Traders Gonna Trade

Your brackets should be in by now. The 2014 NCAA Tournament gets underway in earnest in less than an hour. I think it’s the best three weeks of the sports year. As always, I picked my Duke Blue Devils to win in our family bragging rights bracket. Doing so has served me well in the past.

This year, Quicken loans is offering $1 billion for a perfect bracket, with the prize insured by Warren Buffett’s Berkshire Hathaway. But you probably shouldn’t play, and not just because your chances of winning are so slim. Even so, the lure of playing in a tournament pool is very strong. Millions play — even President Obama plays, if not for money (since it’s actually illegal to do so) — and lots of workers will be less than fully productive for the next couple of days as they try to keep up with what’s going on.

But today I am reminded of 1991. UNLV was the defending champion and came into the Final Four unbeaten and unchallenged. In the national semi-final, the Runnin’ Rebels met my Blue Devils, a team UNLV had destroyed the previous year, 103-73, in the most lop-sided championship game in NCAA Tournament history.

It was U-G-L-Y in 1990 and most people expected more of the same in 1991. Happily, that wasn’t what happened.

But my story today isn’t about the game itself or even the tournament, exactly. In those days, Wall Street trading houses had big tournament pools that featured high entry fees (and thus big prizes for winners) with serious bragging rights at stake. Significantly, because there were lots of traders involved, lots of trading went on. You could call most any major shop and get a two-sided market on any team to win the tournament.

This fact is noteworthy because one particular trader was absolutely convinced that UNLV was going to repeat as champions. More particularly, he was convinced that Duke would not win the tournament and shorted the Blue Devils big without hedging — expecting to profit handsomely when elimination ultimately came. In other words, he was looking to make big money on the trade and not just on the spread. Moreover, losing would mean not just lost potential profits — he would have to ante up real cash. As the expression goes, he was picking up pennies in front of a steamroller.

Our poor schlub was pretty nervous on the Monday after the UNLV upset, but Duke still had to beat Kansas that evening(ironically, it was April Fool’s Day) to win the title for the trader to have to cover his shorts. He feigned confidence, of course, but nobody was fooled. When Duke prevailed over the Jayhawks, 72-65, the fool was six figures (plus) in-the-hole.

The trader made good — sheepishly and painfully — but the brass learned a lesson. Thereafter, the big firms no longer allowed employees to organize tournament pools and trading on the pools that existed was strictly prohibited. It was even enforced. Rumor has it that this was part of a quiet agreement between regulators and internal compliance officials, who were understandably concerned about what had gone on. Wall Street pools still existed after that, of course, but they were now run exclusively on the buy-side; we on the sell-side still played, but it wasn’t the same. There wasn’t any trading that I’m aware of. And that’s a good thing.

Traders are going to trade. And without careful oversight, position limits and careful hedging, it’s inevitable that people will get in big trouble. That lesson applies to NCAA Tournament pools and to any security you might want to name. It applies to your personal portfolio too.

Good luck to your favorite teams.

Advertisement

Five Stinkin’ Feet

Investment Belief #5: Process Should Be Prioritized Over Outcomes

My first baseball memory is from October 16, 1962, the day after my sixth birthday, by which time I was already hooked on what was then the National Pastime. In those days, all World Series games were played during the day. So I hurried home from school on that Tuesday afternoon to turn on the (black-and-white) television and catch what I could of the seventh and deciding game of a great Series at the then-new Candlestick Park in San Francisco between the Giants and the New York Yankees.

Game seven matched New York’s 23-game winner, Ralph Terry (who in 1960 had given up perhaps the most famous home run in World Series history to lose the climatic seventh game), against San Francisco’s 24-game-winner, Jack Sanford. Sanford had pitched a three-hit shutout against Terry in game two, winning 2-0, while Terry had returned the favor in game five, defeating Sanford in a 5-3, complete game win. Game seven was brilliantly pitched on both sides. While Terry carried a perfect game into the sixth inning (broken up by Sanford) and a two-hit shutout into the ninth, Sanford was almost as good. The Yankees pushed their only run across in the fifth on singles by Bill “Moose” Skowron and Clete Boyer, a walk to Terry and a double-play grounder by Tony Kubek.

When Terry took the mound for the bottom of the ninth, clutching to that 1-0 lead (the idea of a “closer” had not been concocted yet), he faced pinch-hitter Matty Alou, who drag-bunted his way aboard. His brother Felipe Alou and Chuck Hiller struck out, bringing the great future Hall-of-Famer Willie Mays to the plate, who had led the National League in batting, runs and homers that year, as the Giants’ sought desperately to stay alive. Mays doubled to right, but Roger Maris (who had famously hit 61 homers the year before and who was a better fielder than is commonly assumed) cut the ball off at the line. His quick throw to Bobby Richardson and Richardson’s relay home forced Alou to hold at third base.

With first base open, Giants cleanup hitter and future Hall-of-Famer Willie McCovey stepped into the batter’s box while another future Hall-of-Famer, Orlando Cepeda, waited on deck. Yankees Manager Ralph Houk decided to let the right-handed Terry pitch to the left-handed-hitting McCovey, who had tripled in his previous at-bat and homered off Terry in game two, even though Cepeda was a right-handed hitter. With the count at one-and-one, McCovey got an inside fastball and rifled a blistering shot toward right field but low and just a step to Richardson’s left. The second baseman, who Terry had thought was out of position, snagged it and the Series was over. McCovey would later say that it was among the hardest balls he ever hit.

“It was an instant thing, a bam-bam type of play,” recalled Tom Haller, who caught the game for the Giants. “A bunch of us jumped up like, ‘There it is,’ then sat down because it was over.

“It was one of those split-second things. ‘Yeah! No!’ ”

Hall-of-Famer Yogi Berra, who has pretty much seen it all, said, “When McCovey hit the ball, it lifted me right out of my shoes. I never saw a last game of a World Series more exciting.”

Had McCovey’s frozen rope been hit just a bit higher or just a bit to either side, the Giants would have been crowned champions. As recounted by Henry Schulman in the San Francisco Chronicle, it was a matter of “[f]ive stinkin’ feet.”

Tremendous skill was exhibited by the players on that October afternoon over half a century ago. But the game – and ultimately the World Series championship – was decided by a bit of luck: that “five stinkin’ feet.”

Gaming the Numb3rs

Last night at the Old Globe here in San Diego I got to see one of my favorite plays, Rosencrantz and Guildenstern are Dead, presented as part of the Globe’s 2013 Shakespeare Festival. Doing so brought the following post to mind in that it uses the play as a springboard for discussing probability and investing. I hope you will enjoy it — or enjoy it again.

___________

Tom Stoppard’s Rosencrantz and Guildenstern are Dead  presents Shakespeare’s Hamlet from the bewildered point of view of two of the Bard’s bit players, the comically indistinguishable nobodies who become headliners in Stoppard’s play.  The play opens before our heroes have even joined the action in Shakespeare’s epic. They have been “sent for” and are marking time by flipping coins and getting heads each time (the opening clip from the movie version is shown above).  Guildenstern keeps tossing coins and Rosencrantz keeps pocketing them. Significantly, Guildenstern is less concerned with his losses than in puzzling out what the defiance of the odds says about chance and fate. “A weaker man might be moved to re-examine his faith, if in nothing else at least in the law of probability.”

The coin tossing streak depicted provides us with a chance to consider these probabilities.  Guildenstern offers among other explanations the one mathematicians and investors should favor —“a spectacular vindication of the principle that each individual coin spin individually is as likely to come down heads as tails and therefore should cause no surprise each individual time it does.”  In other words, past performance is not indicative of future results.

Even so, how unlikely is a streak of this length?

That’s So Random

When my kids were teenagers, if something was random, that was a good thing.  A really good thing, in fact.  Something funny was random.  A good party was random. Being more than a bit of a fussbudget, I objected to such usage.  I didn’t think it was correct.

But I was wrong. 

Probability, Baseball and the WSJ

As if we needed further demonstration that we suck at both math and probabilities, The Wall Street Journal foolishly suggests via headline that the Los Angeles Angels’ season is already effectively over because the team had an MLB-worst 10-20 spring training record. This isn’t the first time that the Journal has performed mathematical malpractice, of course.  But it still demonstrates a fundamental misunderstanding of how to use probabilities – whether in sports, gambling, or investments.

Gaming the Numb3rs

 

Tom Stoppard’s Rosencrantz and Guildenstern are Dead  presents Shakespeare’s Hamlet from the bewildered point of view of two of the Bard’s bit players, the comically indistinguishable nobodies who become headliners in Stoppard’s play.  The play opens before our heroes have even joined the action in Shakespeare’s epic. They have been “sent for” and are marking time by flipping coins and getting heads each time (the opening clip from the movie version is shown above).  Guildenstern keeps tossing coins and Rosencrantz keeps pocketing them. Significantly, Guildenstern is less concerned with his losses than in puzzling out what the defiance of the odds says about chance and fate. “A weaker man might be moved to re-examine his faith, if in nothing else at least in the law of probability.”

The coin tossing streak depicted provides us with a chance to consider these probabilities.  Guildenstern offers among other explanations the one mathematicians and investors should favor —“a spectacular vindication of the principle that each individual coin spin individually is as likely to come down heads as tails and therefore should cause no surprise each individual time it does.”  In other words, past performance is not indicative of future results.

Even so, how unlikely is a streak of this length?

The probability that a fair coin, when flipped, will turn up heads is 50 percent (the probability of any two independent sequential events both happening is the product of the probability of both). Thus the odds of it turning up twice in a row is 25 percent (½ x ½), the odds of it turning up three times in a row is 12.5 percent (½ x ½ x ½) and so on.  Accordingly, if we flip a coin 10 times (one “set” of ten), we would only expect to have a set end up with 10 heads in a row once every 1024 sets {(½)¹⁰ = 1/1024}.

Rosencrantz and Guildenstern got heads more than 100 consecutive times. The chances of that happening are: (½)¹⁰⁰ = 1/7.9 x 1031. In words, we could expect it to happen once in 79 million million million million million (that’s 79 with 30 zeros after it) sets. By comparison, the universe is about 13.9 billion years old, in which time only about 10¹⁷ seconds (1 with 17 zeros after it) have elapsed.  Looked at another way, if every person who ever lived (around 110 billion) had flipped a 100-coin set simultaneously every second since the beginning of the universe (again, about 13.9 billion years ago), we could expect all of the 100 coins to have come up heads two times.    

If anything like that had happened to you (especially in a bet), you’d agree with Nassim Taleb that the probabilities favor a loaded coin.  But then again, while 100 straights heads is less probable than 99, which is less probable than 98, and so on, any exact order of tosses is as likely (actually, unlikely) as 100 heads in a row:  (½)¹⁰⁰.  We notice the unlikelihood of 100 in a row because of the pattern and we are pattern-seeking creatures.  More “normal” combinations look random and thus expected.  We don’t see them as noteworthy.  Looked at another way, if there will be one “winner” selected from a stadium of 100,000 people, each person has a 1 in 100,000 chance of winning.  But we aren’t surprised when someone does win, even though the individual winner is shocked.

The point here is that the highly improbable happens all the time.  In fact, much of what happens is highly improbable.  This math explains why we shouldn’t be surprised when the market remains “irrational” far longer than seems possible.  But we are.

Much of that difficulty arises because we neglect the limits of induction.  Science never fully proves anything.  It analyzes the available data and, when the force of the data is strong enough, it makes tentative conclusions.  But these conclusions are always subject to modification or even outright rejection based upon further evidence gathering.  Instead, we crave and claim certainty, even when we have no basis for it. 

In his brilliant book, On Being Certain, neurologist Robert Burton systematically and convincingly shows that certainty is a mental state, a feeling like anger or pride that can help guide us, but that doesn’t dependably reflect anything like objective truth. One disconcerting finding he describes is that, from a neurocognitive point of view, our feelings of certainty about things we’re right about is largely indistinguishable from our feelings of certainty about things we’re wrong about (think “narrative fallacy” and “confirmation bias”).

As Columbia’s Rama Cont points out, “[w]hen I first became interested in economics, I was surprised by the deductive, rather than inductive, approach of many economists.” In the hard sciences, researchers tend to observe empirical data and then build a theory to explain their observations, while “many economic studies typically start with a theory and eventually attempt to fit the data to their model.”  As noted by Emanuel Derman:

In physics it’s fairly easy to tell the crackpots from the experts by the content of their writings, without having to know their academic pedigrees. In finance it’s not easy at all. Sometimes it looks as though anything goes.

I suspect that these leaps of ideological fancy are a natural result of our constant search for meaning in an environment where noise is everywhere and signal vanishingly difficult to detect.  Randomness is difficult for us to deal with.  We are meaning-makers at every level and in nearly every situation.  Yet, as I have noted before, information is cheap and meaning is expensive.  Therefore, we tend to short-circuit good process to get to the end result – typically and not so coincidentally the result we wanted all along.

As noted above, science progresses not via verification (which can only be inferred) but by falsification (which, if established and itself verified, provides relative certainty only as to what is not true).  Thank you, Karl Popper. In our business, as in science generally, we need to build our investment processes from the ground up, with hypotheses offered only after a careful analysis of all relevant facts and tentatively held only to the extent the facts and data allow. Yet the markets demand action.  There is nothing tentative about them. That’s the conundrum we face.

Even after 100 heads in a row, the odds of the next toss being heads remains one-in-two (the “gambler’s fallacy” is committed when one assumes that a departure from what occurs on average or in the long-term will be corrected in the short-term). We look for patterns (“shiny objects”) to convince ourselves that we have found a “secret sauce” that justifies our making big bets on less likely outcomes. In this regard, we are dumber than rats – literally.

In numerous studies (most prominently those by Edwards and Estes, as reported by Philip Tetlock in Expert Political Judgment), the stated task was predicting which side of a “T-maze” holds food for the subject rat.  Unbeknownst both to observers and the rat, the maze was rigged such that the food was randomly placed (no pattern), but 60 percent of the time on one side and 40 percent of the time on the other.

The rat quickly “gets it” and waits at the “60 percent side” every time and is thus correct 60 percent of the time.  Human observers kept looking for patterns and chose sides in rough proportion to recent results.  As a consequence, the humans were right only 52 percent of the time – they (we!) were much dumber than rats.  Overall, we insist on rejecting probabilistic strategies that accept the inevitability of randomness and error.

As I described yesterday, the great gambler Billy Walters uses a probabilistic sports betting model that is correct roughly 57 percent of time.  He expects and plans for being wrong 43 percent of the time.  Since he can’t predict the timing of his successes and failures, he has to be prepared for long losing streaks (although he obviously hopes that none are long as Guildenstern’s).  Common gambling practice had been (and often still is) to make fewer bets – to bet only on those games one is most sure of.  But that approach is not thinking probabilistically.  Walters makes as many bets as he can within the confines of his model (when he thinks the point spread is off by at least one-and-one-half points). 

For investors, the lessons to be gained here relate to diversification, a carefully delineated and bounded process, clear execution rules, and stick-to-itiveness over the long haul.  This doesn’t mean that quants should control everything.  Old school analysis and judgment still matter, perhaps more than ever since the pile of available data has gotten so large.  But it does mean that our conclusions need to be consistent with and supported by the data, no matter how bizarre the numbers or how long the streak.

Even 100 in a row.

Gaming the System

When I was a kid I had a paper route.  One of my customers was a barber who made book on the side.  Shocking, I know.  The giveaway was the group of guys always hanging around but not getting their hair cut and the three telephones on the wall that rang a lot.  Even as a kid I could tell that something was up.

Anyway, each week the barber and I had a wager.  During the NFL season, I picked a winner of the Buffalo Bills game (the Bills were our local team) straight-up.  When I was right, I got double the subscription price, before tip.  When I was wrong, the barber got his paper free.

As it happened, I won a lot of the time – obviously aided a lot by being able to decide which side of the bet I wanted, not having to worry about the spread and (in no small measure) by the Bills being pretty lousy during that period.  One Monday after (yet another) win for me and loss for the Bills, I was feeling pretty haughty (imagine that) and started talking smack to the barber (imagine that).  Finally, my exasperated barber told me something that made a big impression at the time and which resonates still, “Do it against the spread and then we’ll talk.”

We are forward-looking creatures.  We love to make forecasts, predictions and even wagers about the future.  We just aren’t very good at it.

Sports betting is obviously very big business.  Nearly $100 million was bet legally on the Super Bowl alone and CBS reports that over $2.5 billion (with a “b”) is bet at Las Vegas sports books in a year. The sports books make money – a lot of money.  But almost nobody else does, because winning in the aggregate is extremely difficult.

Pundit Tracker compiled all the NFL picks made by the ESPN, Yahoo, and CBS Sports pundits through the 2011-2012 season.  Obviously, these people are all put forward as experts and promoted as such.  Yet if you had placed $1 on each of the pundit’s picks (based on “moneyline” odds) you ended up losing a good deal of money even after removing the sports books’ commission (the “vig”) from the odds – and these picks weren’t even against the spread. My friend Mike Silver of Yahoo Sports topped the list, but even his “bets” only “earned” an 8 percent return for the year and that’s hardly worth the risk.  Spread betting only compounds the difficulty (point spread betting relates to who wins and by how much; moneyline betting relates solely to who wins, albeit with odds factored in).  In fact, had you been relying upon the advice of the “experts” at CBS Sports to bet against the spread this season, you would have lost a lot of money (note the results here).

One major exception to the general rule is the legendary gambler Billy Walters, a crucial member of the famous “computer group,” which used a careful and computerized process to make a fortune and, as a result, to revolutionize sports betting in the 1980s.  Today, Walters uses multiple consultants – mostly mathematicians – just like a hedge fund manager uses analysts and still makes a ton of money.  Walters’s process is to create his own line largely using statistical measures of the teams and then to bet when his view of a game is significantly different from available commercial betting lines.  He’s not opposed to trying to influence betting lines either.  Walters has the power, the money and the reputation to bet on teams that he doesn’t actually favor in order to move the odds. Once that happens, he lays much larger bets on the other side, the side he wanted all along.

Walters is staggeringly rich (according to 60 Minutes, he is “worth hundreds of millions of dollars”), but he claims a lifetime winning percentage of only 57 percent, as compared to the break-even of 52.38 percent (the winning percentage sports gamblers need to hit to offset paying out a 10 percent vig on their losing bets).  Even so, while he has had losing months (randomness can overcome a good process for substantial periods of time), he has never had a losing year during this 30-year streak.

But that winning streak only started after he made a major change in his approach.  By his own account, Walters lost his shirt many times over before becoming focused, data-driven and careful to play the long game (making a profit while losing 43 percent of the time requires it, especially because the losing streaks can be very long indeed).  One key is lots of bets – whenever the data suggests a significant edge – with bet size being determined by the extent of the edge.

The betting market in Las Vegas isn’t much different from Wall Street. Fed by rumor, speculation and greed, teams, like investments, can grow hot or cold for no good reason.  Moving lines is remarkably similar to market bubbles. Walters insists that “[b]etting on a ball game is identical to betting on Wall Street.”  Walters even claims that he has lost a lot of money in the markets and thinks the Wall Street “hustle” is far more dangerous than that in Las Vegas.

It should be no surprise then that many Wall Streeters have gambling histories, most prominently Ed Thorp.   For more information, read Scott Patterson’s excellent book, The Quants.  I even know a few.

I grew up in this business in the early 90s at what was then Merrill Lynch.  My decade of legal work in and around the industry didn’t prepare me for big-time Wall Street trading.  I’d ride the train to Hoboken early in the morning, hop on a ferry across the Hudson, walk straight into the World Financial Center, enter an elevator, and press 7. Once there, I’d walk into the fixed income trading floor, a ginormous open room, two stories high, with well over 500 seats and more than twice that number of computer terminals and telephones.  When it was hopping, as it often was, especially after a big number release, it was a cacophonous center of (relatively) controlled hysteria.

It was a culture of trading, which makes sense since it was, after all, a trading floor.  And it wasn’t all that different from a sports book.  Most discussions, even trivial ones, had a trading context. One guy (and we were almost all guys) is a seller of a lunch suggestion. Another likes the fundamentals of the girl running the coffee cart. Bets were placed (of varying sorts) and fortunes were made and lost, even though customers did most of the losing because we were careful to take a spread (think “vig”) on every trade. The focus was always on what was rich and what was cheap and the what if possibilities of and from every significant event (earthquake in Russia – buy potato futures).  The objective was always to make the most money possible, the sooner the better.

One of my colleagues there was an excellent trader who traced his success to his “training” as a gambler.  While in college, in the days before the internet and relatively uniform betting lines, he would find a group of games he wanted to play (via a system not nearly as sophisticated as the one Walters used and uses) and would then place bets with bookies in the cities of the teams playing.  In each case he’d bet against the team located in the city of the applicable bookie.  Because the locals disproportionately bet on the local team, the point spread would be skewed, sometimes by a lot.  Thus he had an excellent true arbitrage situation with a chance to win both sides of the bet, which happened a lot.  He traded mortgages in much the same way.

Interestingly, value was almost never at issue on the trading floor. The idea was to exploit inefficiencies and – especially – the weaknesses of whoever was on the other side of the trade right then. Michael Lewis’s wonderful first book, Liar’s Poker, re-issued in 2010 and perhaps (finally) being made into a movie, captures this culture (in his case, at Salomon Brothers) pitch perfect.

Now that the focus of what I do has changed, I am primarily consumed with finding value through investing – which must be distinguished from trading. As Barry Ritholtz puts it, “[t]rading (as opposed to investing) is more about laying out probabilities of risk versus reward; investing is about valuations within the longer secular macro picture.” I would suggest that trading is about selling what is rich and buying what is cheap while investing is about finding, acquiring and holding on to value. That distinction is, I think, the key to why so many analysts misapprehend the market relevance of another terrific Michael Lewis book (which has been made into a movie), Moneyball.

Moneyball focuses on the 2002 season of the Oakland Athletics, a team with one of the smallest budgets in baseball.  At the time, the A’s had lost three of their star players to free agency because they could not afford to keep them.  A’s General Manager Billy Beane, armed with reams of performance and other statistical data, his interpretation of which was rejected by “traditional baseball men” (and also armed with three terrific young starting pitchers), assembled a team of seemingly undesirable players on the cheap that proceeded to win 103 games and the divisional title. If that sounds a lot like the Billy Walters approach to you, you’re right.

Unfortunately, much of the analysis of Moneyball from an investment perspective is focused upon the idea of looking for cheap assets and outwitting the opposition in trading for those assets.  Instead, the crucial lesson of Moneyball (like the Walters process) relates to finding value via underappreciated assets (some assets are cheap for good reason) by way of a disciplined, data-driven process.  Instead of looking for players based upon subjective factors (a “five-tool guy,” for example) and who perform well according to traditional statistical measures (like RBI and batting average, as opposed to on-base percentage and OPS, for example), Beane sought out players that he could obtain cheaply because their actual (statistically verifiable) value was greater than their generally perceived value. Beane sought out players in much the same way that Walters seeks out mispriced spreads.

In some cases, the value difference is relatively small.  But compounded over a longer-term time horizon, small enhancements make a huge difference.  In a financial context, over 25 years, $100,000 at 5%, compounded daily, returns $349,004.42 while it returns nearly $100,000 more ($448,113.66) at 6%.  That’s a key reason why Walters is anxious to place lots of bets.

But why are successful investors, successful prognosticators and successful betters so rare?  I have three reasons to suggest.

The first is our human foibles – the behavioral and cognitive biases that plague us so readily. These issues make us susceptible to craving the next shiny object that comes into view and our emotions make it hard for us to trade successfully and extremely difficult to invest successfully over the longer-term.  Recency bias and confirmation bias – to name just two – conspire to inhibit our analysis and subdue investment performance.  Roughly half of each year’s NFL play-off teams fail to make the play-offs the next season.  Yet our predictions (and bets) disproportionately expect last year’s (or even last week’s) successes to repeat.  Another problem is herding.  On average, bettors like to take favorites. Sports fans (and even analysts) also like “jumping on the bandwagon” and riding the coattails of perennial winners. Sports books can use these biases to shade their lines and increase their profit margins.  So can the Street.

Experts are prone to the same weaknesses all of us are, of course, as Pundit Tracker’s look at the predictions of NFL “experts” noted above so clearly demonstrates.  Philip Tetlock’s excellent Expert Political Judgment examines why experts are so often wrong in sometimes excruciating detail. Even worse, when wrong, experts are rarely held accountable and they rarely admit it. They insist that they were just off on timing (“I was right but early!”), or blindsided by an impossible-to-predict event, or almost right, or wrong for the right reasons. Tetlock even goes so far as to claim that the better known and more frequently quoted experts are, the less reliable their guesses about the future are likely to be (think Jim Cramer), largely due to overconfidence, another of our consistent problems.

We should never underestimate information asymmetry either.   Information asymmetry is the edge that high frequency traders have and why Billy Walters focuses on player injuries and their impact in addition to his models.  At a broader level, it’s why it’s so difficult to “beat the market.”  Obviously, someone is on the other side of every trade.  When you make a trade, what’s your edge vis-à-vis your counterparty?

The third reason is just plain luck.  The world is wildly random.  With so many variables, even the best process can be undermined at many points.   Pundit Tracker describes the “fundamental attribution error,” the error we make when we overweight the role of the individual and underweight the roles of chance and context when trying to explain successes and failures.

While watching SportsCenter earlier this week, where the big story remained Monday night’s controversial (to say the least – it even forced the NFL to settle its lock-out of its regular officials) touchdown call that gave the Seattle Seahawks a 14-12 victory over the Green Bay Packers, I was struck by the huge impact the last-second turnaround had on gamblers.  If Seattle’s desperation pass had correctly been ruled an interception, Green Bay – as 3½ point favorites — would have won by five, covering the spread.  Instead, the replacement officials’ call shifted the win from those who bet on the Packers to those who took the underdog Seahawks.  Remarkably, that result means that as much as $1 billion (that’s with a “b”) moved in one direction as opposed to the other.

Thus a clear win was eliminated due solely to the almost unbelievable incompetence of the replacement officials.  That’s just dumb luck for bettors, and why it can be so frustratingly difficult to wager successfully on both our favorite teams and our favorite stocks. It may seem like the system is gamed, but investing successfully is just really hard (like gambling), as Tadas Viskanta so eloquently points out and I regularly reiterate.

In all probabilistic fields, like investing and gambling, the best performers dwell on process. A great hitter focuses upon a good approach, his mechanics, being selective and hitting the ball hard. If he does that – maintains a good process – he will make outs sometimes (even when he hits the ball hard) but the hits will take care of themselves. That’s the Billy Walters process analogized to baseball. Maintaining good process is really hard to do psychologically, emotionally, and organizationally.  But it is absolutely imperative for investment success.  And for gambling success too.

Against the Odds

The similarities between trading and gambling begin with the idea that both involve speculation.  But they don’t end there. 

Blackjack played with a perfect basic strategy typically offers a house edge of less than 0.5%.  However, a successful card counter who ranges his bets appropriately in a game with six decks can achieve an advantage of approximately 1 percent over the casino, with advantages up to about 2.5 percent possible in various situations.  These numbers vary, obviously, on account of skill and other factors. 

Thus a counter with an average bet of $100 is looking for a return of about $1 or so per hand or about $50 per hour.  However, variance is high.  Thus the standard deviation for that player is about $1,400 per hour.  That’s a tough way to make a living, even without casinos actively trying to prevent card counting and frequently banning those who do (except in Atlantic City since New Jersey law prohibits the banning of card counters). 

Ed Thorp, an academic and advanced math expert, is generally viewed as the “father” of card counting – he certainly pushed it into the public consciousness.  His 1962 book Beat the Dealer outlined various strategies for optimal blackjack play. It should be noted that although mathematically sound, some of the techniques described in Thorp’s book no longer apply, as casinos took counter-measures (such as no longer dealing to the last card).  In any event, Thorp wrote the book on card counting – both literally and figuratively.

A surprising number of traders either got their start in the gambling world or are also involved in it.  It should be no surprise, then, that Thorp followed up Beat the Dealer with 1967’s Beat the Market and became a hedge fund manager. As well as being the “father” of card counting, he’s probably the “father” of market quantitative analysis too (see Scott Patterson’s engaging book, The Quants for more).

Doing the probability analysis to be a card counter is similar to probability analysis done by traders, and the connection is obvious.  Today’s high frequency traders may well be, in effect, card counters (their success rates suggest that they are).  But the connection between sports betting and the stock markets may be closer still.  One of my favorite mortgage traders from the 1990s got his start towards trading as a college student gambling on sports, often betting both sides and taking advantage of bookies’ shading the line in an era before the internet and immediate information when disparities of 3 points or so between “home” and “road” bookies was commonplace. 

That said, sports bettors, like most investors, aren’t very good overall.  In both situations most are driven and undone by their emotions.  For example, recent research from Sports Insights suggests that the higher percentage of bets the favorite receives, the less likely they are to cover (favorites receiving 75 percent or more of the wagers covered the spread just 46.1 percent of the time).  This animal spirits tendency is well-known.  So much for efficient markets in sports betting at least.

But since sports bettors must pick 52.4 percent winners just to overcome their inherent 11:10 disadvantage (sports books are in business to make a profit, after all), this is very significant information.  Although good data is obviously hard to come by, most analysts suggest that the best Vegas touts have a win rate in the area of 55 percent (see here for more).  Indeed, it appears doubtful that more than 5 percent of touts have a lifetime win rate of the 52.4 percent needed for profitability. That makes sports gambling a very difficult way to make a living – the margin of error is small and, as with blackjack, variance is high. 

For example, suppose a bettor went 58-42 (a very respectable 58% win rate) over the course of an NFL season, betting $1000 per game.  That bettor’s end result would be only $11,800. You’d need a hedge fund and lots of leverage to make that a big money-maker. Yet if someone were confident that s/he could win 80 of his or her next 100 bets, s/he could turn $1,000 into $15 billion by proper proportional betting – all over the course of a single season. 

In Vegas, many touts claim ridiculous win rates against the spread.  Some (try listening to Saturday morning sports radio) suggest “investing” in sports gambling.  Similarly, most active managers would have you believe that they are all you need to access untold riches.  In each scenario, it is very possible to succeed.  But success is far less frequent than advertised and far less lucrative as well.