execution quality at the open & close
I’ve been trading an increasing amount at the open and close of the equity markets using market-on-open (MOO) and market-on-close (MOC) order types and have found that the quality of executions varies enormously between the two types and have spent a bit of time analyzing the differences which I share below.
The quick scoop is that MOC orders almost invariably fill at the exchange’s published closing price, while MOOs vary very substantially from the published open price. Below I quantify my findings in a bit greater depth.
I looked at 846 recent MOO and MOC equity trades made over the past two months. Of all of these trades, only one MOC trade didn’t execute at the published close and the price I got was only off by one penny. Across all of the trades, I received the open or close price 55% of the time.
The remaining 45% of the time I varied from the open by an average of +.04% This means that I actually saw a slight price improvement in the average case. That is, if I was shorting then I executed at a price above the listed open and vice-versa for longs. I’ll take it!
In the below chart I plot the trades against their variance, positive or negative, from the listed open or close.
The biggest difference was a whopping 8.56% but at least it went in my favor. The stdev across all of the trades was .87% so we’re not looking at too disperse a grouping.
This data is a bit skewed as the majority of the MOO orders are going short. This is also a pretty limited universe of trades, so I’ll continue to look at the execution quality I’m getting on these order types and will revisit it if I see any interesting changes.
My interpretation is that my broker is making a best-effort to get a fair open price and they’re doing a creditable job of it. The exchanges are doing a nearly perfect job with MOC orders.
What impact does this have on my strategies? I’m not sure yet, but my first blush impression is that it might be worthwhile to try to get some price improvement over the posted open price as a means of both improving the results and extending the capacity of such strategies. It’s a favorable result as it means that strategies which back-test well on open/close data have a pretty good chance of executing well in reality.
A related issue, which I’m still researching, concerns the capacity of such strategies and may be the topic of a future post…
back-testing, execution quality, performance analysis, post-trade analysis, strategy development
Great blog, I have been following it for a few months now.
Just a quick question for you - Have these numbers been updated since Aug 1st? I was curious as to whether or not they changed in the past few months due to the increase in volatility.
Regards,
Eric
Thanks, Eric.
Good question. I haven’t updated these figures since I wrote this but have thousands more trades of data… one of these days I’ll update the study and post the results. I expect the MOCs to continue to show near 100% performance, but it will be interesting to see how the MOOs have done.
Thanks for the reply, I would certainly appreciate any updates. I am working on a long/short strategy that exclusively uses MOOs & MOCs and have not been able to find too much data on execution quality for these order types.
Also, I suspect your trade samples include trades with much higher quantities than a retail account. Do you feel this has an impact on the results? I know that one will incur more liquidity costs as the trade size goes up, but have been unable to quantify it.
Regards,
Eric