidden liquidity in limit order books of US stock exchanges. Traders place more hidden orders for smaller stocks, stocks with lower turnover and more volatile stocks. Boston Stock Exchange and NYSE MKT have the highest level of trades executing against hidden orders in their limit order book. We explored levels of hidden orders around macroeconomic announcements and found no evidence of increase. What makes traders decide to place displayed orders or hide their orders and face a risk of non-execution remains to be explored.
Carlens, H. & Higgins, D. (2016). The Future of Dark
Liquidiry in Europe. Automated Trader, (41), 27-31. Retrieved
Kaya, O. (2016). High Frequency Trading: Reaching the Limits. Automated Trader, (41), 23-26. Retrieved from automatedtrader.net/digital-editions/Q4_digital_2016/#/page/1
Jain, A. & Jain, C. (2017). Hidden Liquidity on the U.S. Stock Exchanges. The Journal Of Trading, 12(3), 30-36. http://dx.doi.org/10.3905/jot.2017.12.3.030
Chakrabarty, B. & Shaw K.W. (2008). Hidden Liquidity: Order Exposre Strategies Around Earnings Announcements. Journal of Business Finance and Accounting. (9-10), 1220-1244, 10.1111/j.1468-5957.2008.02111.x