Facebook IPO: A Forensic Post-Mortem with Eric Hunsader of Nanex
The Next Level with Erik Townsend
Show transcript of Facebook IPO: A Forensic Post-Mortem with Eric Hunsader of Nanex
ERIK: I’m Eric Townsend for the Financial Sense Newshour. The Next Level is a new weekly program segment intended for investment professionals and more advanced private investors. This week’s topic is a forensic, post-mortem analysis of the Facebook IPO. Joining me as my special guest on the program is Eric Hunsader of Nanex.
Eric, thanks for joining me on the Next Level. [00:56]
ERIC HUNSADER: Thanks, Eric, for having me.
ERIK: My pleasure. Now, Nanex is a market data company. But before we begin, I want to put that into perspective for our listeners because to me the words “market data” company kind of imply if I want to know let’s say the closing price of IBM stock on a given day a couple of years ago, well, a market data company ought to be able to provide that information. But in the case of Nanex we're really talking about something much more detailed and different. For example, if I wanted to know three years ago on Wednesday afternoon March 17th, on a specific three-second period of time, let’s say between 2:37:14 and 2:37:17 in the afternoon, exactly precisely what happened with say IBM shares during that period, not only how many shares were bought and sold, but what prices were submitted, what limit order prices might have existed in the queue, Nanex can give me all of that information after the fact. So we're really talking about market data records that are so detailed that it becomes possible to do extremely in-depth analysis. It kind of reminds me of the analysis of a flight-data recorder after an airplane crashes or something.
Eric, give us a little bit more perspective on what you do at Nanex and what the company is about. [2:14]
ERIC HUNSADER: We have about four trillion records now in our dataset and that comes from options, futures, equities, pretty much anything that trades in the United States. And not only can we tell you what happened in a three second window for a specific symbol, but we can tell you what other symbols and what other instruments were also doing at the same time. [2:36]
ERIK: So you basically got all the data; everything that there is that is recordable, you have collected it, saved it and made it available for processing after the fact so you can analyze what happened. [2:48]
ERIC HUNSADER: Well, actually, our business is selling this realtime to subscribers. That is our main business. The actual processing the data is more or less what we do for our customers when investigating oddities. [3:02]
ERIK: So you provide the realtime data feed and you also provide an add-on service which is the post-mortem analysis of interesting things that have gone on in the market. [3:12]
ERIC HUNSADER: Correct.
ERIK: Now, before we dive into the gory details of what happened that fateful day with the Facebook IPO, I want to start by giving our listeners a sense of some of the background issues that came into play that day. Now years ago, a given stock say IBM stock was only traded on one and only one stock exchange. So IBM’s listed on the New York Stock Exchange, so that means the New York Stock Exchange is the only place that you could go to trade IBM shares, but that all changed a few years ago. Please explain to our listeners how and why it changed; what is Regulation NMS and how things work today. [3:45]
ERIC HUNSADER: Regulation NMS is what was debated by the industry in 2005, 2006 and was finally implemented and rolled out in the first, second quarter of 2007. At the core of Reg NMS is this concept called the national best bid or offer, which each exchange trading a specific stock would submit their bids and offers; this information would be aggregated by what they called a SIP -- there’s a lot of acronyms coming up here. SIP stands for Security Information Processor. And one of the formal names or actually implementation of the SIP you might hear is CQS, the Consolidated Quote System. That’s the same thing as the SIPs for a specific group of stocks. But anyways, so those SIPs would accumulate the bids and offers from all the exchanges, find the highest bid and the lowest offer and that would become the national best bid or offer so that an order from a customer coming to any exchange would have to trade at the best bid or offer before it could trade at a next lower price. That was called trade through price protection. And that is the core of what Reg NMS is all about. [4:58]
ERIK: So essentially what Reg NMS does is it provides the industry with a way to make it possible for a certain stock or other security to trade on more than one exchange. And by having a set of rules that provide, first of all, an advertised national best bid or offer that assures that any buyer or seller can get a price that they know was the best price available and that their trades will execute at that price. So even though there might be multiple bids and asks on different exchanges, the Reg NMS system is intended to make sure everybody gets a fair deal, gets a fair price and all their trades execute at the best available price from any exchange anywhere in the world essentially. [5:39]
ERIC HUNSADER: Correct. And by having a SIP that would collect the information from all the exchanges, an investor who wanted to receive and analyze prices on stocks wouldn't have to go to each exchange separately and contract for their data feeds because it would be all available in the SIP. And the SIP would also serve as an audit trail, so that the SEC or anybody could really go back in time and see exactly what happened at a given point in time. [6:07]
ERIK: Okay. So the industry wants to do something fairly sophisticated, which is to trade the same securities on different exchanges. Regulators come back and say, okay, wait a minute, you guys want to do that, you've got to make it fair to investors by providing this thing called a SIP, which I believe stands for Securities Information Processor? [6:24]
ERIC HUNSADER: That’s right.
ERIK: And by providing that, we're going to make sure that there’s one-stop shopping that any trader in the marketplace can go and look at what’s the best bid or offer that’s available on the SIP; all of the trades will occur at that price, everybody is protected and nothing fishy goes on behind the scenes in terms of how a given customer’s buy or sell order gets processed. Or at least that was the idea. [6:45]
ERIC HUNSADER: Yes. It’s that simple. And it wasn’t the SEC mandating this on everybody, I mean this was -- Reg NMS was invented by industry -- widely respected people in the industry who made very good comments, some very prescient about it. So it wasn’t like rammed down everybody’s throats so to speak, it was after a pretty long public discussion period. [7:09]
ERIK: Okay. Now there’s a concept in the regulation called the “eligibility for setting the national best bid and offer” and there’s also a concept of a “non-firm quote” that doesn't set that. Could you just explain that because that’s going to come into play in our analysis of Facebook in a couple of minutes here? [7:25]
ERIC HUNSADER: That was pretty much to accommodate the NYSE which was still trading manually at the time versus NASDAQ the electronic exchange which was trying to say that NYSE might have a better bid than us, but you know, they’re doing it manually and they can’t execute it quite as fast as we can. We can execute it electronically. And so there was this concept introduced called “non-firm” where if an order was sent to the exchange and they didn’t respond within a certain amount of time, their quote could be considered non-firm and the exchange itself who was slow to respond was supposed to mark their quotes as non-firm, which told the SIP, hey, don’t include my bids and offers in your calculation of the best bid or offer. [8:12]
ERIK: So essentially, a SIP’s job is to make sure that the best bid or offer being presented to investors is available in one place at any given time, and if an exchange is not able to participate in that system because its own quotes are screwed up for some reason or they can’t perform at an adequate speed or something like that, there’s a way for them to mark their quotes and just say, hey, don’t count us in this determination of the best bid and offer that investors are presented with. [8:39]
ERIC HUNSADER: Correct.
ERIK: That all makes perfect sense as long as it’s being used in its intended mechanism, which, as we’ll see, doesn't always happen.
Now, another trend which has emerged in recent years that a lot of people have heard the buzz word but not everybody really understands is high frequency trading. Please give us a brief explanation of what high frequency trading is, who does it, how does it work. [9:02]
ERIC HUNSADER: Well, you know, that term is kind of a catch all for many different strategies, all the way from stat arb and it’s just sped up to actual market making. We kind of define high frequency trading as trading algorithms that would not be profitable if not for speed. In other words, speed is what makes it profitable. [9:22]
ERIK: So high frequency trading are computer programs instead of human traders that are trading the market and they have a variety of different algorithms that drive them. Some of them are just trying to scalp bid-ask spreads, others are trying to influence prices by buying and selling at certain times and so on and so forth. [9:42]
ERIC HUNSADER: Right.
ERIK: What does the term “colocation” mean as it’s used with respect to high frequency trading?
ERIC HUNSADER: Well, the speed of trading has gotten to the point where the number one factor in how fast you can trade is the speed of light. And the speed of light travels in an idealized case approximately 186 miles in one millisecond. A thousand foot cable it takes the speed of light approximately a microsecond or a millionth of a second to travel down. And so the closer you get to the actual exchange execution engine the shorter your distance and the shorter your time and the faster you can trade. So colocation came about several years ago when the speed of trading got below say the 10 ms level. [10:26]
ERIK: So if you’re sitting in your living room and you've written a high frequency trading algorithm and I’m sitting in my living room and I’ve written one, if one of us goes and spends a whole bunch of money to put our computer out of our living room and into the same building as the actual stock exchange where the network is connected not through the broader internet but through direct cables from my computer to the exchange, I’m going to have a substantial advantage over you trading from your living room because the messages are going back and forth much slower to your computer than they are to mine. [10:57]
ERIC HUNSADER: Right.
ERIK: Proponents of high frequency trading have argued the presence of algorithmic trading is actually a benefit to all investors in the marketplace because these computer driven trades add liquidity resulting in tighter bid-ask spreads which benefit all investors. Please explain why you have a slightly different view. [11:15]
ERIC HUNSADER: We can’t actually find any academic study that proves that spreads are tighter. In fact, just recently I believe it was the CEO of the NYSE said otherwise, that since Reg NMS the spreads have actually widened. And when you look at how rapidly the best bid and offer changes in one second period of time, it really is very difficult to measure what the spread exactly is. It’s like sometimes we’ll see the bid and offer change a thousand times in a second. It depends on which price point you decide to pick. So on the liquidity part, the liquidity, we've found, looks more illusionary than anything. In fact, one of the things that we saw from the Facebook IPO was when there was this glitch that caused an outage for about 17 seconds on all stocks on NASDAQ, as soon as that outage ended when NASDAQ came back online, we saw a tremendous amount of liquidity disappear from the books in just the blink of an eye in all stocks, not just Facebook and like SPY and other stocks. And it was a permanent thing for the rest of the day. In other words, as soon as these high frequency trading systems detect any problem in the system, they'll just stop trading. When I mean they'll just stop trading, I mean they'll stop trading in a fraction of a second. And so what you might see on the screen as liquidity is only going to be there as long as every thing is -- if nothing has changed in that amount of time. But as soon as something goes wrong in the system or their algorithm doesn't like the input data they’re gone in a flash. [12:57]
ERIK: I think that that really reveals the fallacy of this liquidity argument because liquidity used to be provided by specialists and market makers. And the counter argument that I see is so many of those specialists and market makers have been run out of business because the high frequency traders are providing that liquidity and they’re providing it in a more competitive way. So those other sources of liquidity aren’t in the market anymore, and then when that liquidity is needed the most, when it’s really urgently needed in an incipient crash kind of scenario, that’s when those computer programs all shut down. They recognize something’s wrong and they pull that liquidity out of the market and it’s gone instantaneously. And I know you've done a lot of analysis not only on Facebook but on the flash crash; we don’t really have time to go into the details of that, but I would encourage our listeners to look at your flash crash analysis because I think that really reveals what can happen when high frequency traders pull their liquidity out of the market all at once. [13:59]
ERIC HUNSADER: The other thing going on here, when you used to have one exchange, all of the liquidity would be at that one exchange; when you go from one exchange to 10 exchanges, where you used to have maybe 10,000 shares of IBM at that one exchange, now you have 100 shares at each of the 10 individual exchanges. And Reg NMS only protects that top price at each exchange. So what can happen is when you want to sell 1,000 shares in the old days you’d get and have it all done at once at the same exchange and you knew what the price was going to be. Today, you have to route those orders. You have to split your order into 10 and route them to all the different exchanges. Well, what happens is the fastest high frequency traders will see your order appear at one of the exchanges and as soon as it does, it will immediately withdraw its orders or pullback a penny on all the other exchanges. So it becomes this game of trying to time your orders so that they can’t see it hit on any one exchange ahead of the others. So that’s extremely -- actually, impossible to do. [15:01]
ERIK: And with those high frequency traders providing most of the market making liquidity in the system, what that means is they see you coming, they see you transact your first share on one exchange, whatever you’re trying to do with the rest of your order is probably going to result in some slippage, so your actually losing that benefit of liquidity because the computers that are providing that liquidity are so agile that they can stop providing it when it’s in their interests and not your interests for them to do so. [15:27]
ERIC HUNSADER: And that is so true.
ERIK: Now, critics of high frequency trading often cite a practice of quote stuffing as an example of the negative impact high frequency trading has on the marketplace. Please explain what quote stuffing is all about. [15:42]
ERIC HUNSADER: That example I just gave where people would try to time their orders so it would appear on all exchanges at the same time to evade high frequency traders seeing it, that strategy only works if all the machines are processing in an idealized manner. In other words, if one machine has to process an extra 1,000 quotes from a different stock, that will cause that machine to be slightly slower and over time, the law of averages, more of these orders split up amongst exchanges are going to be detected ahead of time allowing the high frequency traders to step out of the way. [16:17]
ERIK: So essentially what’s happening is, you know, the market is supposed to exist in theory for efficient formation of capital to support businesses and so forth. But what’s happening is some of the traders in the market are trading with computers; other computers are outsmarting them by pulling the liquidity out of the way just as it’s needed. So the first set of computer traders are saying, hmmm, we can outsmart those other computers by sending them a bunch of bogus quotes. An example of quotes might be limit orders, say, to buy or sell a security slightly away from the market price. So those orders they know are not going to be executed, but if I send 10 or 20,000 quotes, that is, 10 or 20,000 limit orders to an exchange against a particular symbol all in the course of two or three milliseconds, the computers at that exchange are going to be so overwhelmed, they don’t know what hit them, and that’s going to then allow me to then go deal with another exchange -- if my computer is fast enough -- and do some trading there that the other high frequency trading programs aren’t able to intercept. Is that essentially the gist of it? [17:25]
ERIC HUNSADER: Yes. But also the exchanges have assigned computers to process groups of stock and any stock in that group that has high message rates will affect all the other stocks in that group. So if you know which stocks are in that group, you can cause problems with those that you’re not trading and the entire group is going to be affected.
The other thing going on here is all these high frequency trading algorithms use as input [the] orders. So they’re not feeding off of anything economically related because that would be way too time consuming or expensive. So they’re feeding off of the orders coming into the system, when you know that -- if you’re a high frequency algorithm designer -- and you know that they’re feeding off of input prices, the temptation is pretty great to try to feed them false information. And that’s essentially where a lot of the high traffic comes from is there are these algorithms that are just putting out bogus information to try to lure or trick another algorithm to reveal its hands; to see whether their algorithms are operating. [18:36]
ERIK: What percentage of the overall message transaction volume is made up by high frequency traders, algorithmic trading?
ERIC HUNSADER: Back in the 1999 bull market when we had the internet bubble and the market was just crazy, the NYSE increased their capacity to a 1,000 quotes per second because the trading was in such a frenzy. Today where we don’t have this wild bull market our message traffic rate is 1.2 million messages a second. [19:15]
ERIK: So we have 1,000 times more messages in the system than we did in 1999?
ERIC HUNSADER: Correct.
ERIK: And most of that is coming from high frequency traders and I know I’ve heard in terms of the actual trades, some studies have indicated as much as 70 percent of all the trades are high frequency trading. [19:32]
ERIC HUNSADER: Yes. And probably closer to 98 percent or higher of the quotes are from high frequency traders.
ERIK: One more concept that we need to understand before we move on to Facebook is the concept of cross quotes. Please explain what cross quotes are about.
ERIC HUNSADER: Well, cross quotes are typically when you have multiple exchanges in the same stock and one exchange has a bid price that is higher than another exchange’s offer price, which Reg NMS discourages that behavior. If an exchange wants to post a higher bid than another exchange is offering it for, the exchange is supposed to clear out that other offer or the other exchange maybe just has a slow quote that’s updating. So we typically crossed and locked quotes all throughout the day; not a high percentage, but we see them. And when the markets get busier, we see more of them. So it directly correlates to message traffic. Now, having a quote from the same exchange where the bid is higher than the offer coming from the same exact exchange, that doesn't happen. And the reason it doesn't happen is because that’s the core function of an exchange, that somebody wants to buy at a higher price than somebody wants to sell at the same exchange, well, what the exchange does is it matches the two orders and you get a trade execution. [20:55]
ERIK: So on any particular exchange, any time that the bid is as high as or, theoretically, starts to move higher than the offer, that situation is resolved because a trade happens; the bid being higher than the offer cannot persist because the trade resolves that. However, when multiple exchanges are involved the calculation of that national best bid and offer could potentially result in getting information from exchanges where maybe one has a bid that’s higher than another exchange’s bid; it’s not supposed to happen or at least it’s not supposed to persist, but it occasionally happens for, what, a few milliseconds at a time?
ERIC HUNSADER: Yes. And that’s typically because one of the exchanges is slow to update their quote.
ERIK: So something is not quite happening as fast as it’s supposed to and that results in a situation where a bid appears to be higher than another exchange’s offer, but within a few milliseconds that situation would resolve itself, a trade would happen and a bid would be lower than the offer again as it should be. [21:56]
ERIC HUNSADER: Right. And only when the market is in extreme activity like the flash crash for example, the NYSE’s bid was about 36 seconds slow to update, and that again on September 29th, 2008, right when they were debating the TARP bill we had the similar event, the -- here NASDAQ’s was delayed into minutes. That’s extremely rare. [22:22]
ERIK: Let’s go ahead and move on then into the Facebook IPO because, boy, the things that happened on that day were definitely extreme in terms of pushing the limits of what’s not supposed to happen in this multiple exchange system. Now, this was arguably the most anticipated securities offering in all of history; everybody from the underwriters to the exchanges had plenty of opportunity to get ready for it, they knew when it was going to happen, trading in Facebook was supposed to begin at 11:00 a.m., but that’s not what happened. We talked about quote stuffing earlier. Please explain how quote cancellations from high frequency traders contributed to the opening delay in Facebook. [23:00]
ERIC HUNSADER: Before we looked at the order book data individually, we saw what NASDAQ said about why it was delayed. They said that quote cancellations caused them to recalculate what the opening cross price was going to be and they made it sound like it took 5 milliseconds to do that, and so we assumed at that time that there were never 5 milliseconds of quietness in their order book. In other words, if they couldn't resolve a price -- the opening cross -- it was because in the last 5 milliseconds there had to be a new quote cancellation. In actuality, what we found is -- and this is still theory because we have no proof this is how their system works, but we've heard and the data confirms that whenever a new price came into Facebook -- either an order add or an order cancel -- they would queue a message to their system that said in effect, “recalculate what the opening cross price should be using the average all the orders in the book.” And so every time a change happened in the book, one of these queued messages would get sent off to tell it to recalculate. Well, the thing is, when you have a system like that, when you already have a message in the queue says to recalculate, you don’t add another one to it because the first recalculation is going to essentially give you what the next one is. In other words, once you’ve calculated the book up to X amount of orders, you don’t have to calculate it again. And so the way their system was designed is they had this stack of recalculation messages sitting in their queue and I have heard that it was on the order of a couple hours of recalculation events, which they really didn’t need to do, they only needed to recalculate it once. So this occurred because of high cancellation rates, but the cancellation rates weren’t all that high actually. [24:53]
ERIK: But what’s happening here? I mean we have a public offering of a company that’s certainly in the news; everybody has heard about. The purpose of these markets, the way they serve society is to provide efficient formation of capital to allow a business like Facebook to grow and expand by being traded publicly. What we have is a public offering that wasn’t really even able to get off to a start because there were thousands and thousands and thousands of messages coming from high frequency traders that don’t appear to me to have anything to do with actually wanting to capitalize Facebook. They were algorithms trying to game the system and exploit differences in bid-ask spreads and so forth. But I don’t think they were doing anything to actually support the purpose of the IPO. [25:45]
ERIC HUNSADER: Correct. Because there were lots of very strange bid and offer prices in there. In fact, we saw before even the 11 o’clock opening, we saw bids for over $4,000, and we saw offers all the way down to a penny before they even opened up. Between 10:45 and 11:30 when they finally opened, there was a total of 595,791 order book messages which represented adding orders and cancelling orders and it netted out to about 360,000 orders total that were sitting in the book before the open. Now, of those 360,000 orders sitting in their book when they finally opened, there were 2,700 different prices. If you think about it, 2,700 prices when Facebook increment was a penny, that represents $27 spread of different orders sitting in that book. [26:50]
ERIK: Wow. My understanding is that according to regulations all trades are supposed to happen at the national best bid or offer, that’s the whole idea of trade through price protection -- make sure everybody gets a fair price. But what your analysis revealed and maybe this has to do with several securities being handled by the same computer is that there was erratic trading in other NASDAQ issues for something like 35 minutes after Facebook finally started trading. So were those other symbols being affected by the computer traffic that was targeted at Facebook? And what were the impact on those other symbols, and basically what happened? What is a bad price spike, how does it occur and what specifically happened during that first half hour? [27:33]
ERIC HUNSADER: So we noticed after they had trouble opening up, starting about 11:10 in stocks like Apple, SPY, Zynga, Intuit -- some high profile names -- Netflix, Amgen, Autodesk. We saw all of a sudden a trade would occur, like for example an Apple, it was trading around $538, we would all of a sudden see it trade at $532 or sometimes it was $10 away. And these were just small orders that were executing outside of the market and we see this every day; we see this in dozens of stocks every day. But what was unique about this is we started seeing it in lots of stocks, all of a sudden right around that event. I think we counted at least 40 different stocks succumb to something or other where it looked like there was a routing issue where the exchanges weren’t seeing the prices right and instead of routing the order executed them at wild prices. [28:43]
ERIK: Now, when you say that there were trades happening outside the market, I assume what you mean by that is the trades were not happening at the advertised national best bid or offer; people were paying more or selling for less than they supposedly should have been able to; is that right?
ERIC HUNSADER: Yes. Significantly less.
ERIK: I don’t know if it actually came down to this, but essentially it’s as if, you know, you’re sitting there looking at your screen, it says say the price is $38 for Facebook. You say, okay, go ahead and buy 100 shares at the market, you know that the market price is $38 because that’s what it says on your screen and you get filled at $45 or something crazy like that. [29:21]
ERIC HUNSADER: Yeah. Something crazy like that.
ERIK: And that didn’t happen on every trade, but you have evidence to show that kind of thing happened during the Facebook IPO and it wasn’t just one or two transactions, it was lots and lots of trades that happened not only in Facebook shares but in other symbols that perhaps were managed by the same computer were trading outside the market. So a whole bunch of people got prices on their trades which were not fair, which were not in accordance with Regulation NMS. [29:52]
ERIC HUNSADER: Correct. And what’s interesting is those trades never actually got cancelled later on in the day, which is pretty surprising because usually the ones that are really wide like that will be triggered and somebody will complain. Actually, you have to complain within like 30 minutes or some ridiculous amount of time and the exchange will investigate it. In these cases, they were so wide the exchange would have busted them. But it doesn't appear that’s what happened. [30:17]
ERIK: So it may be that people who were busy watching the Facebook debacle thinking, “Boy, thank God I’m not part of that thing, I’m invested in Apple over here, I don’t have anything to do with Facebook.” They may have been losing money on Apple trades that were not happening at the advertised national best bid or offer and they might have been distracted by watching the Facebook debacle go down to the point where they didn’t think to complain to someone and they’ve now lost money as a result of that. [30:47]
ERIC HUNSADER: It’s unknown, you know, what the cause of these were. It’s a good thing though that the prices weren’t so far away that they actually caused 5 minute halts in these stocks, which is what we had on the BATS IPO where Apple, again, Apple actually executed far enough away that it halted it for five minutes. They came very close to that but they actually didn’t hit it. [31:06]
ERIK: Now, there was a mysterious period of inactivity on the NASDAQ before the Facebook opened. And I want to put in perspective for listeners that one or two milliseconds is a very long time in computer speak at least in terms of processing transactions on a live exchange. We're talking about in this case 17 seconds of ‘radio silence’ on the NASDAQ. What was that about? [31:32]
ERIC HUNSADER: Right. The way I explained it to a CNBC reporter was that imagine every second the exchange wasn’t putting out information was equivalent to 10 seconds of blue screen on your upcoming broadcast from your system. And so the 17 seconds was approximately 3 minutes of blue screen. I mean it was an absolute eternity by Wall Street standards. And it wasn’t just in Facebook. NASDAQ stopped putting out quotes or any messages period on everything, starting 8 minutes before 11:30 when they finally with their last -- their third postponement where they promised they were going to open Facebook until about 9 seconds into 11:30. So it was 17 seconds of nothing. And when they went quiet they had quotes on all of these other symbols; I guess SPY and Apple and many stocks. Normally, when an exchange is having a problem, it knows it’s going to go out, knows it’s having equipment problems, it will send quotes out to all the exchanges saying we're non-firm or we're withdrawing our quote or we're having equipment problems. That wasn’t the case here. They just completely disappeared without any warning. And when they came back after that 17 seconds, during that period of time, any other exchanges that priced higher or lower would cause crossed quotes with NASDAQ because NASDAQ wasn’t moving, it was where it was at the start of this quiet time; it would cross with the other exchanges. But when NASDAQ finally came back after that 17 seconds, you could see the liquidity just evaporate in all stocks that NASDAQ quotes in; like SPY evaporated by two-thirds of the orders that were in the book before were just disappeared within a fraction of a second. What’s more interesting is that liquidity never returned for the balance of the day. So it was a pretty permanent liquidity evaporation event that spooked quite a few systems. [33:45]
ERIK: So what’s going on here is just before Facebook is supposed to start trading, the whole NASDAQ exchange goes totally dead silent for 17 seconds.
ERIC HUNSADER: Right.
ERIK: Seventeen seconds is an eternity in computer terms. When it comes back online, its numbers don’t match up with the other numbers on the other exchanges. That causes all of the most sophisticated traders, both the high frequency trading algorithms and perhaps the most sophisticated traders at investment banks and so forth to look at the situation and say, whoa, something is very broken here, NASDAQ’s numbers are crossed with the other exchanges, the exchange is going on and off line for 17 seconds at a time, something is bogus here. And that basically spooked them out of the market. They said, “We don’t want to have anything to do with this Facebook IPO, we've seen enough, this has spooked us.” Is that essentially what your hypothesis is as to what happened here? [34:43]
ERIC HUNSADER: Yes. And they were spooked in about a tenth of a second. That’s what was pretty frightening to see. That fast.
ERIK: Now, the 17 seconds is an eternity for a computer program, but it’s easy to see how a human trader might not notice there’s something that lasts for a few seconds, but what shocks me the most reading your analysis, if I understood it correctly, is that NASDAQ stopped sending its quotes for Facebook to the SIP for hours after that; Is that right? [35:15]
ERIC HUNSADER: Right.
ERIK: So there’s a period -- I guess this is where I just don’t understand it -- you know, 17 seconds for you and I, we’re both software people, that’s a long time for computers, but I would think after an hour of the NASDAQ, which is obviously one of the most important stock exchanges in the world, not participating as it was supposed to in the determination of the national best bid or offer for perhaps one of the most anticipated ever IPOs, that’s kind of a big deal. You would think somebody would notice it, they would halt trading again or you know, diagnose this thing; reboot the computer. Do something. But what happened? They just sort of sat back and said, Yeah, a couple of hours have gone by, NASDAQ’s not at the party, but you know, they’ll come join us later. What happened here? [36:01]
ERIC HUNSADER: Well, you know, a couple of things are interesting about this are if you look at the percentage of trades that are executed on NASDAQ versus the other exchanges, you would expect to see quite a difference when they are not participating in the NBBO than when they are. We don’t see that at all. It was like as if there was no effect, period, of NASDAQ not putting out its quote into the NBBO. [36:24]
ERIK: Now, wait a minute. The way you’ve explained this, according to the Regulation NMS, in order to comply with the law, the only way you would get to the NASDAQ to transact any shares would be to go through the NBBO mechanism, the SIP, first; is that right?
ERIC HUNSADER: Yes. That’s exactly right.
ERIK: So if the only legal way to transact Facebook shares on the NASDAQ is to get the price from the SIP and the NASDAQ price is not on the SIP that would seem to suggest to me that there is no legal trading of Facebook shares on NASDAQ. Am I missing something?
ERIC HUNSADER: No. And we see this every day and during periods of very high activity it becomes clear we see trades printing ahead of quotes out of the SIP, for example, that the only way that something like this can happen is if they’re using their direct feeds for pricing and not paying attention to the SIP at all. In the Facebook IPO we have an opportunity to see this: We don’t have to look very close under the second, we can sit back and look at what happened over hours. [37:32]
ERIK: Wasn’t there somebody whose job it was to say, Hey, we've got this Reg NMS system where all the exchanges are supposed to play ball and for two hours NASDAQ is not at the table participating in the calculation of the NBBO? Wasn’t it somebody’s job to notice that and respond to it or do something? [37:50]
ERIC HUNSADER: I would hope that the industry hasn’t gotten to the point where they actually do not use the SIP for routing, but it’s become so commonplace that they’re not even monitoring what’s going on -- the prices at the SIP. But it certainly looks that way. [38:08]
ERIK: You mentioned something a minute ago I want to come back to for the benefit of our listeners. You talked about a trade arriving ahead of a quote. So again, according to Regulation NMS, the only way anybody is supposed to trade anything is that they see on the SIP that’s where a new price has become available. You know, maybe the price of Apple has come down a nickel from where it was a few minutes ago and I want to buy it. I look at the SIP, I say I want to execute against that price. What you’ve seen routinely are situations where the trade happens just before the price is reflected on the SIP. And the only way that could possibly be happening is if some of these most sophisticated algorithmic traders are bypassing the SIP; they are using their co-located, very expensive connections directly to the exchange to bypass Regulation NMS and the SIP and transact directly with the exchange before anybody else can see that price that’s supposed to be, you know, fairly presented for the benefit of all traders; is that right? [39:13]
ERIC HUNSADER: That’s exactly what we see. And you know, when they ignore the SIP like that, it results in not upgrading it or maintaining it to handle higher volumes for example because if they’re not noticing that, hey, you know, our traffic rates are so high now that it’s getting delayed a lot more, if they’re not paying attention to it or routing based on it, there’s no incentive for them to upgrade it. [39:35]
ERIK: And the HFTs that have the co-located computers that cost hundreds of millions of dollars of direct connection are therefore creating a completely and totally unlevel playing field. They have an advantage in that they can deal directly with the exchanges and they can take advantage of a new price that comes on the market before the official mechanism for presenting that price to all traders even knows what it is. [40:00]
ERIC HUNSADER: Yes. And there’s another thing going on here too. All the retail customer orders like the ones going to E-Trade or Ameritrade or pretty much any retail customer order, those orders never actually get down to the exchange floor. They are sold off to internalizers who take that order flow and match those customer orders internally. And they pay a pretty handsome price for being able to do that. The interesting thing is they sell those customer orders based on the SIP price and they have the ability to trade on the faster price so now you’ve got an incentive for there to be a difference between the two. [40:39]
ERIK: So the same people that we know for sure are bypassing the SIP, which is mandated by law as the system you’re supposed to go through to get to the price, we know they’re bypassing that system and they have a financial incentive to play other games to manipulate what prices customer orders actually get executed at. And perhaps you don’t have conclusive evidence that says there’s monkey business going on there too, but it would seem that that would be par for the course given other things that we're seeing. [41:09]
ERIC HUNSADER: Yes. It’s hard to imagine if your legal obligation is to fill that customer order by any price out of the SIP during that one second period for you to always give that customer the best of those prices. It certainly is possible but the incentive is otherwise. And what’s more is there actually no way for anybody to prove -- there is no audit trail, there is no way to know exactly where the prices truly were at any given point in time because if everything was based on the SIP and routed on the SIP then there would be an audit trail and it would be very easy to see what’s going on. But that’s not the case, so it’s impossible to actually prove if each customer actually didn’t get a penny less than they should have. [41:57]
ERIK: Now, overall the Facebook IPO radically underperformed almost everyone’s expectations. Far fewer shares were sold above the opening price than most people expected. I have to wonder whether the reason it failed so badly was specifically because of what you describe: that the most sophisticated traders both human and computer they saw this stuck or crossed quotes; they saw NASDAQ not participating and they just said this thing is crazy, I’m going to walk away and not participate in this whole offering. Well, if those high frequency traders are providing a lot of liquidity that used to come from specialists and market makers and so forth, it’s easy to see how the whole IPO flopping the way it did could be explained by all these computer traders getting spooked out of the market as a result of these system errors in the way orders were being routed and the SIP not having the right prices, and NASDAQ not participating in setting the national best bid and offer and so forth.
To what extent, in your opinion, would you say that the presence of high frequency traders caused this IPO to be such a flop? [43:03]
ERIC HUNSADER: They didn’t do anything out of the ordinary. I think business as usual. It’s just -- it looks to me like there was software at NASDAQ that just for whatever reason didn’t function properly. I mean one of the other things that happened here which is very bizarre is NASDAQ’s own order book that’s a direct level feed had a stuck bid at the top of $4200; there was a $4200 bid sitting in their book until 1350 in the afternoon and there was a stuck offer at $25 sitting in their book until 1:50 in the afternoon. And that’s probably what caused a lot of these internalizers -- I mean we heard Knight Trading for example, in an interview said to the effect that quotes out of NASDAQ were squidged or were -- he used some term to indicate they were screwy. And that is definitely extremely rare to see. In fact, I don’t think it’s ever happened before, on the Facebook IPO, where their own order book was messed up like that. [44:13]
ERIK: Okay. So we don’t have a case of anything malicious. The high frequency traders didn’t try to sabotage the Facebook IPO or anything like that. But essentially what’s happening is all of these systems are so fast and so focused on moving messages at extremely high rates that when a really big event, which the Facebook IPO was by any stretch of the imagination; it was a big deal that everybody in the market was paying attention to. When something like that comes along, everybody is participating in it with this massive volume of data and the computers just couldn't handle it and basically fell over and coughed at a time when it was probably the most critical. I mean everybody watches an IPO to see if it’s going to pop; they’re looking to see if that big doubling of price on the first day kind of transaction volume is going to happen. As people were watching the market to see what would happen, all of the computers were bailing out saying something’s wrong here, something is broken. [11:06]
ERIC HUNSADER: Yes. And unfortunately -- well, that is the thing that worries us the most. The speed that the market can change today and the fact that so much -- such a high concentration is in algorithms that are simply looking at prices. And those prices are coming from other algorithms that are looking at prices coming from other algorithms looking at prices. And somewhere along the line there those prices have to be coming from economic reality somewhere, though there doesn't seem to be people who are basing their trading on economic reality or don’t number high enough or are not fast enough to prevent a rapid slide from developing. [45:59]
ERIK: Well, the thing that certainly occurs to me here is that we hear about high frequency trading supposedly benefiting us by providing additional liquidity. If I go back to what’s the purpose of capital markets, it’s to provide for the efficient formation of capital to support the growth of business and the economy. We’ve got a lot of computers participating in this card game that don’t seem to be helping to deliver to society the intended purpose of capital markets. They seem to be if anything to be increasing the costs of the overall system for everyone by making it so complicated and creating such high demands on the exchange computer systems, that anybody who’s not operating at the sub millisecond level really can’t compete with them. [46:43]
ERIC HUNSADER: What’s amazing is with electronics it should be very transparent. In other words, I should know more about my $20,000 order of Apple stock than I do about my $20 order from Amazon who track it down. I will know exactly what happened to it. I’ll know exactly what the price is, but with my Apple trade all I get is this was what the price you were executed at, have a nice day. You don’t even get a timestamp in the milliseconds or which exchanges executed on or where the best bid and offer were at the time of execution. This is what investors should demand on their trade executions. They should know exactly why they got the price they did without having to pick up the phone and ask their broker. [47:29]
ERIK: Well, even if I have that -- correct me if I’m wrong -- but if I had assurance that my trade did happen at a specific time at the advertised national best bid and offer from the SIP at that time, it is still entirely possible from what you’re saying that my broker knew how to get a better price than what was advertised by the SIP that gave me the deal at the SIP price; they made the actual deal directly with the exchange and substituted it for me and scalped the spread, the difference and put it in their own pocket and they may have even been able to paint the tape by dealing directly with those exchanges through direct messages they may have been able to influence the price on the SIP so that I got something other than what was really what my broker knew perfectly well to be the best available price in the market. And there’s no possible way for me after the fact to go analyze that and find out whether or not I got a fair deal. [48:26]
ERIC HUNSADER: So by having that information, it would be a lot easier in aggregate to say something’s not right. Over time, seeing that you’re always -- to have that information to know where it got routed to for example and what that order router saw the best bid and offer at the time for example would allow regulators to piece things together a million times better than where it is today. [48:50]
ERIK: If there was actually an audit trail that they could follow that was deterministic and really told them what happened after the fact, which as you and I both know, you could design that into the system if that had been a design requirement.
ERIC HUNSADER: Reg NMS. If it was simply followed and enforced we would have that audit trail. [49:08]
ERIK: Let’s go back to that because the research that you’ve done on this is really outstanding. I think the data that you've collected on your website, you've put some graphs and charts together that use color to very graphically depict exactly what happened, what order it happened and you've revealed compelling evidence that shows that Reg NMS is not being followed. You’re showing that the routing of these quotes and trades could not possibly occur within Reg NMS.
Now, I should probably know better than to ask this and I’m crossing my fingers, but I think or I would like to think that as a result of all this, regulators should be totally on top of the situation, they should already to be working closely with you to analyze your finding and make sure that any impropriety that might have occurred during the Facebook IPO, such as those other symbols trading outside the NBBO should be identified, catalogued and prosecuted to the full extent of the law. I’m crossing my fingers now, Eric. Please tell me you’re working closely with regulators that they’re well aware of these issues. [50:13]
ERIC HUNSADER: Actually, I heard from an academic -- a respected academic the other week that several at the SEC are furious with us.
ERIK: They’re furious with you? What did you do?
ERIC HUNSADER: I don’t know.
ERIK: So you’re doing some of the work that they should be doing, You are publicly exposing that there is data that is publically available that is verifiable that proves that things are not being routed according to Reg NMS that the industry is not processing customer’s orders in accordance with the law and they’re not happy with you because you’ve shown them up. I mean that’s crazy.
ERIC HUNSADER: Right. That’s crazy. And so I tend not to think of people as being crazy, but I just can’t explain it, honestly. You know, we take a very data neutral position; we explain what happened and we always show the things that absolutely have been thoroughly checked by us multiple times and multiple ways and playing devil’s advocate. So we're not sitting here you know, screaming, crying wolf all the time or whining if you will. And we're actually of the position that we don’t want to play cop. We understand that Wall Street has always been like this and always will be like this, but it’s affecting everybody in a major way now and it’s been very frustrating. I’ll leave it at that. [51:40]
ERIK: It’s really crazy what the regulatory environment has come to. I think you’re in a situation as I imagine Harry Markopolos must have felt having given the SEC damning information on Bernie Madoff three or four times and it was ignored.
ERIC HUNSADER: What happened to Facebook is so crystal clear and there is so many things that went wrong with it and it’s just one stock the that regulators have to look like; it’s not like flash crash which was billions of pieces of information. This was very simple and contained and we've kind of internally here drew a line in the sand, there’s no point in really publishing anything else until we see what actually comes out of the Facebook IPO because that’s kind of the litmus test of where the regulators really are. [52:28]
ERIK: Would you like to see some of our listeners send any communication to SEC encouraging them to look at your results.
ERIC HUNSADER: I think it would be more beneficial if they wrote to their Congressmen.
ERIK: The only way that change ever happens in a free society is if people actually take an interest personally and make it their job to make something happen. So, Eric’s information at Nanex.net is so compelling on this Facebook thing. There is potentially a hundred billion dollars of wealth and I base that number on the idea that IPO’s will very frequently pop to the point where they trade double the offering price before the first day is over. We saw the opposite happen. So there was a loss of wealth for the entrepreneurs and founders who originally created Facebook that got lost as a result of all this and I hope people will seriously consider writing their congressmen and perhaps sending a link to Nanex.net where they can find more of this information.
Eric, before we close, I want our listeners to know first of all we chose Facebook as you said because it’s so timely and it’s so important and it underscores just how bad the situation has become. But for people who are interested in this, you have done similar analyses on quite a few other events; the flash crash certainly being one of them. In that case you presented very clear evidence that again disproves the official story that a fat finger trade caused the whole thing. Unfortunately though, we are out of time so our listeners will have to read about that topic and many of the other research that you've done from your website. Please tell them where they can find your work. [54:01]
ERIC HUNSADER: It’s at www.nanex.net.
ERIK: Again, you can find more of Eric’s work at www.nanex.net. And again, that’s “.net” not “.com”. I hope you've enjoyed this episode of the Next Level, my guest has been Eric Hunsader of Nanex.net. If you’d like to suggest other advanced topics or guests for future episodes of the next level, please email your suggestions to me through the Financial Sense website. For the Financial Sense Newshour, I’m Erik Townsend. [54:30]
Market Microstructure expert Eric Hunsader of Nanex joins Erik on The Next Level to discuss what really went wrong in the Facebook IPO.
About Erik Townsend
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