Hidden Liquidity Research Articles

Hidden liquidity, market quality, and order submission strategies

Using exogenous changes in hidden liquidity from the Tick Size Pilot Program, we show that hidden liquidity on lit venues has significant effects on various measures of market quality and order submission strategies after controlling for the impact of dark pools and stock attributes. We find that spreads, depths, trading volume, and trade size increase with hidden liquidity. The extent of informed trading and the price impact of a trade also increase with hidden liquidity on lit venues. Traders use more inside-the-quote limit orders, while algorithmic traders engage in more pinging activities and raise message traffic with more hidden liquidity.

The impact of reporting changes on hidden liquidity: Evidence from the Chicago stock exchange

This paper analyzes the NYSE Chicago's move to the Pillar integrated trading platform in November 2019. Following the move to Pillar, reported trading volume increases significantly for both equities and exchange traded funds (ETFs). The increase in trading volume and volume share in the NYSE Chicago is largely driven by trades against hidden liquidity. This increase in hidden liquidity is economically significant and is largely determined by the reporting of qualified contingent trades, QCTs. Although these trades were reported in the NYSE's Daily Trades and Quotes (DTAQ) files, these trades were not reported to the SEC's MIDAS database until to the move to Pillar. This paper shows the cross trade volume in the CHX comprises a significant amount of hidden volume in the U.S.

Adaptive Trading Strategies Across Liquidity Pools

In this paper, we provide a flexible framework for optimal trading in an asset listed on different venues. We take into account the dependencies between the imbalance and spread of the venues, and allow for partial execution of limit orders at different limits as well as market orders. We present a Bayesian update of the model parameters to take into account possibly changing market conditions and propose extensions to include short/long trading signals, market impact or hidden liquidity. To solve the stochastic control problem of the trader we apply the finite difference method and also develop a deep reinforcement learning algorithm allowing to consider more complex settings.

Hidden Liquidity, Market Quality, and Order Submission Strategies

Hidden Liquidity, Market Quality, and Order Submission Strategies

COVID-19 and market structure dynamics

We examine the impact of COVID-19 on market structure in the U.S. Specifically, we analyze the impact of both the COVID-19-induced market uncertainty period as well as the suspension of the NYSE floor on trading dynamics such as market fragmentation, algorithmic trading, and hidden liquidity in the market. During both the heightened market uncertainty and NYSE floor suspension periods, we find a significant increase in hidden liquidity yet significant decreases in both algorithmic trading and market fragmentation. However, despite withdrawing from the market during this period, remaining algorithmic traders appear to improve market quality. Our results indicate that COVID-19 had a significant impact on order routing, pre-trade transparency, and automated trading.

Hidden Liquidity, Market Quality, and Order Submission Strategies

Using a recent pilot program as an exogenous shock to hidden liquidity, we show that hidden liquidity has significant effects on various measures of market quality and order submission strategies after controlling for market volatility and other stock attributes. Spreads, depths, trading volume, and trade size increase with hidden liquidity. The extent of informed trading and the price impact of a trade also increase with hidden liquidity. Traders use more inside-the-quote limit orders, and algorithmic traders engage in more pinging activities and raise message traffic when there is more hidden liquidity.

Order Exposure and Liquidity Coordination: Does Hidden Liquidity Harm Price Efficiency?

We show that the excessive use of hidden orders causes artificial price pressures and abnormal asset returns. Using a simple game-theoretical setting, we demonstrate that this effect naturally arises from mis-coordination in trading schedules between traders, when suppliers of liquidity do not sufficiently disclose their trade intentions. As a result, hidden liquidity can increase trading costs and induce excess price fluctuations unrelated to information. Using NASDAQ order book data, we find strong empirical support and illustrate that hidden liquidity is higher if bid–ask spreads are smaller and relative tick sizes are higher.

Flashes of Trading Intent at NASDAQ

AbstractWe use the introduction and subsequent removal of the flash-order functionality from NASDAQ as a natural experiment to investigate the impact of voluntary disclosure of trading intent on market quality. We find that flash orders significantly improve liquidity in NASDAQ. Furthermore, overall market quality improves (deteriorates) when flash functionality is introduced (removed). This result can be attributed to increased competition among liquidity suppliers across competing trading venues. Alternatively, flash orders attract responses from reactive traders immediately after the announcement, attracting more “hidden liquidity” and lowering risk-bearing costs for the overall market.

Hidden Liquidity: Some New Light on Dark Trading

ABSTRACTUsing a laboratory market, we investigate how the ability to hide orders affects traders’ strategies and market outcomes in a limit order book environment. We find that order strategies are greatly affected by allowing hidden liquidity, with traders substituting nondisplayed for displayed shares and changing the aggressiveness of their trading. As traders adapt their behavior to the different opacity regimes, however, most aggregate market outcomes (such as liquidity and informational efficiency) are not affected as much. We also find that opacity appears to increase the profits of informed traders but only when their private information is very valuable.

The impact of visible and dark orders

Using publicly available market data we construct a three-component order flow imbalance vector that represents executions against displayed quotes, executions against dark liquidity and the net imbalance in the flow of limit orders. The main contribution of this paper is empirical evidence of how these different components of order flow affect returns. We find that market-order executions against hidden liquidity have the smallest impact on stock returns. In contrast, market executions against displayed liquidity provide the highest pressure on market returns, followed by the price impact from the net limit order flow. Drawing from these results, we define a scalar imbalance that correlates to stock returns more closely than the traditional Lee-Ready imbalance. We also provide empirical evidence that price impact is a nonlinear concave function of the scalar imbalance.

Commonality on Euronext: Do location and account type matter?

Using a rich dataset of orders and trades for a sample of stocks listed on four Euronext markets, we apply principal component analysis and provide evidence on the existence and magnitude of commonality in returns, order flow and liquidity. We show that commonality in order flow mainly comes from foreign market members acting for their own account. Proprietary trading is a major driver in trade imbalance and return commonality. Next, we provide evidence on commonality in hidden liquidity. In contrast to commonality in visible depth that is the strongest for large firms, comovements in hidden depth seem to be stronger for small caps. We also show that commonality in returns, order flow and liquidity is not constant throughout the day. The opening of US markets is a key moment where commonality often reaches its maximum level. These findings suggest that most of the commonality is driven by foreigners, generating an increase in systematic liquidity risk, due to foreigners' similar trading behaviors, whose importance evolves throughout the day.

Hidden Liquidity Inside the Spread

Using a sample of 300 NYSE listed securities and excluding trade reporting facility trades, we examine the impact of order executions inside of the posted spread on market centers, an inside the spread trade(IST). The posted spread on a market center may differ from the National Best Bid and Offer Quote (NBBO). IST volume represents a substantial portion of daily volume executed at market centers, averaging 14%. However, only 51% of these trades improve on NBBO prices. Although the volume is substantial, cost savings from IST’s is small, averaging $132 per stock day. We find evidence that hidden liquidity inside the spread tends to act as a loss leader, drawing additional trades after the successful execution of liquidity inside the spread. When an exchange’s quotes are competitive with the NBBO, the probability of observing an IST is lower but when intermarket competition is high the probability of observing an IST increases. We find that IST’s are generally uninformed. Our results are robust to firm size and trading intensity.

Canary in a Coal Mine? One-Share Orders and Trades

1-share trades are the most common odd lot trade size, accounting for 9.62% of all odd lot transactions and 3.65% of all trades on NASDAQ in 2012. While 50.41% of 1-share trades result from broken orders, 34.89% of 1-share trades are intentional. We provide evidence that traders use 1-share trades to “ping” for hidden liquidity. In particular, our results indicate that 1-share trades are disproportionately aggressive and also execute against hidden liquidity more than any other odd lot trade size. We also find a relative increase in trading immediately following a 1-share trade. Our results support Clark-Joseph (2014), who suggests that traders may use small, unprofitable trades to detect information from other traders. Specifically, 1-share trades represent the minimum cash outlay necessary to trade, while simultaneously producing the smallest possible effects on a market maker’s inventory, and in turn, a security’s price.

Hidden Liquidity: Some New Light on Dark Trading

Hidden Liquidity: Some New Light on Dark Trading

Hidden Liquidity: Some New Light on Dark Trading

We use a laboratory market to investigate how the ability to hide orders affects traders’ strategies and market outcomes in a limit order book environment. We find that order strategies are greatly affected by allowing hidden liquidity, with traders substituting non-displayed for displayed shares and changing the aggressiveness of their trading. As traders adapt their behavior to the different opacity regimes, however, most aggregate market outcomes (such as liquidity and informational efficiency) are not affected as much. We also find that opacity appears to increase the profits of informed traders but only when their private information is very valuable.

Optimal Display of Iceberg Orders

We develop a sequential trade model of Iceberg order execution in a limit order book. The Iceberg-trader has the freedom to expose his trading intentions or (partially) shield the true order size against other market participants. Order exposure can cause drastic market reactions (“market impact†) in the end leading to higher transaction costs. On the other hand the Iceberg trader faces a loss-in-priority when he hides his intentions, as most electronic limit order books penalize the usage of hidden liquidity. Thus the Iceberg-trader is faced with the problem to find the right trade-off. Our model provides optimal exposure strategies for Iceberg traders in limit order book markets. In particular, we provide a range of analytical statements that are in line with recent empirical findings on the determinants of trader’s exposure strategies. In this framework, we also study the market impact also market impact of limit orders. We provide optimal exposure profiles for a range of high- tech stocks from the US S&P500 and how they scale with the state-of-the-book. We finally test the Iceberg’s performance against the limit orders and find that Iceberg orders can significantly enhance trade performance by up to 60%.

Sunshine Trading: Flashes of Trading Intent at the NASDAQ

We use the introduction and subsequent removal of the flash order functionality from NASDAQ as a natural experiment to investigate the impact of voluntary disclosure of trading intent on market quality. We find that flash orders significantly improve in NASDAQ. Furthermore, overall market quality improves (deteriorates) when the flash functionality is introduced (removed). This result can be attributed to increased competition among suppliers across competing trading venues. Alternatively, flash orders attract responses from reactive traders immediately after the announcement, attracting more \hidden liquidity and lowering risk-bearing costs for the overall market.

What Characterizes Commonality on European NYSE Euronext Markets?

Using a rich dataset of orders and trades for a sample of stocks listed on the four European markets of NYSE Euronext, we apply principal component analysis and provide evidence on the existence and magnitude of commonality in returns, order flow and liquidity. We show that commonality in order flow mainly comes from foreign market members acting for their own account. Proprietary trading is a major driver in trade imbalance and return commonality. Next, we provide evidence on commonality in hidden liquidity. In contrast to commonality in visible depth that is the strongest for large firms, co-movements in hidden depth seem to be stronger for small caps. We also show that commonality in returns, order flow and liquidity is not constant throughout the day. The opening of US markets is a key moment where commonality often reaches its maximum level.

Pre-Trade Transparency and Informed Trading: An Experimental Approach to Hidden Liquidity

We use experimental asset markets to analyze trading under different transparency and information settings. We find that both liquidity and informed traders use undisclosed orders to compete for liquidity provision. In opaque markets, traders increase aggressiveness to improve execution probability. Without information friction, opacity enhances liquidity, especially toward the end of trading, and is beneficial for liquidity traders. Under informed trading, adverse selection drives market outcomes mainly around news announcements. Monopolistic insiders exploit opacity at the expense of large liquidity traders. Opacity does not affect informational efficiency with a monopolistic insider, but value discovery is faster with shared informational rents.

Relating Market Impact to Aggregate Order Flow: The Role of Supply and Demand in Explaining Concavity and Order Flow Dynamics

We investigate how the dynamics of supply and demand affect the relationship between aggregated order flow and returns/market impact. We classify order flow according to the different event types that comprise it: Limit orders, cancelations and market orders executed against displayed or hidden liquidity, and summarize it using three imbalance variables that are aggregate measures of net supply and demand across these different order types. We postulate state equations that link the imbalances among themselves, and, subsequently, to returns, and use empirical market data to understand their functional relationship. By examining aggregated order flow across different time intervals we establish that there is a characteristic time scale over which the market can reliably infer the existence of an imbalance in supply/demand and adapt to it, thus reducing the imbalance. We show that, although components of order flow are highly auto-correlated, the conditional probability distribution of changes in liquidity provision or consumption are bimodal, where a strong imbalance of a given sign has a much higher than random probability to lead to an imbalance of the opposite sign in the future. This bimodal distribution explains the difficulty of obtaining reliable estimates of market impact. We show that the concavity of the relation between market impact and order flow depends sensitively on the relative contributions of the three contemporaneous imbalance components, as well as on their past values.