Bill’s right, Michael, you didn’t know it, but you weren’t simply bidding for the $100 bill. In fact, you were playing a slightly modified version of the 1930’s newspaper beauty contest game that John Maynard Keynes used as an example for the correct thought process of investing and trading in his classic economics book, The General Theory.
You see, before there were the Flash Crashes of 2010 and the mass crashes of 2008, 2001, and 1987, before there was a “Battle of the Quants” conference series, and even before the Nobel Laureate Harry Markowitz first fully applied mathematics to the calculation of risk, Keynes pointed out the most fundamental truth of markets and trading that remains in place today. He showed how winning the market game required invoking the same process one should go through in order to win the beauty contests that were then posted in the newspapers of Keynes’ day.
Just as the current media do with their provocative opinion-based journalism strategies intended to garner viewership, various newspapers had begun publishing beauty contests using photographs and write-in voting to boost circulation. Typically 100 pictures appeared and six were chosen by the readership. Eventually the women themselves who were voted to be the most beautiful ended up in Atlantic City for the “Fall Frolic,” but readers could also win by picking the six that would win the most votes. Everyone could win.
Keynes point was that this newspaper premonition of American Idol provided an object lesson for successful trading and investing. It wasn’t about picking the one you thought possessed the greatest beauty, although that seemed on the surface as the obvious thing to do. The way to win, if you were the average player sitting at home, was to choose the women other voters were most likely to pick.
It is not a case of choosing those [faces] that, to the best of one’s judgment, are really the prettiest, nor even those that average opinion genuinely thinks the prettiest. We have reached the third degree where we devote our intelligences to anticipating what average opinion expects the average opinion to be.
He actually described his strategy as not one derivation but two whole steps removed from what the contest appeared to be. This carries the trading analogy through. In market games, it isn’t your opinion that counts nor even the immediate opinions of others’ opinions of whatever asset you are trading. Winning the market game means that you need to deduce what the perception of others will be about, for example, GE or GOOG in the future.
Unfortunately, Michael, in our object lesson here, there is almost no way to win. You can play for the fun of it but you have to realize that the only risk management strategy that doesn’t get you into trouble is to be the third highest bidder as the bids get within striking range of “fundamental value.” Just like in trading, where you want to exit when the momentum really picks up, if you bid no higher than $80 or $85, most likely someone else will think there is still $15 to $20 of “undervalue” in this stock. “Hell, I’ll pay $90 for $100,” they decide, and then some naïve bystander who doesn’t understand the real game jumps in at $95 on the same basic logic.
For some reason, people get focused as $100 approaches, and the rules become clear at this point. Instantaneously, everyone remembers or realizes that the only way out of paying $90 becomes to bid $100, and then the only way out for you, Michael, of paying $95 is to bid $105.
They use this game at Harvard’s behavioral finance executive class, and I’ve been told of the auction ending with numbers like $120 or $130, which, frankly, reminds me of what happens in a “short squeeze.” In fact, the short-squeeze lesson brings this exercise home. Short squeezes present an easy place to see Keynes’ reality. You can lose lots of money fast if you are caught in one and, over the long haul, remembering that just because you think something is highly overvalued doesn’t mean everyone else isn’t in the throes of the fear of missing out and therefore continue to try to buy the rapidly rising stock (or gold future or whatever). I myself had an instance, in 2004, where my broker’s research showed someone’s technology to be ineffective but their marketing continued to be so good that the stock just kept on going up. I finally gave in for a big loss and I still see that ticker on CNBC at around my original entry point. In other words, so much for my opinion and for the opinion one removed from me—it was the opinions in the third degree that counted more than anything!
We go about making our market decisions based on facts we try to find but, in the end, they don’t tell us what we want and need to know. Facts can never be more than clues in a puzzle. We, if we are going to consistently get it more right than wrong, need the explicit strategy of predicting people.
You make money by correctly predicting the opponent’s future perception—not “the facts”!
Now I know this reality doesn’t go down easy. Every one of you just spent a boatload of money, time, and energy getting graduate-level degrees from top institutions. You have been training to get good at facts, and I am here to tell you that you might be better off studying to be a psychic.
It’s not that the facts, or what are likely to be the future facts, have no value at all. But they have to be put in the proper context. When will other people know the same thing? When will they see it? What other factors will obscure the truth from being widely known?
Answers to these types of questions can’t be black and white. Regardless, you need to be thinking about how the future will play out in other traders’ minds. Remembering that in the end, this is all that counts. Thinking “socially” also puts your brain in the right frame of mind to make the best judgment calls. It gets you in tune with the real game.
Question in the back of the room? Yes?
“But what about the fact that so much of today’s trading stems from algorithms executing on models, and no human really is involved?” asked a guy who had just graduated from MIT’s financial engineering program.
Yes, I get that question over and over. But think about it, where did the model and the algo come from in the first place? Sure, we have some rudimentary true machine learning now; but even behind, in a market context, some human somewhere made decisions about implied volatility and assumptions about other factors that they can and frankly often do change. It’s called “recalibrating the model” and it goes on on a regular basis. In other words, the models behind the matrix of electrons began as perceptions and clusters of ideas in human minds. Hence, no matter how computationally complex, cash flow fundamental, technical, or esoteric your analyses are, you still are searching for one thing: Why will anyone pay a different price in the future than you are paying now?
What does the price, any price, of a stock, future, or tradable bond really actually mean anyway?
Take the price of AAPL today. What does it tell you? At any given moment, doesn’t it simply reflect the net perception of the value of Apple as a company? Actually, does the price of the stock reflect market players’ views of the value of the company today or does it reflect their view of the value of where the stock might be trading at tomorrow? Or wait, there is even another possibility. Does the price simply communicate that more people believe Apple Inc. will make more money next year than they did last year? We can go one—right after great earning you might see a pop in the price—“Wow, everyone is buying iPads”—but wait … if everyone is buying them now, who will be left to buy next year? Maybe their revenues will decrease.
Price reflects perception, and perceptions can stem from almost innumerable combinations of factors.
Market numbers, therefore, differ as a category from other kinds of numbers such as arithmetic or algebraic numbers. The trading price of AAPL or a 10-year Treasury only ever conveys the collective but relatively momentary judgment call of everyone participating in that market at that moment. A market number contains nothing precise or absolute in and of itself.
Furthermore, the saying “that’s what makes a market” comes into play here in that you’ve always got to have a buyer and seller, right? In a pure world, it’s logical to assume that buyers buy because they think the price is going higher and sellers sell because they think it is going lower. Sure, there are forced buys and sells that impact the market, particularly when it moves in one direction over a relatively short period of time; but generally speaking, we can accept that transactions have two parties with mostly opposite perspectives.
Therefore, a single, very underappreciated, fluid, and elusive force prints each and every price to the tape—the joint perception, for whatever reasons, of two human beings or the algorithms they control. Because the number by definition quantifies the perception, it appears orders of magnitude more factual than it is.
Every single price at every single moment now and forever will be only a perception—nothing more and nothing less.
To make matters even more perceptual and less precise, the numbers change every nanosecond. Give them a whole day and they may change quite dramatically. A whole month, year, or decade and who knows if 100 means 100 or if it means 500 or 1? Prices really are a bit like all of those people in all of those cities all over the world who say. “Don’t like our weather? Stick around an hour, it will change.”
Truth only partially exists in stocks when a company goes bankrupt and their stock goes to zero. Aside from that, whether AAPL goes higher or IBM goes lower in and of itself tricks us into thinking we are playing a numbers game. Even the common term “expected value” in statistics gets glossed over. We focus on the value part and not the expected part. Expectations are simply that, what we think will happen—but certainly, unless you are an extremely talented psychic, not what will actually happen!
At the moment of an IPO or merger, something akin to a truth may also fleetingly exist. But what happens when trading starts the next day? If nothing else, the fact that the next buyer or seller can bid higher or sell lower, for whatever reason they may have—perceived value, need to raise cash, or for want of a manipulated perception created for other market participants—proves that “truth” simply does not exist. What if a news story or a no-bid situation in one corner of the market causes a reaction and a selling or buying wave moves quickly through other markets? When prices are down, you can argue that they are undervalued or you can argue that the trend is down and prices tend to keep going in the direction they are going.
Finally, what about the ever-present trading question of “what if I just hold onto it a little bit longer?” The essentially infinite number of choices when it comes to the question of how much longer to hang onto a trade means, by definition, certainty and truth cannot exist.
It might seem like I am beating a dead horse here. Yet I know that in the back of your minds, this imprecise business of perception goes against the grain of almost everything you learned in school. It gets said without anyone actually saying it—somewhere there is a mysterious formula for the truth in the market and all assets traded on one. We semiconsciously keep looking for it and that is a waste of time—time which can be better spent putting our numbers into the proper context.
I’m not saying that mathematically based analyses should be thrown out. I don’t mean that at all!
However, the math we do should have the right purpose, and you shouldn’t mistake models for truth when they are only a clue to the real question.
If the real question is, “What will other player’s perceive in the future?”, then quantitative analyses make sense in their right context—as clues but not as answers.
From my perspective this remains the proverbial elephant in the room even today with all the post-crash talk of irrational behavior. Markets serve many functions in the overall economic picture but their actual operation, the waves that move the numbers in one direction or another are more social (and by that I mean the perceptual waves that travel from market player to market player) than anything else.
For example, the fear of missing out or not getting the trade the other guy got plays a huge role. It’s part of something called “regret theory”—a particular type of academic decision model that factors in the role of regret or, more importantly, the prospect of regret. The potential for regret drives prices in ways much more profoundly than generally recognized, particularly in bubbles and upward pushes.
Call it Shull’s “social market hypothesis,” if you like, but remember, this game remains messy and imprecise and political and variable—an endless poker game where even the cards don’t have clear values. Internalizing this truth and organizing your market strategies around it automatically puts you in the very desirable “one-up” competitive position against those who continue to fiddle with numbers while not understanding the essence of the game.
Now, obviously, trading couldn’t occur without numbers. We can’t completely ignore them if for no other reason than we need higher ones than we had before to be making any money.
But the process for growing bigger numbers differs from what it seems. Think of it this way: our lives happen via our bodies, but our bodies alone are not our lives. Transportation happens via trains, planes, and automobiles, but the trip isn’t the plane. In each case, the “container” (the human body or the plane) is the envelope, the transport mechanism, the vehicle. The end goal, however, exists beyond the mechanism.
Likewise, when it comes to markets: numbers actually serve a whole different master—a master less precise, more artful, and much more subject to interpretation—the function of a language.
Languages, even those based on the same structure as American, British, and Chinese English, convey or attempt to convey meaning from one individual to another. The same word in two dialects might mean something completely different; ditto for the same word in two different contexts. Take the word “fixture,” for example. If you live in the United States, you most likely think bathroom faucet. If you live in England, you hear the word fixture and immediately think about the schedule of your favorite football team.
Words never amount to more than subjective symbols. Symbols deliver a meaning but they exist to refer to something else and inherently have none of the intrinsic or immutable law of 2 plus 2 equals 4.
Solving the eternal puzzle of markets depends entirely on your ability to fluidly wield the sword of numbers as a language and not as a law.
Mistakenly co-opting numbers’ language role into investigative formulas intended to discover some definitive answer contributes directly to financial mayhem at every level. It misleads us into thinking we have indeed managed the risk of the numbers deflating on us. It tricks us into believing that we know what the future will bring.
Think of it this way. If the numbers in the market’s case serve only as breadcrumbs to the real answer (other players’ future perception), it makes no sense to focus all of our intellectual firepower on the bread (wheat, rye, or pumpernickel). We need to think in terms of why the crumbs of clues are developing in the way that they are.
The optimal risk management would put quantitative analyses in the proper qualitative contexts to deduce their real meaning. Had this kind of qualitative, interpretive language perspective been in place in the years preceding 2008, then the anecdotal accounts of the fairly widespread fear and anxiety may well have had a shot at being heeded.
Instead, back in 2008, what you had were assets that were making money right up until they weren’t, only a few players to trade in them, and then a fast-spreading rumor that the music was about to stop. But if a common sense context, instead of the alchemy of mixed credit classes, had been able to even get a seat at the table, then more players might have been on the short side. If more people had been shorting those complex credit instruments, the demand would have been mitigated and most likely so would have the price. Had that happened, the billion- and trillion-dollar bonfires might not have been quite so dramatic.
This holds true for any market decision. People who got annihilated buying the first dip when the Internet bubble cracked could be told the same thing. Ditto for the people who shorted it in 1999 when it had a year and a blow-off top to go. And certainly double-ditto for the people who could have looked past the relatively mundane returns of Bernie Madoff. Systematically checking for the human or social perception context would have led to wider awareness and less acting out of the insidious fear of missing out that pervades the trading mind.
Know that you are trading or investing against people who at the end of the day read the same books you do. You want for people to see what you do—just after you do. You don’t need to know what everyone is doing, you only need to imagine that the people you are buying from or selling to are indeed more like you than you tend to think.
Traders get scared that they have to find something no one else knows. They worry that Goldman Sachs controls the whole market. Trust me, I know traders at Goldman and at plenty of other major institutions; they are actually human and they don’t have some exclusive information unknown to the general population. They do, however, think in terms of social markets whether they realize it or not. In fact, in my experience, the better the trader, the more they think in terms of the human perception waves that certainly drive short-term markets. Regardless of any day’s market rhythm, their thoughts naturally gravitate first toward how the price action is being perceived by their direct trading competitors.
Each of us has the skill to understand others in at least two specific ways: directly, when we can see another human being’s physical movements and indirectly through symbols, when we can only see reflections or artifacts of their activity. Research shows that the brain uses a different system when it looks for people in symbols versus when it can see the actual person, but everyone’s got both systems. Some of us use them better than others and that alone may explain “natural born traders,” but we also can all get better at using what comes naturally.
Think about it. We all have to predict what other people are going to do almost every moment of our lives. Scientists call this mental ability “theory of mind,” or ToM for short. It simply means consciously (or usually unconsciously) working with a theory of what is going on in someone else’s head. In short, it is pattern recognition of likely human behavior.
We can see it most easily in any kind of navigating down a street or highway to get somewhere. For example, here in New York City, we do it walking down Lexington Avenue in particular. As some of you are new to New York, let me explain that Lex bisects the Upper East Side and in order to get anywhere you simply have no choice but to dodge and weave through the delivery people, the doyennes of Park Avenue, and the typical tourists trying to figure out which restaurant is and which isn’t an authentic local joint. Outside of New York, everyone does it when they drive, otherwise every non-showroom car anywhere would be dent laden! In other words, we, essentially without much conscious thought, take in the scene and infer what others will do next. If we weren’t all doing this as we go about our business, we’d all be running into each other much more often than we do!
But here’s the good news, something I have been talking about since 2008 at least. This natural ability that we all have showed up in a groundbreaking study as the key to accurately reading markets. Called “Exploring the Nature of ‘Trader Intuition’,” the work showed (in the lead author Antoine Bruguier’s words): “We find that skill in predicting price changes in markets with insiders correlates with scores on two ToM tests.”
Now, if there is only one thing I don’t need to educate you all on, it is statistical correlation. I can assure you, however, that this author didn’t use the word incorrectly or lightly. Finished as part of a PhD program at the respected research school of California Institute of Technology, the paper also went through multiple reviews by the prestigious Journal of Finance, where it appeared last October. But some of you may be a bit worried about the term “insiders.” No, it doesn’t mean Raj Rajaratnam, although clearly when anyone pursues inside information they are indeed directly pursuing a social markets strategy. In this case, the term “insiders” refers to players in an artificial market who traded in that market with a more detailed understanding of the dividend payout scenarios. That understanding caused them to trade their experimental assets in a slightly different way, but the important part comes when completely uninformed players in the next phase of the experiment were asked to detect whether there was effectively intentional buying or selling.
In the second part of the experiment, some players could see the purposeful price movement and some couldn’t. They were all playing while an fMRI machine recorded images of their brains. Researchers could then infer what kind of thinking was going on from what parts of the brain were being more heavily taxed. Each participant pushed a button each time they saw what they thought was purposeful buying (and, in fact, they also paid a small penalty if they missed it in order to keep their attention).
The team found that “increased activation” occurred in areas of the brain known to be key to theory of mind skills when the artificial market data had informed buyers and sellers in it versus when it did not.
But here’s the real kicker of this particular experiment: the parts of the brain typically associated with formal mathematical thinking showed essentially not much at all going on!
Now I realize that this result could be a tad disconcerting, but I am confident, that with the training and resulting skills this group has, you will be able to capitalize on your natural theory of mind skills and put your entire mathematical prowess to better work—when you do so in the right context of trading other people’s current and future perceptions (even if instantiated through computer models and algorithms).
Does anyone here happen to recall seeing or reading the issue of Vanity Fair magazine that was out last summer, the one with a very young and beautiful Elizabeth Taylor on the cover? Now I know you all probably don’t put Vanity Fair at the top of your regular reading lists but this one also highlighted an article about a very well-known (albeit normally very private) trader who clearly has got it down when it comes to reading markets as people’s perceptions and not as numerical truths.
The article described Stevie Cohen, a nine-time repeat member of Absolute Return magazine’s “The Rich List” (which has been running 10 years), who has been described as having a “Rain Man”–like gift for reading stock tickers. Gary Goldrin, formerly the CEO of his clearing firm, was quoted as saying: “I’ve seen all his records, hundreds of thousands of trades, all of it, and my conclusion is simply that the guy is an artist. He looks at a stock market in chaos and sees order. He was just right over and over and over.”
I know the SEC is currently snooping around his offices but even if they were to find something not right, it doesn’t change Cohen’s legendary ability. Reportedly, beginning in the spring of his ninth-grade year and continuing right through college at the University of Pennsylvania, Cohen “did far better at the poker table than in the classroom.” So he graduated to the windows in front of the Philadelphia Merrill Lynch window to “watch a stock go by at say, 50 … 50 … 50 … 50 …. And then it might go up or down a tick. You could see the trade happening. You could just watch it in slow motion. And later, not right away, I found I was pretty good at guessing which way those numbers would go…. There’s kind of an art to reading the tape. I can’t really explain it: It’s about pattern recognition.”
Bruguier’s study explains it, at least in my opinion. For whatever reason, and maybe someday we will find out, Cohen clearly has well-honed people-reading skills—even if he doesn’t know it. The question of natural-born traders gets asked over and over and over, and I think we really do have our answer in this research about leveraging one’s theory of mind abilities versus relying on what we can see through the masquerade costume of a numbers game.
I am sure you all know that almost a century earlier, Jesse Livermore, memorialized in the book, Reminiscences of a Stock Operator, said essentially the exact same thing. Originally published in 1923, ostensibly as a report of a series of interviews with the legendary stock picker, the book indicates him to say “interested in the behavior of prices … I could remember in detail how the prices had acted on the previous day, just before they went up or down…. A battle goes on in the stock market and the tape is your telescope.”
Actually, my original trading mentor, Don Winton, had a similar talent too. When I first began trading, we would sit watching a spreadsheet of quotes flashing. We could see the bid price, the offer price, the last trade, and the supposed amount of stock available for sale and being bid for. The numbers would flash, just like Cohen says, and Don would say, “It’s going up” (usually when I thought it was going down!), and I would say, “How do you know?”, and he would say, “Just watch. Can’t you see there are buyers?” Another guy we traded with, Eddie, always talked the same way: “they are buying or here they come to sell.”
I realized one day that everyone I knew who could make a lot of money on a regular basis talked about the markets in terms of people. Think of the best market players you know, whether they run a billion dollar hedge fund or trade their own capital. If you listen over their shoulders you hear them speak to the screens as if the monitors can hear. Implicitly, their perceptions are not about the bars, lines, and formulas, but about the people behind the flashing electrons.
Anthropologists term it anthropomorphizing, or giving human qualities to non-human objects—but of course that would be wrong. I actually thought that at the time. Being right out of graduate school I kind of thought they were taking this numbers puzzle (yes, I admit it) and trying to turn it into something it wasn’t. Then it hit me—in the case of reading markets, it wasn’t infusing something non-human with humanity, it was simply seeing it for the truth it is.
In fact, when he wasn’t around and I wasn’t intimidated by Don’s book-like reading of the market, I found that in fact I could do it, too. The owner of our trading firm, a gentleman named Robert Kanter, told me I had the best instincts he had ever seen after I had shown him the charts on a series of drug stocks on a Friday; and on Monday, they were all up a dollar or more. (He also said he thought a woman couldn’t trade but that, dear class, is a story for another day!)
As I became more and more subject to my own schooling as a trader, I became more what I now consider the literal victim of the “markets are only probability” teaching. It took a long time, and it wasn’t really until my first glimpse at this study back in 2007 that I realized why not only me but many other traders I knew could at least sometimes read the language of the market almost as easily as they can read the printed word.
The lesson?
Intentionally resolve to evaluate the social/human context through which you analyze your data, projections, or probabilities. Do this first for yourself as a risk management tool, i.e., what are the emotional and social pressures playing on you, and second as a strategy tool for understanding market action.
Andrew Lo, director of the Financial Engineering Lab at the Massachusetts Institute of Technology, has proposed the adaptive markets hypothesis. It is based on the idea that an ever-evolving ecosystem offers a superior model for markets. If you ask me, this idea makes complete sense given that biological humans would naturally produce biologically behaving markets. How could they do anything else? It would be the underpinning to my social markets hypothesis; as social creatures predict what other social creates will be doing, some will win and some will lose.
Bill looked over at Michael with a quizzical look. He mouthed, “But how?” Michael figured it was time to ask and raised his hand. “Denise, I sort of get what you are saying, but wouldn’t it be a lot harder and even impossible to analyze the social underpinning of the market? I mean, how would you know who and what they are really thinking, and wouldn’t there be so many variables?”
Michael, I am glad you asked. First, remember that all human brains basically work the same, at least at a fundamental level. We will get into this, but whatever emotional-social context any one of us exists in is actually the foundation for our beliefs and perceptions. (David Brooks, in fact, has just released a book on this very subject, The Social Animal, and I recommend it to all of you.) Knowing this makes it easier.
Let’s start with a hypothetical ABC Fund that strategically includes human analytics and predictions in devising trading ideas and risk strategies. In the summer of 2008, no algorithm for massaging historical data could ever have foretold that if a major investment bank like Lehman Brothers went bankrupt, AIG would also be severely wounded. No historical precedent with the then-set of factors existed. Meteorologists, however, don’t quit because they cannot say for sure where a hurricane will make landfall. Our ABC Fund looked at the drama of Bear Stearns’ demise and the pervasive chatter regarding who else might fall most likely noted that the cries of moral hazard and the understandable potential limits of then-Treasury Secretary Henry Paulson’s and Federal Reserve Chairman Ben Bernanke’s appetite for bank rescues. (Which does by the way speak to the power of social context also, i.e., if we accept the social premise, then we can apply its inferences to “other” people.)
For ABC Fund, “A bank fails” would have topped the human scenarios list. So if you believed that the possibility of a bank failing could become very real, you could think what kind of trades could that produce. Our ABC Fund would have at least flat financials, particularly AIG, as a risk management strategy or maybe even more likely, short as a tactical bet.
Conversely, numbers alone, in an undervalued or mean reversion strategy, very well may have justified being long Lehman or AIG, given the extreme prices at which they were trading. Furthermore, if Andrew Ross Sorkin and his reporting in Too Big To Fail is to be believed, and I see no reason why it shouldn’t, essentially no one, not even AIG, fully realized what would happen.
Of course, many a Black Swan believer will respond with the criticism that I can only see this because I know now about it. But I disagree. If we are smart enough to come up with the complex models we do, we are certainly smart enough to capitalize on skills we all have—people prediction—and start doing it better. In fact, I will go one step further and predict that for those who adopt this strategy, most of the swans in their view will remain white!
Think of an auction. Christie’s or Sotheby’s publish their expected (or hoped for) sales prices in terms of what prices are conveyed by—numbers. Auctions, however, frequently end up with the art changing hands for prices substantially different from what the experts anticipated.
What can we ever infer about the meaning of any particular price? In an auction, the last price influences the next price and stems from the antecedents of previous prices, but neither are ever a straight line. Someone bids out of expectation for reasons unknown to the crowd. A telephone bidder mysteriously bids even higher.
Bidders worldwide are competing to buy a valuable asset and likewise sellers are hoping to simultaneously sell their “treasure” to the higher bidder. So while trading isn’t the same as auctioning off both the house and the contents from the most expensive house in town, it is akin to having Internet access to all of the auctions all over the world at the same time. Diversifying means picking and choosing the best combination of what will work in your house.
So to get going down the right mental path, we need to think of the realities that all come into play in an auction. A bidder wants something and he or she wants it bad. So does another bidder. They bid beyond any previously stated “objective” assessment of the value. But, where did that, or any market value assessment, even come from? Ahh … what other people paid for it the last time!
Let’s imagine that an auction for antique convertibles is being held in Palm Beach. One hundred cars sit on pedestals ready to be on the auction block. But this particular auction differs from the typical one squawking auctioneer in front and bidders with paddles in the audience. The auction house decided to try something a little different this time. Instead of one car at a time, they decided to auction all the cars at the same time. Each owner (or seller) will stand with an auctioneer and 100 auctions for individual cars will happen simultaneously. Ten thousand people have shown up (or are on the simulcast) to bid for these 100 convertibles So we have 100 cars, 100 sellers, and 10,000 potential buyers. (Some are just trading in simulation so they won’t actually be buying but they may make a bid or two.)
As the bell rings, what happens? For one thing, it sounds chaotic. But is it? No, course not. Certain buyers came knowing they wanted to go home with three cars and they had a maximum of $100,000 to spend. Others came thinking that if they get a good deal, they’ll buy one; and still others showed up thinking that they want that cherry red Mercedes at any cost! If all goes well, all 100 cars will sell above their expected price.
But what if all doesn’t go well? Can you imagine how this scene would play out? What if someone shows up at the auction with a report from the local Better Business Bureau regarding a complaint about the official repair shop of the auction house? What if someone tells the person standing next to them that he has heard that some of the cars were in accidents but that fact has not been disclosed to the public? Will that guy bid? What about the other guy he told? Or, will that second guy go tell his wife who will run to stop her best single friend from over-bidding on the lily white 1973 Mercedes coupe? And if that happens, if she backs out suddenly, what will happen to the auction going on right next to that one?
In market terms, let’s take the alarming Flash Crash of May 6, 2010. Even without knowing the literal communication networks and bottlenecks, it gets a bit easier when you understand the fundamental auction model of markets.
Reportedly, one firm tried to sell 75,000 futures contracts valued at more than $4 billion. If truckloads of more antique convertibles started pulling into our Palm Beach auction, what would happen to the buyer’s willingness to bid up the prices? In fact, would there be any buyers at all?
In the case of today’s hyper-computerized market and the unfortunate reality, in my opinion, of so many venues where one can send their order, it is very easy to have a situation where at one simultaneous auction no one wants the goods. Word travels blindingly fast to the other auctions and, instantaneously, the value of that stock or piece of art or car plummets.
Furthermore, the no-bid situation defines essentially all market melt-downs. We call it a “liquidity crunch” but that simply means there are no buyers for those who want to sell. In 2008, the market, for what were known as structured financial products, actually had very few players overall. It had no central marketplace and the trades were done direct from buyer to seller with no middleman.
I realize that literally millions of questions, answers, and words in print have analyzed what happened. But at its core, once one of the main players let the cat out of the bag that they weren’t bidding anymore, two things happened. Everyone else questioned whether they should be bidding. Think musical chairs where everyone sees that one chair is gone and someone isn’t going to get a seat. Then as a courtesy of the way prices of assets are declared daily at the close of business, known as mark-to-market accounting, everyone had to mark down the value of similar assets (artwork or convertibles) on their books. As assets were marked down, none of the relatively small number of eligible players wanted to bid for more and that in turn drove the prices that could be agreed to for any sale even lower. It is an auction and when word spreads through the crowd that all the Monets (or Mercedes) are knock-offs of the real deal, the prices plummet!
As the saying goes, and as many a great thinker has pointed out over time, perception is indeed reality.