HFT Stock Market Manipulation Behind Facebook IPO Stock Crash

A simple explanation of NANEX data showing how Wall Street used high frequency computers to manipulate Facebook’s stock price during their stock offering.

If that weren’t enough, the Wall Street trading data firm NANAX released a report today documenting the use of ultra-fast computers to manipulate and crash Facebook’s stock price after it started trading Friday.

Their analysis, shown below,  details the use of High Frequency Trading algorithms using exploits to “short-circuit” the NASDAQ stock exchange.

The “short-circuit”  was used to keep the Facebook stock price from going above $42.99 and to manipulate the stocks price downward in a manner that allowed traders to make massive profits from the stock price drop.

Imagine walking into your local pawn shop and the store is selling Compact Discs for $5.00 dollars and buying them for $10.00!

The NANEX report released today shows that by using high speed computers Wall Street traders were able to manipulate the stock exchange into that situation during the Facebook IPO.

Specifically, as shown in the chart above, the algorithm forced the Bid (which is the a broker is willing to purchase as stock for) to exceed the Ask  (the price brokers are willing to sell the stock for).

As you can imagine, once the market is forced into this condition the algorithm can purchase millions shares from a broker for $42.20 and then sell it right back to the broker for $42.45.

Such a condition on the stock market results in a virtual “short-circuit” as a large number of high-frequency trading bots detect the condition and repeatedly execute the trade making massive profits.

As the exchange is hit with a massive number of trades from the HFT bots the exchange freezes like an overworked computer.

While the exchange is frozen the backlog of HFT orders get added to a queue where they will eventually be processed when the deadlock is broken.

The chart from above is just one many times the algorithms forced this condition and then exploited it during the Facebook IPO.

The next chart shows exactly how the algorithms were able to create such a condition were the bid and ask price were inverted during the Facebook IPO.

The series of trades in the chart starts off with the spread (the difference between the bid and ask price) starting off very tight with a 1 cent difference.

The shapes on the bottom of the gray boxes show the various traders being made on 4 different exchanges which in turn have to synchronize with each other.

By flooding purchasing a massive of stock on one exchange and simultaneously selling it on another exchange the algorithm is able to drive the price on exchange up while driving the price down on the other, thus creating a condition in which the bid is higher on one exchange than the ask is on another exchange.

To use a real world example, let’s suppose that 4 different pawn shops all part of a franchise buy Lady Gaga CDs from the public for $4.99 and sell them to the public for $5.00.

Now suppose you start repeatedly purchasing all of latest Lady Gaga CDs from pawn shop 1.

Eventually this pawn shop will raise the price on the CDs due to the high demand.

At the same time you sell all those CDs to pawn shop #2 for a small loss and eventually this shop lowers the price on the CD due to the high supply.

Eventually you drive the price of the CD at shop #1 up to $6.00 while driving the price at shop #2 down to $4.00

Meanwhile shop #3 and shop #4 still have the CD priced at $5.00.

You just created a market with an inverted bid and ask price that you can exploit for a massive profit!

To cash out you buy all Gaga CDs from  shop #2 for $4 each.

You also buy all of the Gaga CDs from shops #3 and #4 for $5 each.

Now you sell them all to pawn shop #1 which is buying them for $5.99 each and make a massive profit!!

When the franchise finally updates the price of the Gaga CDs across their stores, they see that shop #1 is buying them for $3.99 so this becomes the new buying prices in all the stores.

On the stock exchange high-speed computers accomplish the same exact effect by running their high frequency trading algorithms across the different stock exchanges

The price is driven downward on one exchange until the bid and asking price are inverted.

Once the bid exceeds the ask, HFT bots across the system hit the exchange with a flurry of trades  creating a backlog of orders from which the algorithms make massive profits.

The bid-ask inversion exploit wasn’t the only force at work here either.

As reported by Bloomberg and CNBC the IPO’s underwriters  openly admitted their plan was  to cap the opening day stock pop at a 10% increase.

The official reason is they didn’t want the price to surge too high, too fast.

In other words, they wanted to prevent a bubble that would burst and leave their

Why? Because 75% of the stock offering went to Wall Street firms and  ‘sophisticated investors’ for the price of $38.00 a share. (sophisticated investors are rich investors who are allowed to get in on an IPO before it actually starts trading publicly).

The other 25% the stock went to the ‘retail investor’, that is the general public or the mom and pop investor, who had to buy the stock at its opening price of $42.05.

In order to prevent the stock from crashing too soon after the IPO and cause banks and ‘sophisticated investors’ to get burned for their investment they planned on throttling the increase of the Facebook stock price.

They knew it was going to crash, that is why they withheld from the public the fact that Wall Street had cut Facebook’s revenue estimates after raising questions (in secret) about Facebook’s plan to monetize their site traffic.

Chart 2 below, shows how high frequency traders were able keep the NASDAQ Bid stuck at $42.99.

However, you’ll notice that while the NASDAQ was stuck at $42.99 the other exchanges traded downward.

This is most likely the result of the underwriter’s setting a $42.99 trigger for their HFT bots while another group of bots was at working manipulating the Bid and Ask price across the exchanges.

It created a race condition were two HFT botnets  were battling each other to manipulate the price and they continued cancelling out each others trades on the NASDAQ causing the bid to flat-line at $42.99.

Additionally, a third force was likely at play here manipulating the price for an entirely different reason – hedge funds looking to profit from shorts.

While hedge funds couldn’t directly short the Facebook IPO stock  (to short a stock means placing a bet in which you make money if the stock price goes down) there are ways around that restriction.

Stocks often trade in step with other stocks that are in the same industry.

By shorting a similar company, LinkedIn for example, while at the same time using HFT bots to trigger a Facebook sell off hedge funds looked to profit handsomely.

Another and better example is Zynga which makes all of their money through Facebook is a direct proxy to Facebook.

At the same time, shorts against  Zynga or LinkedIn and the fall of their stock price were sure to create a feedback loop that weighed down on the price of Facebook.

Indeed, social networking stocks all crashed during the Facebook IPO.

In fact Zynga shares fell so much their trading was suspended on the stock exchange and anyone short made handsome profits.

At the end of the day, Skynet short circuited and ended up cannibalizing themselves.

Someone made massive profits from manipulating the bid ask price.

Someone made massive profits shorting other social networking stocks.

Facebook’s insiders made massive profits cashing out on the profits.

The underwriters who were looking to cap the stock’s increase at 10% ended up dumping billions to keep the stock artificially propped up so it wouldn’t fall below the $38.00 IPO price per their contractual requirements.

The millions of mom and pop investors who were suckered in by all of the media hype and fanfare jumped in an opening price of  $42.05 only to have it crashed back down to $38.00 with similar 10% declines to follow suit.

Zero Hedge reports:

Back on March 27, following the epic disappointment that was the BATS IPO, we presented a detailed forensic analysis courtesy of Nanex, which demonstrated step by step how a Nasdaq-borne algo may have been the culprit shattering BATS’ hopes of ever going public. Fast forward two months later to the most anticipated IPO in recent history, in which FaceBook’s even more epic, if not quite as stark, implosion has set back the general public’s faith in capital markets decades back. The irony, of course, is that FB didn’t do anything that many weren’t warning about: it simply plunged which would make perfect sense in a normal world. This in turn was the spark that provoked the public ire – had FB simply doubled since IPO day, nobody would care about what really happened on May 18. Alas, it didn’t. And now the lawsuits come. The problem is we don’t transact in a normal world, but one dominated by central banks and algorithms – which is why the most pressing question for those who grasp the real new normal is how come in a market as controlled and manipulated as the central bank-dominated venue we have now, was FB stock allowed to plunge? For what may be the actual definitive answer, as opposed to now trite philosophical ruminations on valuation, ethics, underwriter and shareholder greed, we once again go to Nanex, which has caught the perpetrator red handed once again.

Somehow we doubt many will be surprised to learn that the reason FB failed to take off following its break of trading in the low $40s, has everything to do with, you guessed it, another HFT algo, which in those first instants of trading, did something that threw the entire market off: it kept crossing the market, with the Bid surging above the Offer, in the process shocking the entire price-supporting HFT array, designed to build upon upward momentum, resulting in the only other natural outcome: a steep, rapid selloff.

As Nanex’ Eric Hunsader tells us: “Turns out just before Nasdaq’s quote crossed and became non-firm, one copy of the same quote (crossed) was marked regular, and I think that caused other algos to react and immediately sell off the stock. When that crossed quote from nasdaq appears, bid prices from other exchanges suddenly evaporate and that causes the NBBO spread to explode from 1 cent to 70+cents in 1/10th of a second! Nasdaq’s quote started doing this when the stock approached 42.99 — that effectively prevented the stock from going higher (a few spurious trades right at the open came from BATS for 44 ~ 45 etc, before Nq’s quote was in play). So these stupid Algos effectively short circuited the stock for Facebooks IPO! Unreal.”

Sadly, for millions of people who were gullible enough to buy into the propaganda, all too real.

Below is Nanex with its traditionally lauradtory forensic analysis that leaves nothing to the imagination:

Did a Stuck Quote Prevent a Facebook Opening Day Pop?

On 18-May-2012, within seconds of the opening in Facebook, we noticed an exceptional occurrence: Nasdaq quotes had higher bid prices than ask prices. This is called a cross market and occurs frequently between two different exchanges, but practically never on the same exchange (the buyer just needs to match up with the seller, which is fundamentally what an exchange does).

When Nasdaq’s ask price dropped below its bid price, the quote was marked non-firm — indicating something is wrong with it, and for software to exclude it from any best bid/offer calculations. However, in several of the earlier occurrences the first non-firm crossed quote was immediately preceded by a regular or firm crossed quote!

During the immediate period of time when the Nasdaq quote went from normal to non-firm, you can see an immediate evaporation in quotes from other exchanges, often accompanied by a flurry of trades. We first noticed this behavior while making a a video we made of quotes during the opening period in Facebook trading.

The reaction to the crossed quote often resulted in the spread to widen from 1 cents to 70 cents or more in 1/10th of a second! It is important to realize that algorithms (algos) which are based on speed use existing prices (orders) from other exchanges as their primary (if not sole) input. So it is quite conceivable, if not highly likely that these unusual, and rare inverted quotes coming from Nasdaq influenced algorithms running on other exchanges.

It is now more than a curiosity that the market was unable to penetrate Nasdaq’s crossed $42.99 bid which appeared within 30 second of the open and remained stuck until 13:50. Could this have prevented the often expected pop (increase) in an IPO’s stock price for FaceBook?

This also brings another example of the dangers of placing a blind, mindless emphasis on speed above everything else. Algos reacting to prices created by other algos reacting to prices created by still other algos. Somewhere along the way, it has to start with a price based on economic reality. But the algos at the bottom of the intelligence chain can’t waste precious milliseconds for that. They are built to simply react faster than the other guys algos. Why? Because the other guy figured out how to go faster! We don’t need this in our markets. We need more intelligence. The economic and psychological costs stemming from Facebook not getting the traditional opening day pop are impossible to measure. That it may have been caused by algos reacting to a stuck quote from one exchange is not, sadly, surprising anymore.

Chart 1. NBBO (National Best Bid or Offer) Spread along with Nasdaq quote.
NBBO Spread colored black: bid < ask (normal), yellow: bid = ask (locked), or red: bid > ask (crossed).

Chart 2. Nasdaq’s Stuck Bid appears to set a defined ceiling in Facebooks stock price during the first minute of trading.

Chart 3. Close-up showing NBBO along with ARCA quotes (red) and Nasdaq quotes (black = normal, green if non-firm).

Chart 4. Same period of time as chart 3, but showing NBBO and trades from Arca (red circles) and Nasdaq (black circles) for reference.

Chart 5. Note how the spread tightens in all exchanges when Nasdaq Quote goes from Non-firm to normal.

Chart 6. Just before Nq Quote changes to non-firm, a crossed quote from Nasdaq appears and is marked normal.

Chart 7. The next charts are more examples of other exchange prices reacting to Nasdaq’s quote changing to non-firm

Chart 8.

Chart 9.

Chart 10.

Chart 11.

Chart 12.

Or, another way of presenting what happened, is the following video.

Hunsader’s explanation of what you are seeing:

Watch Nasdaq’s quote (first box to appear – 10 o’clock). Note the crazy bid prices are higher than the equally crazy ask prices. After trading opens, Nasdaq’s quote will start turning red when it’s no longer eligible to set the NBBO. Watch how quotes on the other exchanges react wildly causing the price to evaporate.

Each box represents one exchange. The SIP (UQDF in this case) is the box at 6 o’clock. It shows the National Best Bid/Offer. The shapes represent quote changes which are the result of a change to the top of the book at each exchange. The time at the top of the screen is the time of the last quote or trade update in Eastern Time HH:MM:SS:mmm (mmm = millisecond). We accelerate time until the open, and then we slow time down so you can see what goes on at the millisecond level. A millisecond (ms) is 1/1000th of a second. The blink of an eye is about 200 ms.

Note how every exchange must process every quote from the others — for proper trade through price protection. This complex web of technology must run flawlessly every millisecond of the trading day, or arbitrage (HFT profit) opportunities will appear.

Dear class action suit attorneys – you are indeed quote (sic) welcome.

Source:Zero Hedge

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