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Facebook's IPO, NASDAQ and the Illiquid Electronic Marketplace Revisited

May 24, 2012
Facebook's IPO, NASDAQ and the Illiquid Electronic Marketplace Revisited


By R Tamara de Silva

May 24, 2012



According to the news people, there is blame to be had all around after shares of the largest initial public offering in history, Facebook (FB), lost almost twenty percent of their value in the first three days of being publicly traded. However, the lasting lesson of FB's IPO is that the financial world's increasing reliance exclusively on electronic trading often leads to catastrophic problems during critical market events.

Discontent over FB's IPO is heard from regulators and especially investors who saw the value of the their investment drop, to those who consider that the IPO was priced to perfection at 106 times its last 12 month's earnings or at 5 times the value of the most valuable (according to market capitalization) company in the world, Apple. The possibility of investing in FB's initial public offering, as in any other IPO, always bore the risk of buying an IPO at a price above its market price-that is the price it has in the publicly traded market. That said, very public examples of less than elegant IPOs are said, (whether in practice their impact is meaningful or not), to threaten the investing public's appetite for prospective IPOs. Another concern with FB's IPO is the possibility that FB, and its lead underwriters including Morgan Stanley, J.P. Morgan, and Goldman, Sachs & Co., failed to disclose material information involving new information about FB's revenue prospects during the IPO roadshow to all but a handful of their large clients-not the public supposedly because their larger clients had paid for the seemingly "inside information." Keep in mind that under the federal Securities laws, information about revenue, operations and prospects of a planned IPO are considered "material information" and must be divulged to the public in a very scripted manner. This has already resulted in a class action lawsuit filed for $15 billion in damages to the investing public. Another and more significant class action lawsuit was filed on the third day of FB becoming public, a lawsuit which picks up on the most important aspect of FB's IPO - the failure of one of the world's largest and its fastest electronic trading platforms-the NASDAQ.[1 ] Traders and investors who placed orders in FB on the day of its IPO were stuck in limbo as the electronic exchange that calls itself, "the power behind 1 in 10 of the world's securities transactions" froze and stopped working. NASDAQ's software issues constitute neither a reasonable failure nor an excusable one. Let the world take note that we will rue the end of the trading floor and open outcry as FB's IPO demonstrates how we are hostage to electronic software that like all software will fail or have glitches and show us how worthless electronic markets are when they are completely illiquid and we are held hostage to them.

All market transactions involve a degree of risk. In the law as in the markets, there is a presumption, albeit rebuttable, that the greater the amount of information a market participant has, the better able the participant is to assume and understand the risk behind a transaction. Information is valuable in decision making until such time that too much information leads to diminishing returns because the amount of information incapacitates the decision-maker and prevents him from making a decision. Risk increases dramatically when a market participant's information about price and order execution becomes nil. This is precisely what happened to the traders of close to 30 million shares of FB on the day of its IPO because of a software glitch at the NASDAQ.
What happened on the day of FB's IPO to most of the traders of FB shares is a condition little understood-the state of high illiquidity along with a lack of transparency. Transparency refers to the degree of information that is available. In a perfectly transparent market all relevant information about a market transaction from the price, order size, order flow, trading volume, identity of the traders/counterparties, all bids and offers available, etc. would theoretically be discoverable.

Transparency's value in the marketplace is best explained by its absence- a condition of opaqueness. Lack of transparency in the financial markets is called opaqueness. The environment that led to the past credit crisis was opaque. In the past mortgage debacle, few of the players knew what the baskets of mortgages they were packaging, buying and selling were actually worth. The participants in instruments that led to that last crisis operated in a very opaque if not downright murky environment. The mortgage related securities being traded from brokers to banks and between banks were not pegged to the value of anything tangible and often marked by model to myth. One could make the case that they were not even derivatives because their value was effectively not derived from an underlying anything.[ 2]

Illiquid and opaque markets occurred during FB's IPO. The opposite of illiquidity in the market is liquidity. Liquidity is the lifeblood of well functioning trading markets. In its simplest terms, liquidity is the ability of a market participant to trade at his or her price-that is to get in and out of the market at their chosen price. A history of the financial markets shows that liquidity requires a broad based collection of market makers to keep markets liquid. The more market participants the better. Without market makers, we see wide illiquid market spreads. These wide bid offer spreads in turn lead to market maker defection, to volume decreases and unfavorable trading markets for the public at large.

The regulated futures market, long a stepchild of the financial markets, with open outcry and electronic trading is the most liquid and transparent market in the world. It has been remarkably free from systemic financial crisis . . .with the exception of a certain salad oil scandal. All over the world at any given time, the value and the price of an S&P500 futures contract are known. What is more impressive is that during all major crises from the market crashes to presidential assassinations, the futures markets with open outcry have maintained their liquidity and their ability to absorb even the world's crisis level order flow or volume-without a glitch.

But most people, even corporate governance committees at financial exchanges conflate volume for liquidity-they are completely distinct. Most of the trading volume now on the largest domestic trading exchanges is in the form of electronic trading or more precisely in the equity markets, it is in high frequency trading. High frequency trading is spreading from securities to other markets like futures, currencies, derivatives, and debt instruments and to the overseas exchanges. To put this in perspective, in 2003 high frequency trading accounted for only 5% of all trading volume while today it is well over 70%.

High frequency trading firms ("HFTs") utilize a series of algorithms to take advantage of the computers' speed and proximity to the marketplaces to get information about orders and price before every other market participant. Three types of institutions comprise the trading volume of HFTs and are what is meant by HFTs: 48% proprietary high frequency trading firms, 46% investment banks and 6% hedge funds. Investment banks often have dual roles in owning proprietary high frequency trading firms and directing investment bank trade to and from these firms.

The physical exchanges like NYSE, NASDAQ and CBOE lease out space to HFTs that allows them to place their supercomputers directly next to the supercomputers of the exchanges thereby giving the HFTs advantages of milliseconds and microseconds-to see price and order information (inside information) before anyone else that is not paying for co-location and does not have a supercomputer with algorithms at the physical exchange. Their proximity to the servers at the physical exchanges give them an insurmountable advantage which they utilize to "trade," or effectively front-run everyone else's orders. Any argument that we have a level playing field in terms of price and order information in the market today is simply false.

It should be said that for the majority of the time and in non-crisis conditions, HFT works and is the major revenue generator for the electronic equity exchanges. It is argued that HFTs, like their human counterparts, are market makers in that they provide price discovery. I am profoundly skeptical of the argument that HFTs are pure market makers as this term has historically been understood because they are not active market makers. HFTs are quintessentially passive, largely using their location and software advantage to detect volume and to see order flow before everyone else and to react to it. Their market making activities are essentially different from the floor trader and floor broker who will take an unqualified risk even in the most volatile times, HFTs make markets passively by reacting to other people's activities that they are able to see happening before anyone else can. HFTs hold their market positions for milliseconds up to a few hours. Often HFTs fish for what order flow is out there by sending out false quotes to induce a reaction and therefore gauge the type of order flow that is out there in milliseconds before retracting its bids and offers-long before anyone would react to them...things non HFTs simply cannot do and what would on the trading floor be called the jailable offenses front-running and trading on inside information...but I digress.

The fact is most volume on equity exchanges like NASDAQ and NYSE are the result of electronic order flow and HFTs. However, these "traders" or algorithms are historically the very worst market-makers when crises occur because unlike their human counterparts, they largely bolt-withdrawing and canceling bids and offers en masse. Hence in times of crisis, in the marketplace dominated by HFTs, liquidity not just lessons, in the absence of human market makers, it largely disappears. What this means for all other traders and the public is that they cannot execute their orders or trade when a market crisis occurs.

This is what happened during the Flash crash of May 6, 2010 wherein the Dow dropped almost 1,000 points (the biggest intraday loss in history) losing nearly 10% of its value in seconds along with most of the 8,000 individual stocks and exchange traded funds, some of which traded 60% below their value of seconds prior before ultimately recovering. A September 30, 2010 report by the joint staffs of the CFTC and SEC to the Joint Advisory Committee on Emerging Regulatory Issues, that studied the causes of the Flash crash found that the presence of electronic trading and its interaction with HFTs during that crisis eroded liquidity, "the interaction between automated execution programs and algorithmic trading strategies can quickly erode liquidity and result in disorderly markets."[3 ]

In the case of FB's IPO, and according to sources including the trading database developer Nanex LLC, HFTs caused the NASDAQ to have to delay the opening of trading on FB because of "excessive quote cancellations," adding that this is "ironic enough, it was mostly HFTs that benefited later when NASDAQ quotes stopped coming from the Securities Information Processor (SIP) which transmits quotes for everyone who doesn't get the premium direct feeds."[ 4] In other words, NASDAQ's software could not handle the volume of bids, offers and cancellations from HFTs before FB's opening.

At this point, it would not be logical for the exchanges to commission independent research and study into the true impact of HFT on price discovery, liquidity and volatility and what this means to their markets because the volume of trades generated by HFTs constitutes their major source of revenue. The exchanges now have a conflict of interest between their vital public functions of providing price discovery and liquidity and their bottom line.[5 ] Both the SEC and CFTC noted in their joint report into the Flash Crash of May 6 2010 that "high trading volume is not necessarily a reliable indicator of market liquidity". As I stated above, liquidity erodes or disappears in a market crisis where there is a prevalence of HFTs because volume comprised of quotes and price information recorded in the milliseconds (1/1000th of a second) if not microseconds (1/millionth of a second and the current speed of many HFTs) that can be withdrawn and cancelled before ever being in danger of being executed is not only not known with certainty to be recorded, but it is "noise" in terms of its impact on price discovery and it is simply not executable liquidity.

There was a time just a few years ago when the largest exchanges in the United States were de facto public utilities. They provided the most crucial of all functions to the world, they established the price of all the metals, grain, oil and bonds the world needed to exist. Price discovery and the liquidity provided by their trading members to the world was a vital service to the world economy. The equity exchanges existed primarily to provide equity capital to businesses through the exchange in ownership of shares traded at the exchange. Now the exchanges are by and large public companies with elaborate corporate structures and well paid corporate boards whose concern has shifted away from assuring the most liquid and crisis-free markets in the world to layers of decisions made by committees all with the view to revenue and deliberately not thinking outside of the revenue generating box. This is not a problem in principle except in this case it will be because the exchanges in protecting their primary revenue source, the HFTs, will no longer function as they once did and the public will suffer. Future crises will likely result in crippling illiquidity that will harm the trading public and result in massive financial losses.@
R. Tamara de Silva

May 24, 2012
Chicago, Illinois

R. Tamara de Silva is an independent trader and lawyer

Footnotes:
1. Case 12 cv 04054 Phillip Goldberg v. NASDAQ, OMX Group, Inc. and the NASDAQ Stock Market LLC-which I am attaching here: Goldberg v. Nasdaq .pdf

2. But if they were, their value was not discoverable, or perhaps not verifiable. The values of mortgage securities were not marked to market, they were not pegged to an underlying asset, and if they were, no reasonable allowance was made for unfavorable movements in the value of the underlying assets.

3. http://www.sec.gov/news/studies/2010/marketevents-report.pdf

4. http://www.nanex.net/aqck/3099.html

5. According to one credible source, one of the Chicago exchanges has established its own HFT that will likely compete with its customers and the investing public.

J.P. Morgan's $2.3 Billion Loss as a Red Herring

May 14, 2012


J.P. Morgan's Loss as a Red Herring
By R Tamara de Silva
May 14, 2012

Much ado is being made about J. P. Morgan's disclosure of over $2 billion in trading losses and one hopes the media and regulators do not use this as yet another opportunity to completely miss the point. Wall street must not rely exclusively on its present risk models that are based exclusively on VaR and variations of VaR-it must learn to think outside its own box and anticipate worse case scenarios. We cannot afford to have many more systemic crises that threaten to bring down the financial system simply because yet again, the unexpected and un-modeled occurs.

Chief Executive Jamie Dimon's public self-flagellation aside, this loss compromises merely 20% percent of J. P. Morgan's pretax profit for the first quarter of this year. Put another way, J. P. Morgan has a market capitalization of $137.4 billion of which $2 billion comprises a bit more than 1 percent--hardly fodder for anyone's angst against quasi-public Wall Street juggernauts that seem to privatize profit and publicize loss being 'too big to fail." Mr. Dimon is wrong to assert that the trading losses were the result of hedges. It would be more wrong for lawmakers on either side of the aisle to call for hasty regulations on an industry they have never really understood and from whose pockets they are lobbied and receive the heftiest campaign contributions. A cursory look at what has happened to the Volcker Rule illustrates this point. The real lesson of J. P. Morgan's $2.3 billion loss is that Wall Street must once and for all adjust the way it manages and understands risk.

Risk management is the difference between success and ruin in the financial markets and its failure is felt around the world by even those hapless individuals who have never sold a credit derivative. No where is the importance of risk management better illustrated than to recount our most recent crises, which according to Wall Street's most prevalent measurement of risk, Value at Risk (VaR), were never supposed to happen: The market crashes of 1987 and 2000, Long-Term Capital Management, the collapse of Bear Stearns, the Savings and Loan Crisis, the crash of 1929, the collapse of Northern Rock, the Russian Debt crisis. Understanding risk is the single most important consideration for any participant, from the independent trader to the juggernaut of a Goldman Sachs.

But what does Wall Street understand by the term risk? There are many discussions of what constitutes "risk" in the financial markets but not surprisingly, there is no one definition. Typically, discussions of risk revolve around the concepts of Value at Risk (VAR), beta, delta, the capital asset pricing model (CAPM) and the Black-Scholes options pricing model (BSM). All these ways of quantifying risk are based on inarguably faulty assumptions.

This is where a refresher on the Gaussian Bell Curve enters any discussion on Wall Street's risk models. There are really only two things worth knowing about a Gaussian Bell Curve this is also taught to the legions of business school graduates who go on to write risk models as analysts and traders on Wall Street. The first is that a Gaussian Bell Curve assumes events occur in a normal distribution. What this means is that in a Gaussian Bell Curve, if events or occurrences were plotted, they would occur in the largest numbers at or towards the very center of the bell curve. All these events, plotted on a chart would take the shape of a bell curve, hence the name. Events which occur less frequently would occur towards the edges of the curve. The further the event was from the center of the bell curve, the more improbable it is to occur. This is called a normal distribution. The second thing one should know about the Gaussian Bell Curve, perhaps less well taught is that it does not predict market events very well at all.

Analysts at investment banks make models of reality with predictive capability-it is called modeling. J. P. Morgan invented Risk Metrics in 1994 as a set of financial models that were to be used by investors to measure portfolio risk. Risk Metrics like financial modeling in general, attempts to take a certain set of variables or causes and isolate them as being the very variables that account for change in financial markets. This is a bit simplistic but works reasonably well when reality happens within the fat center of a bell curve. Financial models seek to replicate financial reality much like economic models and models of human behavior that have become extremely popular in the social sciences writ large. Financial risk models like social science models suffer from all the weakness and frailty of over-simplifying reality and selectively isolating causal variables. In sum, financial risk models fail catastrophically when worse-case scenarios or even the genuinely unexpected occurs.

This does not mean we have a reasonable alternative to risk models as we humans do not like indeterminacy. We do not like to make decisions or look back in hindsight and think we made decisions by tossing a coin. On the contrary, we like to find reasons for why we made decisions and why events occurred. We tend to think we can. We have at times an irrational belief in the rational. But as Pascal once stated, nothing is more rational than the abdication of reason itself. The ability of social scientists or investment bankers to explain events through the actions of rational human actors appeals to our psyche. It is appealing simply to think we can.

Models of the financial markets like models of the human behavior in the social sciences have serious limitations. To start, they have to simplify reality. One of the ways that modeling simplifies and in a sense, falsifies reality is by making assumptions about human beings, which are not true. For example, modeling tends to assume that humans, whether in a marketplace or in a poker game are rational and that they act at all times in accordance with their best interests. This is not borne out by reality. Any cursory historical account of human behavior belies that humans act rationally. Human beings are emotional actors as much as they are rational actors.

The supremacy of emotions to the human story is only matched by our social scientists willful neglect of them. However, given a choice, time and again, we often act on our emotions and against our rational interests. Our strongest emotions keep us awake at night, they cause us physical pain, they have helped our race to achieve beyond all expectation when at other times they have left us paralyzed. We think wishfully when we rationally should not. The markets are replete with examples of irrational exuberance, traders who act out of hope, fear and greed as much as they act out of a consistent rational interest in maximizing their profits. Markets historically at tops and bottoms have betrayed the irrational mob mentally of the masses of its participants.

If humans were truly rational, we simply would not have addictive behaviors like gambling, drug addiction, drinking or any self-destructive behavior. We may not even have much ill-health, skin cancer, road-rage, or obesity because knowing we should take care of ourselves, we would-this is rational. We may never purchase luxury items or clothes. We may not care so much about how our neighbors live because we would not feel envy, jealousy, sympathy or pity. The pursuit of leisure and charitable activities may well be quite different. So many of Tocqueville's observations about American life would not hold water.

But in reality, half of our brains are devoted to pure emotion. And this half has expressed itself as the stuff of life. We cannot seem to choose our emotions one at a time either. If we were, we would want to be able to love without being vulnerable to grief, to experience the wings of hope without putting ourselves in danger of experiencing disappointment or the failure of our hoped-for event. As a race, we have spent most of our time acting on our emotions and being in their grip as is borne out in our history, our mythologies, culture, our wars and literature. It is in every sense human to be irrational or at least to experience emotion. To argue that humans are rational actors is at a minimum to simplify things, but really it is not a valid assumption.

Modeling also suffers from faulty assumptions about the ability of human participants to gather, assimilate and react to information. Most models are sensitive to information. Information causes the rational actor in a model to act a certain way, presumably in a way that will maximize that actor's interests. In the real world, information is not perfect. There is misinformation. Rumors, false tips, erroneous analyst reports are example of misinformation. Some information that is available to a rational actor is false information. Even if we assumed that all the information available to market participants was correct and no false or misinformation was available, market participants would process and assimilate the information differently and at different rates. One example of misinformation and information assimilated at different times is the discovery of a report in 2008 on the internet that United Airlines was facing bankruptcy. This report was over a year old but it caused the price of the stock to drop by over 40% in a single day, before it was discovered that the report was old. In the real world, individually and collectively, we have different intellectual and ideological frameworks, we also have different levels of intelligence, among other factors that allow us to reach very different conclusions when faced with the same information. My neighbor may react to rising gasoline prices years faster than I would by immediately cutting down on his driving or purchasing a hybrid vehicle. Market participants react to identical information at various rates. One person may react quickly to too little information and another may wait much longer accumulating much more information. Sometimes waiting to act while accumulating and digesting information is not a good thing like waiting to liquidate a losing position before your losses wipe you out when acting sooner would have allowed you to cut your loss without going broke.

Another problem with financial models is that they do not account for insider information or conflicts of interest. A good idea for anyone with a year to spare would be to write a volume chronicling conflict of interest in the financial world. One of the inherent conflicts in investment banks has been the Chinese wall that is supposed to separate the investment banking and sales functions of the investment house from the research and analysis side. Some have argued that this Chinese wall did not always exist. There is an inherent conflict between the need to sell the investment banking services of a bank to the same customer who is being covered by the bank's analysts. There is an enormous and still unresolved conflict of interest in the functions of credit ratings agencies. The credit ratings agencies are paid by the issuers (their clients) of the securities they were supposed to evaluate, creating an inherent conflict of interest. If the analysts or agencies are too harsh in their coverage, then the ability of the bank to sell its investment banking services may suffer or the agencies will lose their clients.

What about the potentially insider information that the analysts obtained in covering a company and the danger that this information would travel across the room to the trading floor of the investment bank? Another conflict of interest certainly, but it is also an example of a market participant having insider information or simply information that other market participants do not have, before they have it.

Another fallacy with financial modeling is that models are required to isolate a fixed amount of causal variables. In other words, a financial model that was designed to predict the risk of an investment portfolio would be comprised of say twenty factors or variables, each of which or a certain number of which would affect a change in measure of risk to the investment portfolio. What if in reality, it was one hundred or ten thousand different variables or things that would change the riskiness of the portfolio?

The financial models of investment bank analysts and traders assign likelihood to the possibility of certain events occurring. Financial models assume a normal distribution (a bell curve) of asset returns or risk. Using a normal distribution, events that diverge from the mean or center of the bell curve, by five or more standard deviations, known as a five-sigma event, are very rare and ten-sigma events are nearly impossible. However, the 1987 market crash represents a change of 22 standard deviations. The odds of such a 22 standard deviation event occurring are so low as to deemed impossible.

In the real financial markets, events considered nearly impossible by financial models assuming normal distributions of events, not only are possible, they are occurring frequently. There have been multiple fluctuations greater than five standard deviations in our most recent past. Events that according to a Gaussian Bell Curve are supposed to occur only once every one hundred thousand years, if at all, are occurring in certain cases, several times in a decade. Dramatic market events or fat tails do occur in a greater frequency than is possible assuming normal distributions suggesting distributions are not normal. Since the 1998 Russian debt crisis, the global financial markets have experienced at least 10 events, none of which were supposed to occur more than once every few billion years.

The financial services industry is full of at least two generations of analysts, investment bankers, statisticians and of course economists, who have been indoctrinated through college, their masters and MBA programs to believe in the bell curve and normal distributions-it is beyond time that they learned to think outside the box. Alternatively, to the extent that any regulations are enacted, they should seek to once more separate investment banking from commercial banking so that as long as Wall Street relies on one VaR number, they are allowed to fail and their losses are never again shared by the public. @
R. Tamara de Silva

May 14, 2012
Chicago, Illinois

R. Tamara de Silva is an independent trader and lawyer