Minimum quantity (MQ) is popular among traders as a tool for improving execution quality. As an exchange, we have a unique perspective on the effectiveness of MQ use. In this three-part series, we attempt to quantify how effective MQ use is in optimizing execution.Minimum quantity (MQ) is popular among traders as a tool for improving execution quality. As an exchange, we have a unique perspective on the effectiveness of MQ use. In this three-part series, we attempt to quantify how effective MQ use is in optimizing execution.
There are many factors that impact execution quality — a few of them are adverse selection, information leakage, and ability to source liquidity. The weight that is applied to each of these changes depending on the goals of the trader and the strategy.There are many factors that impact execution quality — a few of them are adverse selection, information leakage, and ability to source liquidity. The weight that is applied to each of these changes depending on the goals of the trader and the strategy.
In Part I, our analysis on IEX found that MQ may not be very effective for mitigating adverse selection. In Part II, our data suggested that using MQs of 100 (or 200 for stocks priced under $50) could help minimize information leakage, but higher MQs indicated no additional performance benefit. In Part III, we try to establish how much liquidity traders may miss by using MQs on IEX.In Part I, our analysis on IEX found that MQ may not be very effective for mitigating adverse selection. In Part II, our data suggested that using MQs of 100 (or 200 for stocks priced under $50) could help minimize information leakage, but higher MQs indicated no additional performance benefit. In Part III, we try to establish how much liquidity traders may miss by using MQs on IEX.
A commonly understood risk of using high MQs is that they may hinder the ability to source liquidity, because they purposefully prevent trading with counterparties below a certain size threshold. Of course, this is the express purpose of using MQs in the first place — traders often set high MQs to prevent trading with certain types of liquidity associated with small counterparty sizes. However, since Parts I and II did not find that MQs necessarily improve performance, we want to quantify the risk and potential opportunity cost of different MQ levels.A commonly understood risk of using high MQs is that they may hinder the ability to source liquidity, because they purposefully prevent trading with counterparties below a certain size threshold. Of course, this is the express purpose of using MQs in the first place — traders often set high MQs to prevent trading with certain types of liquidity associated with small counterparty sizes. However, since Parts I and II did not find that MQs necessarily improve performance, we want to quantify the risk and potential opportunity cost of different MQ levels.
First, we look at how MQ impacts fill rates and hit rates. Then, we look at how much liquidity is avoided at different MQ levels. In short, Part III uses data from shares traded on IEX from 1/1/20 to 9/30/20 to address the question: How much liquidity could you be missing by using high MQs?First, we look at how MQ impacts fill rates and hit rates. Then, we look at how much liquidity is avoided at different MQ levels. In short, Part III uses data from shares traded on IEX from 1/1/20 to 9/30/20 to address the question: How much liquidity could you be missing by using high MQs?
Fill Rates and Hit Rates
Setting a MQ reduces the number of possible counterparties with which an order can interact. Intuitively, this means that fill rates and hit rates should decrease as MQ increases.
In our analysis, fill rates measure the portion of an order that is filled (filled shares / shares sent), and hit rates measure the portion of orders that found liquidity (orders with trades / orders sent). This analysis looks at Midpoint Peg and Discretionary Peg orders on IEX from non-prop (i.e., agency or full-service broker-dealer) firms.[1] We only look at orders that rested for at least 30 seconds to reduce the impact of duration on the results. As you can see below, there is a clear decrease in both fill and hit rates as MQ rises.[2]
How Much Liquidity is Missed with MQs?
Now that we’ve established that higher MQs are correlated with lower hit and fill rates, we move to looking at just how much liquidity is missed at different MQ levels.
When setting MQs, traders must consider the amount of liquidity they are willing to forego to attract a large counterparty. However, this is difficult to gauge since it is impossible for a trader to know the volume of liquidity with which they did not trade. As an exchange, though, IEX has a unique vantage point that allows us to see exactly how many shares orders with MQs did not trade with while waiting for a size-eligible counterparty.
To measure the opportunity cost associated with MQ, this analysis looks at midpoint orders with MQs and counts the number of shares with which they could not interact because the contras were too small. We look at resting orders on IEX from 1/1/2020 to 9/30/2020 that rested for 30 seconds or longer.
In the table below, the “Avoided Liquidity” column represents the percentage of orders that had at least one share prevented from trading because a contra didn’t meet the MQ threshold. As you move to the right, the “Avoided 50–100% of Order” column represents the percentage of orders that avoided shares equivalent to 50–100% of the entire order. The right column explains the percentage of orders that avoided shares equivalent to or greater than the size of the order.
We find that 19% of all orders with a MQ of 100 had some amount of missed volume, but this number increases over 2.5x to 49% when the MQ rises to 200 shares. This dramatic increase in missed liquidity is likely because 59.8% of midpoint trades on IEX are between 100–199 shares (see Part I), so limiting yourself to only trade with contras larger than 200 shares removes a lot of eligible counterparties.We find that 19% of all orders with a MQ of 100 had some amount of missed volume, but this number increases over 2.5x to 49% when the MQ rises to 200 shares. This dramatic increase in missed liquidity is likely because 59.8% of midpoint trades on IEX are between 100–199 shares (see Part I), so limiting yourself to only trade with contras larger than 200 shares removes a lot of eligible counterparties.
The most concerning situation is when a MQ results in missing shares that would have accounted for at least 100% of your order size. We call this situation “Severe Opportunity Cost,” because an order could have been fully filled over the period of time it rested before being canceled or filled by size-eligible counterparty. Below, we see that “Severe Opportunity Cost” increases 5x as MQ rises above 200.The most concerning situation is when a MQ results in missing shares that would have accounted for at least 100% of your order size. We call this situation “Severe Opportunity Cost,” because an order could have been fully filled over the period of time it rested before being canceled or filled by size-eligible counterparty. Below, we see that “Severe Opportunity Cost” increases 5x as MQ rises above 200.
Next, we look at the “avoided liquidity” metrics through a practitioner’s lens by isolating orders that saw significant market volume trade during the life of the order.[3]Next, we look at the “avoided liquidity” metrics through a practitioner’s lens by isolating orders that saw significant market volume trade during the life of the order.[3]
We take this perspective because we want to look at situations in which an order missed a reasonable level of liquidity, not just small orders the trader intended to filter out. As the saying goes: “If a tree falls in the forest and no one is there to hear it, did it really fall?” If a trader avoids trading against small orders while nothing sizeable is trading in the market, the opportunity cost of those avoided shares might not be significant. Put simply, it is important to look at times when missing volume is the most expensive.We take this perspective because we want to look at situations in which an order missed a reasonable level of liquidity, not just small orders the trader intended to filter out. As the saying goes: “If a tree falls in the forest and no one is there to hear it, did it really fall?” If a trader avoids trading against small orders while nothing sizeable is trading in the market, the opportunity cost of those avoided shares might not be significant. Put simply, it is important to look at times when missing volume is the most expensive.
Source: IEX Market Data. Date Range: 01/01/2020–09/30/2020.
When only looking at situations when there was meaningful liquidity traded during the order, there is an even more dramatic increase in missed liquidity on IEX as MQ goes up. Importantly, the increase in “Severe Opportunity Cost” (avoiding over 100% of an order) increases over 5x when the MQ rises above 199 shares! [4] When only looking at situations when there was meaningful liquidity traded during the order, there is an even more dramatic increase in missed liquidity on IEX as MQ goes up. Importantly, the increase in “Severe Opportunity Cost” (avoiding over 100% of an order) increases over 5x when the MQ rises above 199 shares! [4]
The Opportunity Cost of Limiting Counterparties
Over and over, our analysis shows a jump in the amount of liquidity orders miss in the 200+ MQ buckets compared to the 100–199 share bucket.
This suggests that MQs of more than 100–199 risk much greater opportunity cost than MQs of 200 or less. Each time MQ is used, traders are choosing to reduce potential counterparties, but the dramatic increase in cost from a 100- to 200-share suggests a larger tradeoff than might be anticipated.
Of course, the risk of missing liquidity must be viewed in the context of larger trading strategies and the specific circumstances of a given order. Our purpose in sharing this analysis is to provide insight that may help traders maximize their own performance. Please see our summary post, “Do Minimum Quantities Maximize Performance” for an overview of all three parts of this series and key takeaways.
[1] IEX classifications of non-prop trades are on a best efforts basis by Member firms’ trading sessions. This method is used throughout for graphics that measure markouts.[1] IEX classifications of non-prop trades are on a best efforts basis by Member firms’ trading sessions. This method is used throughout for graphics that measure markouts.
[2] See Appendix for table that shows the percentage of orders that rested for more than 1 second by MQ and ADV. It is worth nothing that orders with no MQ had lower hit and fill rates than orders with a MQ of 100 shares. This is counterintuitive as these small orders can interact with counterparties of any size. However, the orders with no MQ did not realize higher hit and fill rates because they had the shortest duration of all the orders, resulting in less opportunity to get filled or trade.[2] See Appendix for table that shows the percentage of orders that rested for more than 1 second by MQ and ADV. It is worth nothing that orders with no MQ had lower hit and fill rates than orders with a MQ of 100 shares. This is counterintuitive as these small orders can interact with counterparties of any size. However, the orders with no MQ did not realize higher hit and fill rates because they had the shortest duration of all the orders, resulting in less opportunity to get filled or trade.
[3] In this look, we only count volume as “missed” if market-wide volume traded over the life of the order equals the lesser of half the order size or 1000 shares.[3] In this look, we only count volume as “missed” if market-wide volume traded over the life of the order equals the lesser of half the order size or 1000 shares.
[4] The 1,000+ MQ bucket begins to see less severe opportunity cost because those trades include some extremely large order outliers in which the denominator (order size) is so large that there is little opportunity for enough shares to trade that it would miss the entire order.[4] The 1,000+ MQ bucket begins to see less severe opportunity cost because those trades include some extremely large order outliers in which the denominator (order size) is so large that there is little opportunity for enough shares to trade that it would miss the entire order.
Appendix:
Below are the % of orders in each Min Qty Bin that rested for at least 30 seconds. You can see that only 7% orders with no MQ rested for longer than 30 seconds, compared to 24% of orders that had a MQ of 100–199. To control for these differences in usage patterns, when measuring hit rates, fill rates, and opportunity cost, we isolated orders that rested at least 30s.
