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
Have you found any more information on this? I am still working on this strategy & am wondering how accurate the data published in retail level data feeds is. I have investigated the concept a bit and found that the feeds are said to be inaccurate as they generally use the first trade as the open price instead of the primary market opening cross.
Do you know of any retail level data feeds that provide the primary exchange’s opening price?
Regards,
Eric
Hi Eric,
Sorry for the delay in responding… I only just noticed these comments. I’m not aware of any “retail level” feeds that provide this level of detail, but I’d expect they exist. The NYSE’s TAQ data certainly can be combed to glean this data though the cost is ~$1K/month and the real cost is hidden in managing the mass of data.
I’d expect that any feed that accurately provides the executing exchange should be adequate as the correct MOO price can be determined where the trade is the first of the day on the “listing” exchange and the MOC price can be similarly determined by finding the last trade of the day from the listing exchange. Yahoo’s data is accurate for the MOC but not the MOO as they seem to be interpreting the first price of the day as the first price after 9:30am on any exchange.
I believe this explains the discrepancy I’d detailed in this post.
Figuring out, for any given feed, if you’re getting the right MOO & MOC prices is easy enough as you just need to trade on the open and close and compare the prices you got to the prices your feed provides.
As for market impact, we never trade more than an analysis of recent MOO/MOC volumes suggests we can reasonably get away with…
Best,
Tito – Thanks for the response. I found several data feeds that do what I need.
I have some Matlab code written to evaluate the order execution on MOC orders as well as some Opening Gap entries. I think the bigger challenge for me will be to evaluate how to handle MOO & MOC entries in many instruments. Specifically, I would like to be able to say ‘my desired order size will move the market up to reduce my potential profit by x.x%, therefore I should reduce my share size and increase the number of instruments I am trading’. Have you found any academic or other papers that go into this concept at all? I haven’t found too much so far in my search.
Regards,
Eric
Would you share some details on those feeds as others may find it useful?
Sorry, I don’t know of papers that specifically address market impact on the open or close.