Larry Tabb made a comment the other day about my post on Google’s strategy that got me thinking… Larry said “It used to be that we were supposed to care about privacy – seems more and more that we are willing to give up privacy for functionality, ease of use, and the ability to better understand what I as a consumer want.”
The same is true about trading.
When deciding how much personal information to reveal on Google Buzz or Facebook, the correct answer will depend partly on who you are (there is a big generational element to this), how much information you have to protect, and what you want to achieve.
If you are 18, have very little private information, and want to socialize as much with as many people as possible, then you are likely to reveal a lot (maybe too much) on Facebook.
If you are an adult professional (with a lot more at stake) and you just want to know what your former college buddies are up to, you might reveal a lot less.
So with trading.
Every decision about trading tactics (i.e. how to get the trade done, not whether to buy or sell) should be driven by the investment strategy. For example:
- If you are buying a small quantity of a highly liquid stock because you anticipate positive earnings, then you might well choose to cross the spread and pay the offering price to get your order filled quickly before (you hope) the stock spikes up. Because your order is immaterial to daily trading volume, you have little to lose and (you hope) a lot to gain by showing your entire order to the market.
- On the other hand, if you are a large fund trying to accumulate a substantial portion of a small-cap (and probably illiquid) stock which you intend to hold for 2-3 years, you are more likely to let your order sit in a dark pool, go to a broker for capital commitment, slice your order up using an algorithm, etc.. In general you will be more passive and less likely to show your hand. You certainly wouldn’t show the entire order on the exchange, because you have a lot to lose and little incentive to rush.
The European regulation that addresses “best execution” is MiFID. MiFID recognizes that different investors have different strategies and therefore will make different trade offs in their trading tactics… They do not have a single/uniform definition of “best execution”, any more than Facebook has a single definition of “appropriate privacy.” Instead, MiFID recognizes that best execution is not limited to execution price but also includes cost, speed, likelihood of execution, likelihood of settlement and any other factors deemed relevant.
In contrast, the US regulation that addresses “best execution” (RegNMS) defines it very narrowly as getting an order executed at or between the best bid and the best offer on a publicly displayed market. Never mind that you may want to do 100,000 shares of a small cap name and there may only be 100 shares at the inside quote.
In practice, traders in the US markets need to make trade offs and answer a variety of questions, including:
- How important is certainty of execution
- How important is speed of execution
- What is an acceptable market impact
- How much of their hand to show
One of the most important tradeoffs is between how much of your hand to show (resulting in market impact) versus certainty and speed of execution. i.e. If you show your entire order to the market you have a much higher probability of getting it filled quickly, but you will move the market.
It is instructive to consider some of the recent innovations in the US equities markets (or innovations that, while not strictly recent, have become more popular in the last few years) in light of these trade offs.
Take algorithms. Algorithms allow a trader to slice a large order into many small orders and spread them out over time, usually to be executed on displayed markets that only offer liquidity in small quantities. The trader can thus comply with best execution obligations (i.e. each individual “child” order is executed at or inside the NBBO) while minimizing market impact and information leakage. (If you were to look at the average price achieved by all child orders that make up a single parent order, the parent order may well be executed at an average price better or worse than the NBBO at the time the parent order was created. So if the client were buying and the market rose dramatically over the life of the parent order, did the client get “best execution” just because each individual child order looks good?)
Dark pools provide another alternative. In general they offer less certainty or speed of execution, but also lower market impact. An order may sit for days or weeks in a dark pool until it is matched. (Your mileage may vary… On a pure institutional/block system like LiquidNet, the order is likely to sit until it is matched by a contra-side institutional order. On Millennium, it may be executed incrementally as it is matched against algorithms, but exposure to such smaller orders also creates greater risk of information leakage and gaming.)
Now let’s take a look at flash orders.
In a nutshell, flash orders allow a client to send an order to an exchange, and optionally permit the exchange to briefly “flash” the order to liquidity providers who can fill the order at a better price. This improves the certainty of execution, and may provide price improvement over the NBBO, but it increases information leakage. Should the client care? Well, if he is not concerned with information leakage (maybe he only had that one order) and he wants to “get done” now, then he is probably happy to use a flash order. If on the other hand, his order is one of many child orders for a larger parent order, he may prefer to be more passive and decrease information leakage.
Some teenagers expose all their photos and personal information to the world (including, ahem, college admissions officers), while others only permit their friends access to their information.
On the face of it, this is good. Flash orders provide a useful tool that allows a knowledgeable investor to make a reasonable tradeoff.
But consider Google Buzz. Google Buzz was rolled out last week as Google’s social network, its answer to Facebook and LinkedIn and MySpace. Cleverly, Google leveraged users Gmail contacts to automatically create “followers” on Google Buzz, and (by default) allowed any user of Buzz to see any other users followers. Google very quickly found out that many users don’t want the world to know who all of their Gmail contacts are. Especially if that list includes doctors, lovers, bookies, or other relationships best kept private.
So how does this relate to Flash Orders?
Google’s Buzz appears to be a reasonably clever service, and it actually does allow users to control their privacy. But Google erred by making the default privacy settings very non-private, and by making the privacy controls too difficult to find. They made a decision on behalf of their customers to expose those customers information. They made the trade off.
Likewise, the problem with flash orders is really not flash orders themselves. The problem is that in many cases a broker chooses to submit an order as a flash order, with the client never realizing (never mind understanding) the trade off that the broker has made on the clients behalf. Of course this is just a symptom of some more fundamental problem, namely:
- RegNMS dictates a very narrow definition of best execution, which has had the unintended consequence of encouraging innovation that helps the customer achieve their execution objectives despite this regulatory constraint. Maybe we should have a broader definition of best execution that incorporates the clients trading objectives.
- Brokers incentives are not aligned with their client’s objectives. Which is why brokers receive rebates for order flow, use flash orders, and generally do things that maximize profits for brokers rather than for customers (the phrase that comes to mind is “where are all the customers yachts?”)
- Even asset managers, who act as the proxy for most investors, have incentives (maximize assets under management) that are at cross-purpose to their client’s objectives (maximize risk adjusted return.)
But those issues are subject for another post.