
A few weeks ago I put together an analysis of the “Flash Crash” using technical analysis. Since then, in following Apple down the rabbit hole, things have indeed gotten “curiouser and curiouser”. What follows is a compilation of interesting things I have discovered in market Wonderland about May 6th, 2010.
During the ten minute period between 2:40pm and 2:50pm on May 6, 2010, for all market centers combined, the bid /ask spread was crossed (the Bid was higher than the Ask) for 7% of AAPL trades. This was not a high figure 5 years ago, but in the era of High Frequency Trading, errant cross trades are arbitraged quickly so the average has been driven down to 2-3% on any given day. Even then, we’re talking pennies.
For example, here is minute 2:37pm before the flash crash. I selected 2:37pm because it had the highest dollar trade volume of any one minute period preceding the flash crash. Despite the high trade volume (and the same economic back drop as eight minutes later) the bid/ask spreads are well managed with the highest concentration between 0 and 40 cents.
There are cross-trades (depicted as negative numbers) of course, but you can see that for this moment in time they represent less than one percent of the trades.
Things clearly heated up during minute 2:45pm of the Flash Crash, with spreads staying close to a dollar for the first half minute but widening to as much as 5 dollars in the latter 30 seconds. Still, notice that cross-trades were limited even with this extreme volatility. While there were fewer trades executed in minute 2:45 than minute 2:37pm, 2:45 is closer to the average transaction volume per minute for AAPL that day.
Here is a one minute chart of Apple to help put things in perspective.

Here are the trades per second for AAPL across all market centers during the minutes under discussion. Again, note that while trades dried up a bit in minute 2:45 relative to the other periods shown, there were still over 2600 trades executed for which the bid /ask spreads are plotted above.

For minute 2:46pm the normal spread expanded to between 0 and 5 dollars, and there were far more outliers and crossed trades.
The next chart is the bid/ask spread of AAPL trades executed at the top 3 market centers (by volume) for the entire ten minute period between 2:40 and 2:50 pm. It accurately shows the number of trades and the bid /ask spread of those trades, but it is not an accurate chronological overlay of the trades. In comparing this chart with the previous chart it is evident that almost all of the outliers occurred in minute 2:46 and most crossed trade outliers occurred at NYSE-ARCA.
NYSE-ARCA held the National Best Bid Offer (NBBO) for AAPL for only 25% of all trades during this ten minute period but was the source for 52% of crossed trades.
The data used to produce all of these charts is cumulative market center data compiled by the NASDAQ for AAPL and does not include any busted trades for May 6th, 2010. The issue of AAPL and busted trades is interesting because based on the data I have reviewed so far, I would have guessed the broken trades would have occurred in minute 2:46pm, or at least during key moments of the Flash Crash. They didn’t, this is Wonderland.
This is the busted trade data for AAPL I obtained. There could be more, I don’t know. It is difficult for me to get trade bust data for May 6th.
These busts were obviously high stub quotes for AAPL in minutes 3:44 and 3:49pm. It would make more sense if the time stamp was off by an hour, but even then, there was ample liquidity across all market centers collectively for both time periods. The spreads were under a dollar, bids were plentiful, and there were very few cross trades.
What is interesting is that busts occurred in Apple at all given the size of its market cap. It is also interesting that the busts occurred at NYSE-ARCA.
The SEC-CFTC report highlighted ETFs as an area for further study because more ETFs had busted trades than other stocks during the Flash Crash. All ETFs trade on NYSE-ARCA.
Perhaps more ETFs were broken because they traded on NYSE-ARCA and not because they were ETFs.
Inter-Market Sweep Orders (ISO) were created as an exemption to the order protection rule of Regulation NMS. An ISO is a limit order that 1) is identified as an ISO when routed to a trading center and 2) simultaneously with the routing of the limit order, one or more additional limit orders are routed to execute against all better-priced protected quotations displayed by other trading centers up to their displayed size. All orders must be identified as ISO orders available for immediate execution. The ISO exemption was adopted to allow institutional traders to forgo the best price requirement, in order to fill large orders. In practice there is no difference in the size of orders executed using ISO and non-ISO and use of the exemption has proliferated to be the primary order flow method for market makers and many large trading institutions. In truth, it is a loop hole that allows traders to use ISO to preference their order flow as a precision instrument allowing them to limit execution to a specified market center regardless of the NBBO. In the fragmented market structure of today it is used by market makers for bid/ask spread arbitrage, precision order placement, and directing trades to exchanges where they are reimbursed for limit orders. But ISO trades can also be used for more nefarious predatory trading tactics such as combining ISO with short selling to suck liquidity out of an illiquid / troubled market or security, or, fine tune an attack on a known weak hand or anticipated liquidation.
Both the NASDAQ and NASDAQ-BATS declared self help against NYSE-ARCA in the minutes preceding the flash crash. Was that the equivalent of “blood in the water” alerting predators of weakness?
NYSE-ARCA held the National Best Bid Offer (NBBO) for AAPL for 25% of all trades during the ten minute period of the Flash Crash but was the source for 52% of crossed trades. 100% of AAPL crossed trades executed on NYSE-ARCA were ISO exempt trades. In fact, in minute 2:46pm 100% of all AAPL trades executed on NYSE-ARCA were ISO exempt trades. Were they weak-seeking guided missiles or reimbursement seeking liquidity providers?
It is clear that there was ample liquidity in AAPL when viewed collectively across all market centers during the Flash Crash. Did ISO exempt trades play a role in NYSE-ARCA illiquidity? How many broken stocks listed on other exchanges were busted on NYSE-ARCA rather than their listing exchange?
While I have only presented detailed information regarding one stock so far, this chart from the Nanex flash crash study indicates that cross trades were a significant characteristic of the Flash Crash event on May 6th, 2010, and that NYSE-ARCA was a key player.
Were the generally illiquid markets on May 6th, 2010 brought down by predators wreaking havoc on NYSE-ARCA by turning ISO exempt trades into guided missiles?
Is it ironic that ISO exemption is the new normal, and its utility goes way beyond its stated purpose? Not in Wonderland.
In Wonderland, the uptick rule (that was in place since the great depression) was repealed 3 months before the market top in 2007.
In Wonderland, one stock is allowed to exert 24% leverage over an index.
In Wonderland, shares held by pension funds, trusts, foundations, the Treasury, employee stock plans, and other sources not available to the market on a daily basis are eliminated from index calculations.
In wonderland, it is all about volatility, and little or nothing to do with stability.
This trip down the rabbit hole may have produced more questions than answers, but I do believe that I have solved the riddle that perplexed the Hatter.
Question: Why is a raven like a writing desk?
Answer: Because the bankers told the regulators that it was.
This post is a duplication of an article I wrote for Minyanville.com

Relative strength measured by ratio analysis on one minute charts provides a surprising level of detail about the Flash Crash that cannot otherwise be interpreted. I believe that this innovative use of ratio analysis is critical to understanding the compressed events of the Flash Crash.
Here is a chart of the NASDAQ-100 index, AAPL, and the relative strength (ratio analysis) of Apple to the NASDAQ-100 on a one minute chart in the moments leading up to, and during, the Flash Crash. What ratio analysis shows is that at 2:44 pm on May 6th, 2010, AAPL spiked down hard relative to the Index itself. In other words, Apple was MUCH WEAKER than the index during the Flash Crash, or conversely, much stronger to the downside than the index.
In Was the Flash Crash Apple’s Fault I showed that the NASDAQ-100 was the weakest link during the Flash Crash and that Apple represents 20% of the weighting of the NASDAQ-100. Adding this chart to the evidence I presented in that article, it is not a stretch to logically interpret that Apple pulled the index down with it, rather than vice versa.
1 Minute Chart
1 Minute Chart
Right Click on chart {view image} to enlarge
I believe that the cumulative evidence presented here and in my previous article clearly shows that Apple is relevant to the cause of the Flash Crash.
I wrote this as an article for Minyanville.com and it can alternately be viewed here.
I am fascinated by the rapid decline and complete recovery that took place in less than 15 minutes exactly one month ago today on May 6, 2010 coined the “flash crash”. Even with the gloomy global economic back drop since then, it has taken the S&P 500 a full month to close lower than the downward spike of that event which originally occurred in two to three minutes. In over ten years of studying the markets on a daily basis I have never seen anything like it. I have spent the last few weeks studying the flash crash for evidence that could lead to an explanation of how it happened.
I started my research after reading the Preliminary Findings Regarding the Events of May 6, 2010 by the SEC-CFTC Joint Regulatory Committee. The report is 80 pages long with another 100 pages of appendices. The report includes excellent research and is chock full of interesting facts and clues about the “flash crash”. The report clearly states that it is preliminary, but I was still surprised by important clues (to me) that jumped off the page, but were not highlighted or included as a focus for further study by the committee.
I wrote a letter to the committee highlighting one such clue I found in the report regarding the tight grouping of profits at the extreme pivot away from the start of the crash. In other words, a relatively small number of traders successfully sold short, then caught a falling knife at exactly the right time for some outlandish profits (almost a half billion). Even if the profits were subsequently denied because of canceled trades, the uncanny prescience of a select few to cover at the perfect time warrants further study, especially since what precipitated the crash is unknown.
One idea highlighted in the report that received popular media attention was that aggressive hedging precipitated the crash. Circumstantial evidence included one S&P 500 futures hedger who represented 9% of futures volume during the crash and the outsized number of ETFs among broken securities (69%) as a result of the crash. Futures and ETFs are considered primary vehicles for hedging.
The SEC-CFTC committee pointed out several inconsistencies with this thesis but highlighted that additional analysis of large futures traders and the out-sized impact on ETFs were areas for further study. They also highlighted the role played by liquidity providers, high frequency traders, dark pools, and market mechanisms like circuit breakers, stop logic (forced pause CME Futures), stub quotes, stop-loss market orders, self-help (time-out mechanism allowing exchanges to stop routing orders), and liquidity replenishment points (forced pause NYSE), as areas for further study.
The report concluded that a confluence of economic events, market forces, and trading system functionality led to a significant dislocation of liquidity as measured by broken trades, bid/offer spreads, self-help declarations, and out-sized ETF factors.
Furthermore, due to the complexity and extremely tight linkage between the various market products, a detailed market reconstruction of hundreds of millions records, from dozens of different sources, comprising five to ten terabytes of data, consuming a significant amount of staff resources, was required to sequence the events of the flash crash.
This last part captured my attention. The idea occurred to me that ratio analysis might provide a short cut to a high probability answer of where the crash originated. Ratio analysis in charting is most often used to determine relative strength between two markets or two securities. If I applied it to one minute charts leading up to and during the flash crash, I might be able to identify the relative strength of market linkage between futures, stocks, and ETFs during the crash, determine the likely sequence of events, and possibly even isolate the weakest link in the crash.
The following chart shows the moments leading up to, and during, the flash crash at 2:45pm on May 6, 2010, and in my opinion paints a clear picture of the events in the order they occurred:

Right click {view image} to enlarge
Believing that I was onto something significant, I focused my lens even more on the NASDAQ-listed stocks and then it struck me. I recalled something that seemed odd to me when I originally read it in the report but it didn’t immediately register to me why it was odd. Now it did.
APPLE was the #1 top broken stock by trading volume during the Flash Crash. To truly appreciate the significance of this you need to reflect on market capitalization. As market caps go, Apple is a titan among the minnows. In fact, the NASDAQ lists it as one of only two mega-cap members (the other is MSFT). Apple has the second largest market cap of any US listed security. Only Exxon Mobile is larger, and not by much.
Market capitalization is so significant it is the basis for most market indexes. The premise of a market capitalization index is that the stocks with the largest market capitalization (and shares outstanding) are more stable and therefore given more weight than the smaller stocks with fewer shares outstanding.
In a market capitalization weighted index, each stock is weighted by its market value. Most market indexes including the NYSE, S&P500, NASDAQ Composite, NASDAQ-100, and all Russell Indexes are market capitalization weighted. As stocks come and go and market caps rise and fall, indexes are rebalanced to reflect the changes. When a stock’s market cap grows continually for an extended period of time its percent value of the index grows proportionally. For this reason index owners have rules for rebalancing their indexes.
The NASDAQ-100 is not rebalanced very often. In fact, the last rebalancing of the NASDAQ-100 was in 1998 when Microsoft grew too big too fast. What is too big? The following excerpt is taken from the NASDAQ-100 Index Methodology document on the NASDAQ website:
“On a quarterly basis coinciding with the quarterly scheduled Index Share adjustment procedures, the Index will be rebalanced if it is determined that: (1) the current weight of the single largest market capitalization Index Security is greater than 24.0% and (2) the “collective weight” of those Index Securities whose individual current weights are in excess of 4.5%, when added together, exceed 48.0% of the Index. In addition, a special rebalancing of the Index may be conducted at any time if it is determined necessary to maintain the integrity of the Index.”
When Microsoft’s hefty weighting was redistributed in 1998, AAPL and other smaller corporations received fractional percentage points from Microsoft’s rebalancing. Since then, Apple’s market cap has grown significantly and its weighted percentage of the NASDAQ-100 index has grown along with it. However, because the rebalance conditions have not been met, the index has not been rebalanced.
Maybe it doesn’t need to be rebalanced yet. After all, AAPL is the largest stock in the NASDAQ100, the second largest stock in the S&P500, a super mega-cap. It can’t be jostled around like a micro-cap. It is too big to fall. Or is it?
The following table shows the top ten weighted stocks of the NASDAQ-100 index. The weightings (Market Percent) are the actual weightings given to each stock in the NASDAQ100 for month end May, 2010. May 6th market values were probably higher for many stocks in the index, but Apple, which is the point of my discussion, was about the same.
|
Security Symbol |
Closing Price |
Market Value |
Market Percent |
May6 High |
May6 Low |
Max Point Drop |
Max % Drop |
Impact on Index % |
|
AAPL |
257.16 |
610953594638 |
19.1011 |
258.3 |
199.3 |
59 |
22.84% |
4.3630077 |
|
MSFT |
25.8 |
146283208930 |
4.5735 |
29.88 |
27.91 |
1.97 |
6.59% |
0.3015326 |
|
QCOM |
35.56 |
135390509467 |
4.2329 |
37.63 |
35.56 |
2.07 |
5.50% |
0.2328489 |
|
GOOG |
485.18 |
135212635741 |
4.2273 |
517.5 |
460 |
57.5 |
11.11% |
0.4697 |
|
CSCO |
23.16 |
88990407249 |
2.7822 |
26.65 |
23.23 |
3.42 |
12.83% |
0.3570403 |
|
ORCL |
22.57 |
88585403683 |
2.7696 |
24.97 |
22.2 |
2.77 |
11.09% |
0.3072404 |
|
INTC |
21.42 |
77828968847 |
2.4333 |
22.33 |
19.9 |
2.43 |
10.88% |
0.2647971 |
|
TEVA |
54.82 |
75804842567 |
2.37 |
60.38 |
57.17 |
3.21 |
5.32% |
0.125997 |
|
AMZN |
125.46 |
69559496272 |
2.1747 |
132.3 |
120.6 |
11.7 |
8.84% |
0.1923204 |
|
RIMM |
60.7 |
63529163932 |
1.9862 |
69.29 |
62.53 |
6.76 |
9.76% |
0.1937756 |
Look at market percent of AAPL. Apple stock weighs in at 19% of the NASDAQ-100 index. This is not an error. Now look at how much Apple dropped on May 6th. I show the calculated impact that AAPL alone had on the NASDAQ-100 that day. This is more than a red flag; this is a smoking gun. It is probably the spark that ignited the fire that brought down the house.
Using ratio analysis on one minute charts I have shown that NASDAQ-100 stocks likely led prices down on May 6th, 2010. I then showed how Apple’s extreme market percent of the NASDAQ-100 leveraged into a significant drop in the NASDAQ-100 and probably precipitated the Flash Crash.
What I am unable to show is why Apple dropped 23%. The SEC should immediately study the trades of APPLE on May 6th. If a large trader(s) precipitated the market crash on May 6th, Apple was the vehicle.
I think the confluence of economic activity, market forces, and trading functionality thesis should be moved to the back burner, and a market manipulation thesis should be moved to the front-burner in the investigation.
I think that a rapid 22.84 percent drop in AAPL affecting a 4.3% drop in the NASDAQ-100 index is grounds for a special (and immediate) rebalancing by NASDAQ.
TMD




In reviewing the Report of the of the staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues, and the prepared testimony of SEC Chairman before the Subcommittee on Securities, Insurance and Investment of the United States Senate Committee on Banking, Housing, and Urban Affairs, I was enthralled by the thoroughness of the investigation into the market mechanisms and structure that caused the precipitous drop and sudden loss of liquidity in United States Markets.
The complexity of this problem is highlighted in the Next Steps section of the report near the end, and it concludes that a detailed reconstruction of the markets involving hundreds of millions or records comprising an estimated five to ten terabytes of information must be analyzed so that cross-market patterns can be detected and then the behavior of stocks and traders can be analyzed in detail in order to solve the problem of “what caused the drop?”.
While I unquestioningly agree with the complexity involved in answering the question “what caused the drop?” I also wonder if focusing solely on that question is a bit myopic.
I find that when I am faced with a very complex problem, that if I work through a number of restatements of the problem, I will often identify other worthwhile problems to solve that will allow me to see the data differently. Often, the restatements are less complicated than the original problem.
“Who profited from the drop?” seems to me like a very good question to ask and could bring different answers and solutions into focus.
First, since the possibility of a very large sell order precipitating the “flash crash” cannot be discounted, it is possible that following the money could lead to the origination of said sell order(s).
Second, and far more important in my opinion, is the issue raised by the SEC chairman in her prepared testimony regarding whether market professionals fully met their best execution obligations during the “flash crash”. I believe that the answer to this question and more can be more accurately discovered by following the money.
If the markets during normal volatility can be compared to a game of musical chairs with market participants following a somewhat orderly procession until an economic factor occurs (the music stops) and there is a rush for the chairs (exits), then by comparison the afternoon of May 6th was a game of musical chairs where the music stopped and the lights went out at the same time. While it is important to understand why the lights went out, it is also important to thoroughly investigate the conduct of professional market participants while it was dark.
Below are tables 1&2 combined, taken from the Joint Report on page 20. The tables show the # trades, volume, and dollar volume for gains and losses between 2:40 pm and 3:00 pm in percentage terms. I added the columns percent of trades and percent of dollars.
|
|
Total Trades |
Total Vol |
Total $ Vol |
% trades |
% dollars |
|
|
|
|
|
|
|
|
All trades |
7,135,104 |
1,995,000,637 |
56,651,582,692 |
|
|
|
|
|
|
|
|
|
|
Gains |
2,121,380 |
636,291,411 |
18,603,965,183 |
30% |
33% |
|
|
|
|
|
|
|
|
0% to 10% |
2,108,076 |
632,378,310 |
18,079,956,948 |
99% |
97% |
|
10%-20% |
10,075 |
3,039,456 |
53,123,704 |
0% |
0% |
|
20%-30% |
927 |
281,383 |
8,589,789 |
0% |
0% |
|
30%-40% |
517 |
167,439 |
1,827,449 |
0% |
0% |
|
40%-50% |
106 |
32,866 |
536,641 |
0% |
0% |
|
50%-60% |
45 |
19,188 |
358,048 |
0% |
0% |
|
60%-70% |
67 |
14,466 |
387,321 |
0% |
0% |
|
70%-80% |
184 |
46,456 |
1,147,215 |
0% |
0% |
|
80%-90% |
178 |
44,075 |
1,143,775 |
0% |
0% |
|
>90% |
1,205 |
267,772 |
456,894,313 |
0% |
2% |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
Losses |
5,013,724 |
1,358,709,226 |
38,047,617,508 |
70% |
67% |
|
|
|
|
|
|
|
|
0% to 10% |
4,912,125 |
1,324,448,213 |
37,383,122,363 |
98% |
98% |
|
10%-20% |
63,860 |
22,171,745 |
522,444,343 |
1% |
1% |
|
20%-30% |
12,923 |
4,077,881 |
85,328,519 |
0% |
0% |
|
30%-40% |
6,112 |
2,317,245 |
30,461,333 |
0% |
0% |
|
40%-50% |
2,519 |
767,393 |
9,641,261 |
0% |
0% |
|
50%-60% |
1,682 |
472,624 |
8,334,944 |
0% |
0% |
|
60%-70% |
1,056 |
370,920 |
4,328,898 |
0% |
0% |
|
70%-80% |
798 |
292,061 |
2,245,851 |
0% |
0% |
|
80%-90% |
1,109 |
237,259 |
1,152,480 |
0% |
0% |
|
>90% |
11,510 |
3,553,885 |
557,516 |
0% |
0% |
70% of all trades during this period were losses and 30% of trades were gains. Interestingly though, the dollar losses were 67% versus 33% gains. Furthermore, what stands out as a red flag is that the majority of the extra dollar gains were trade profits >90% away from the 2:40pm price. This is a very good place to focus the lens.
Here I show only the trades between 2:40 and 3:00pm on May 6th with gains or losses greater than 10%. Again, I added the percent of trades that were gains and percent of trades that were losses.
|
Btwn 2:40:3:00 |
Gains>10% |
Losses > 10% |
Totl trades>10% |
%gains trades |
%losses trades |
|
10%-20% |
10075 |
63,860 |
73,935 |
14% |
86% |
|
20%-30% |
927 |
12,923 |
13,850 |
7% |
93% |
|
30%-40% |
517 |
6,112 |
6,629 |
8% |
92% |
|
40%-50% |
106 |
2,519 |
2,625 |
4% |
96% |
|
50%-60% |
45 |
1,682 |
1,727 |
3% |
97% |
|
60%-70% |
67 |
1,056 |
1,123 |
6% |
94% |
|
70%-80% |
184 |
798 |
982 |
19% |
81% |
|
80%-90% |
178 |
1,109 |
1,287 |
14% |
86% |
|
>90% |
1,205 |
11,510 |
12,715 |
9% |
91% |
|
Total |
13,304 |
101,569 |
114,873 |
12% |
88% |
The trade data of gains and losses greater than 10% between 2:40pm and 3:00pm on May 6th is decidedly skewed toward losses with 88% of the trades being losers verses 70% for all trades during this period.
It would be very interesting to see this subset of data (and the dollar volume data below) broken out by accounts categorized into retail and professional for comparison. I think that could provide additional insight into how the professionals conducted themselves while the lights were out.
Here is a breakdown of the red flag data from above. Notice the marked difference in dollar gains starting around 70%-80% through > than 90%. I don’t think it is too much of a stretch to surmise that this was the buy-to-close zone for sophisticated short sellers. In a free falling market this data leaps off the page as a pretty tight grouping.
Of particular note is the detail of the > than 90% red flag data where only 9% of the trades reaped greater than 99% of dollars gained. By examining these buy-to-close trades, and matching them with their sell-to-open counterparts, the CFTC / SEC might discover interesting information regarding market participant involvement on May 6, 2010.
|
Btwn 2:40:3:00 |
Gains |
Losses |
Tot $ Vol |
%gain $ |
%loss $ |
|
10%-20% |
53,123,704 |
522,444,343 |
575,568,047 |
9% |
91% |
|
20%-30% |
8,589,789 |
85,328,519 |
93,918,308 |
9% |
91% |
|
30%-40% |
1,827,449 |
30,461,333 |
32,288,782 |
6% |
94% |
|
40%-50% |
536,641 |
9,641,261 |
10,177,902 |
5% |
95% |
|
50%-60% |
358,048 |
8,334,944 |
8,692,992 |
4% |
96% |
|
60%-70% |
387,321 |
4,328,898 |
4,716,219 |
8% |
92% |
|
70%-80% |
1,147,215 |
2,245,851 |
3,393,066 |
34% |
66% |
|
80%-90% |
1,143,775 |
1,152,480 |
2,296,255 |
50% |
50% |
|
>90% |
456,894,313 |
557,516 |
457,451,829 |
100% |
0% |
|
Total |
524,008,255 |
664,495,145 |
1,188,503,400 |
44% |
56% |
Examining the events of the “flash crash” through the lens of market mechanism and structure will very likely provide additional clues about specific catalysts that contributed to the exaggerated price swings and liquidity vacuum as measured by broken trades, bid/offer spreads, self-help declarations, and outsized ETF factors. However, in order to identify whether or not market participants conducted themselves professionally, ethically, and even legally is better viewed through a money lens.
By focusing the money lens on a very small sampling of the market data that I was able to glean from the report, I believe I uncovered helpful information about market participants on May 6th, 2010.






















