Barry’s Point Illustrated

Barry Ritholtz has an excellent post up this morning (as usual).  In it, he outlines his simple, 3-step process to avoid big mistakes and to keep errors manageable.

• Rule #1: Expect to be wrong
• Rule #2: Admit Error
• Rule #3: Repair

Weather forecaster Lisa Hidalgo wonderfully illustrates our need to recognize when things go wrong and adjust accordingly in the video below (even though the original error doesn’t appear to have been her doing).  You might also want to read Joe Posnanski’s fine article on Rick Ankiel swinging for the fences, a rare example of an athlete adjusting when things go wrong and re-inventing himself to save his career.

“What are you sinking about?”

As he has before, Barry Ritholtz has posted an excellent piece this morning describing a variety of things he is thinking about.  It reminds me of the following classic commercial.

As demonstrated by the poor German Coast Guard official in the commercial, if we are going to get anything worthwhile out of each other’s thinking, we need to be speaking the same language — both literally and figuratively.  But that happens all too infrequently in our business. Indeed, we can’t often agree on even the foundations of our would-be discussions.

Edge: Milevsky, Ritholtz, Zweig, Ferri and More

This year’s Edge question is What *should* we be worried about?  I put that same question to some of my friends. What should *we* be worried about?

Some of their shorter answer follow.

Establishing Your Top 10 Investment Default Settings

I pay a lot of attention to the investment process.  In that regard, every investor — personal or professional — ought to have a clear investment plan based upon appropriate personal considerations, goals and outlooks and every investor ought to stick to that plan unless and until something significant changes. But there is a crucial component of the investment process that gets surprisingly little attention:  our investment default settings.  We can use them when we aren’t sure what to do, when we’re deciding what to do, when our circumstances have changed but our plan hasn’t (yet), or when we’re just starting out. 

The idea here is that we all have default settings — known and unknown, acknowledged and unacknowledged — and that those defaults greatly influence how successful we are and become.  Having the right default setting in defined contribution plans make a big difference (more here). I would examine and apply my default settings across and throughout the entire investment process and even suggest that we need to look at our default settings as carefully as we look at anything else.

What follows are my suggested default settings.  Your mileage may vary.

The Fiscal Slope

The so-called “fiscal cliff” – a term coined by Federal Reserve Chairman Ben Bernanke – refers to the combination of tax increases and spending cuts scheduled to be implemented automatically next month without peremptory Congressional action.  As things stand now, the payroll-tax holiday will end, which means a tax increase for workers of as much as 2 percent of wages. Income-tax rates will also revert to pre-George W. Bush levels, raising taxes for nearly all taxpayers. Across-the-board cuts in domestic and, particularly, defense spending are triggered as well.  The spending cuts will go into effect because Congress couldn’t reach a deal last year during the debt ceiling crisis to reduce the deficit by at least $1.2 trillion. 

If Congress does nothing, the good news is that these three factors will likely drive the budget deficit to less than 1 percent of GDP by 2018 and then stay below that level through 2022, at which point demographics and health care cost issues lead it to start rising again.  The bad news is that these same three factors will be a major drag on an already weak economy, triggering a recession and the loss of about 2 million jobs, according to a Congressional Budget Office report issued in August.

Based (at least) on the political theatre on offer by the Sunday news shows yesterday (Tim Geithner was seemingly everywhere), the movers and shakers assume that the U.S. would not be so foolish as to pull a “Thelma and Louise” and drive off the fiscal cliff.  In other words, politicians in Washington won’t be that stupid, self-destructive and shortsighted as to let that combination of the expiration of the Bush tax cuts, the end of the reduction in Social Security taxes and the imposition of automatic budget cuts send the U.S. economy back into recession.

Moreover, a number of excellent commentators (led by Barry Ritholtz in The Washington Post; more here) are banging the drum for the idea that the fiscal cliff is more of a slope than a cliff and thus isn’t that big of a deal. Many (such as Eddy Elfenbein here) also insist that a deal pretty much has to happen.  I only disagree with the likes of Barry and Eddy with great trepidation, but I think they’re missing an important point.

While I think that the effects of going over the cliff (or down the slope) are likely to have a greater impact on a soft economy than Barry suspects and that the likelihood of a deal is somewhat less than Eddy believes, my primary and overall concern relates to something barely mentioned in the day-to-day coverage.  Notice how the Obama administration’s opening bid last week in negotiations to avert the alleged crisis included a demand for authority unilaterally to raise the U.S. debt ceiling. 

The debt ceiling is where the real fight will be and where the real risk lies (note Bruce Bartlett’s careful analysis here).  The CBO estimates that given current spending and revenue trends, the existing debt ceiling will be reached before the end of the year.  Since far too many Republicans seem willing to allow the federal government to default on its debt in order to exact further concessions on spending and entitlements (recall what happened last summer), I think the overall risk to the markets remains substantial. 

The overall strength of the U.S. economy and (especially) our ability to print money means that the likelihood that owners of U.S. debt won’t get paid should be essentially nil.  However, markets generally do a dreadful job of analyzing political risk.  One’s ability to pay is much easier to deal with than one’s willingness to pay. Such an unwillingness to pay — even if the default is only temporary and “technical” — could have enormous repercussions with respect to U.S. borrowing rates as well as to the overall strength of what is (for now, at least) the world’s reserve currency. The U.S. dollar has remained the world’s default currency and U.S. Treasuries have remained the world’s default securities largely because investors have always been entirely confident that the money invested there was safe from political machinations.  I don’t think it entirely far-fetched to wonder if those days may be numbered.

I hope Barry and Eddy are right.  But I’m plenty nervous just the same.

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.

Investors’ 10 Most Common Behavioral Biases

Barry Ritholz (of The Big Picture and a Sunday Business columnist at The Washington Post) recently contributed Investors’ 10 most common mistakes to The Washington Post Business Section quarterly investing section. It’s a commentary that he has been working on for a while — the ten topics are listed with links to longer discussions of each common mistake here. I created my own investing “checklist” (here) in response to Barry’s original list. For yet one more iteration of the theme, I offer my list of Investors’ 10 Most Common Behavioral Biases.  There are a number of others, of course, and more will continue to be uncovered.  But I think that these are the key ones.  Your suggestions of important ones I have missed are welcome.

1. Confirmation Bias. We like to think that we carefully gather and evaluate facts and data before coming to a conclusion.  But we don’t. Instead, we tend to suffer from confirmation bias and thus reach a conclusion first.  Only thereafter do we gather facts and see those facts in such a way as to support our pre-conceived conclusions.  When a conclusion fits with our desired narrative, so much the better, because narratives are crucial to how we make sense of reality.
2. Optimism Bias.  This is a well-established bias in which someone’s subjective confidence in their judgments is reliably greater than their objective accuracy. Indeed, we live in an overconfident, Lake Wobegon world (“where all the women are strong, all the men are good-looking, and all the children are above average”).  We are only correct about 80% of the time when we are “99% sure.” Fully 94% of college professors believe they have above-average teaching skills (anyone who has gone to college will no doubt disagree with that). Since 80% of drivers  say that their driving skills are above average, I guess none of them drive on the freeway when I do.  While 70% of high school students claim to have above-average leadership skills, only 2% say they are below average, no doubt taught by above average math teachers. In a truly terrifying survey result, 92% students said they were of good character and 79% said that their character was better than most people even though 27% of those same students admitted stealing from a store within the prior year and 60% said they had cheated on an exam. Venture capitalists are wildly overconfident in their estimations of how likely their potential ventures are either to succeed or fail. In a finding that pretty well sums things up, 85-90% of people think that the future will be more pleasant and less painful for them than for the average person.
3. Loss Aversion. We are highly loss averse.  Empirical estimates find that losses are felt between two and two-and-a-half as strongly as gains.  Thus the disutility of losing $100 is at least twice the utility of gaining $100. Loss aversion favors inaction over action and the status quo over any alternatives. Therefore, when it comes time for us to act upon the facts and data we have gathered and the analysis we have undertaken about them, biases 2 and 3 – unjustified optimism and unreasonable risk aversion – conflict. As a consequence, we tend to make bold forecasts but timid choices. 
4. Self-Serving Bias. Our self-serving bias is related to confirmation bias and optimism bias. Self-serving bias pushes us to see the world such that the good stuff that happens is my doing (“we had a great week of practice, worked hard and executed on Sunday”) while the bad stuff is always someone else’s fault (“It just wasn’t our night” or “we simply couldn’t catch a break” or “we would have won if the refereeing hadn’t been so awful”).
5. The Planning Fallacy.  In his terrific book, Thinking, Fast and Slow, Nobel laureate Dan Kahneman outlines what he calls the “planning fallacy.” It’s a corollary to optimism bias and self-serving bias. Most of us overrate our own capacities and exaggerate our abilities to shape the future.  The planning fallacy is our tendency to underestimate the time, costs, and risks of future actions and at the same time overestimate the benefits thereof.  It’s at least partly why we underestimate bad results. It’s why we think it won’t take us as long to accomplish something as it does. It’s why projects tend to cost more than we expect.  It’s why the results we achieve aren’t as good as we expect. 
6. Choice Paralysis. Intuitively, the more choices we have the better.  However, the sad truth is that too many choices can lead to decision paralysis due to information overload.  For example, participation in 401(k) plans among employees decreases as the number of investable funds offered increases. We are readily paralyzed by too many choices.
7. Herding. We all run in herds — large or small, bullish or bearish.  Institutions herd even more than individuals in that investments chosen by one institution predict the investment choices of other institutions by a remarkable degree.  Even hedge funds seem to buy and sell the same stocks, at the same time, and track each other’s investment strategies. That affinity fraud  (e.g., Bernie Madoff fleeced the Jewish community to which he belonged) is so common is definitive evidence of herding.
8. We Prefer Stories to Analysis.  As noted above, narratives are crucial to how we make sense of reality.  They help us to explain, understand and interpret the world around us.  They also give us a frame of reference we can use to remember the concepts we take them to represent.  Perhaps most significantly, we inherently prefer narrative to data — often to the detriment of our understanding.  Keeping one’s analysis and interpretation of the data reasonably objective – since analysis and interpretation are required for data to be actionable – is really, really hard even in the best of circumstances. A corollary to this problem and to confirmation bias is what Nassim Taleb calls the “narrative fallacy” — looking backward and creating a pattern to fit events and constructing a story that explains what happened along with what caused it to happen.
9. Recency Bias. We are all prone to recency bias, meaning that we tend to extrapolate recent events into the future indefinitely. As reported by Bespoke, Bloomberg surveys market strategists on a weekly basis and asks for their recommended portfolio weightings of stocks, bonds and cash.  The peak recommended stock weighting came just after the peak of the internet bubble in early 2001 while the lowest recommended weighting came just after the lows of the financial crisis. That’s recency bias.
10. The Bias Blind-Spot. I have written many times about the cognitive biases which plague us and make it difficult for us to make good choices, including (obviously) here.  Knowing about them is imperative if we are going to deal with them.  We would always be wise to factor in these biases when performing analysis and making decisions. Unfortunately, we all tend to share a “bias blind spot” — the inability to recognize that we suffer from the same cognitive distortions that plague other people. Here is a wonderful (both hysterically funny and achingly sad) example.

CFA Conference: Kaletsky, Ritholtz, Rosenberg, Mauldin

If you don’t read The Big Picture, by Barry Ritholtz, you should. Breakfast with Dave is a terrific daily email economic report by David Rosenberg. The day it became a pay-for service was truly sad. I worked with him at Merrill — he’s an excellent economist.  Anatole Kaletsky was  a columnist and Principal Economic Commentator for The Times (London). He now runs GaveKal full-time and is also the author of Capitalism 4.0: The Birth of a New Economy in the Aftermath of Crisis.  And if you don’t read John Mauldin‘s weekly email, Outside the Box, you should. His books are worthwhile too. That’s the opening panel at the CFA Institute Conference today.  It’s a very good one.

My session notes follow.  As always, these are at-the-time notes.  I make no guaranty as to their accuracy or completeness.

Ritholtz

• “Lots I can’t control.”
• Focuses on what he can control.
• Secular bear market since 2000, should end sometime between now and 2018 or so; trying to be ready when the next secular bull starts.
• Analysts generally wildly wrong — we’re bad forecasters.
• We miss a lot today by focusing so much on the future.
• Why is anyone surprised that the French elected a socialist? — European banks have not had to suffer austerity; their debts have been covered.
• Tech and other start-up stuff coming are astonishingly positive and bullish (once we get past the current valley).
• Fed is already involved in bailing out Eurozone banks; expect it to continue (even if the appetite isn’t strong).

Rosenberg

• “In these markets, I pray a lot.”
• “Austerity” is a dirty word, but it hasn’t really been tried.
• Principal concern in Europe — political cohesion is unravelling (that was a key hopeful point).
• Expects more velocity and volatility.
• Eurozone, China and USA all in trouble.
• “Fiscal cliff” is real and dangerous (the results of kicking the can down the road).
• If if the can keeps getting kicked, GDP growth still poor.
• Global deleveraging cycle — central banks will keep rates down and the yield curve will stay steep.
• Fed tightening cycle probably 5-10 years out.
• Once the credit card is maxed out, how can one get on a “growth path”?
• The only monetary union that has succeeded historically is the U.S.
• Gold now trades less like a commodity and more like a currency; thinks it will peak at around $3,000.
• Fiscal crisis will hurt emerging markets (Mauldin thinks it will help — more self-sufficiency).

Kaletsky

• “The world out there looks very grey to most, but it looks pretty bright to me.”
• Lots of focus on “somewhere else.”
• Volatility between pretty defined parameters.
• Eurozone has problems, but the U.S. is the driver, and the U.S. outlook is pretty positive (pulling out of the recession) even though expectations are highly negative.
• The U.S. bond market is the most dangerous asset class in the world.
• Expects the (steep) yield curve to steepen further (long-bonds could lose 20% cash without the Fed doing anything).
• The main (and most relevant) uncertainty is the situation in the U.S.
• Not that worried about Europe — economically untenable situation is becoming politically unsustainable, so it will have to change and either the Euro will break up or something else (perhaps a bank bail-out scheme on a Euro-wide basis), but that is the road to recovery, previously thought not-do-able.
• Germany will not allow itself again to become the pariah of Europe.
• Euro countries and entities may not be able to borrow from the capital markets, but can go to their central bank.
• The dollar is the least ugly in an ugly contest (all panelists agree).
• Thinks we’re in a secular bull market (since 1989 — Berlin Wall, internet, Tiananmen Square, Euro invented).

Mauldin

• “The only good thing that will come out of the debt crisis is that the French will get theirs.”
• Today it’s all Greece all the time; it will adjust to all France all the time.
• Countries will leave the Euro because they are tired of (untried) austerity.
• “Europe is a tinderbox looking for a match.”
• French banks are crucial but capital is imploding.
• “Japan is a bug in search of a windshield.”
• Terrified by the JOBS Act, even before FINRA and the SEC writes the rules.
• Japan is imploding and dying.

Fear, Greed and Ego

Barry Ritholtz has an excellent post up this morning (as usual).  This one makes an excellent distinction between trading and investing:  “Trading (as opposed to investing) is more about laying out probabilities of risk versus reward; Investing is about valuations within the longer secular macro picture.” About this I can only agree wholeheartedly and move on.

He then goes on to examine the roles of those well-known (if not so well understood and even less so well controlled) emotions of fear and greed:

By the way, that is a little known element about Greed & Fear: Greed is actually a variant of Fear — the fear of missing the move higher, fear of leaving profits on the table, fear of losing clients, fear of lower income, fear of losing your job. Hence, when most people say that Fear & Greed drives the market, they are really saying FEAR drives the market in both directions. True Greed doesn’t come into the picture, IMO, until we get to the stupid phase — think DotComs circa 1999 or Housing circa 2005.

I have a five-and-a-half month old grandson.  When Aiden thinks something fun is going on or even might go on, he will resist taking a necessary nap with every fiber of his being.  In our family, we say that he is suffering from FOMO – fear of missing out.  FOMO is exactly what Barry is getting at here.  But I also think that Barry is leaving out something important.

Fear and greed are frequently trumpeted as the twin drivers of markets.  I have long contended that there is a third driver and one that is underappreciated:  ego.  Ego often pushes us to do things we might not do otherwise (and vice versa).  I’ll show them.  I just know I’m right.  Mine is bigger than yours.

Profitable trading takes great conviction, perhaps even great ego, but one can “fall in love with” his position or ”marry” her bias to great detriment.

Humility isn’t easy for anybody.  It’s especially difficult for those who are successful, driven and competitive and this business attracts those types of people and rewards those types of people.  But humility can protect us from ruin.  As Stanley Druckenmiller said about George Soros (in The New Market Wizards : Conversations with America’s Top Traders), ”I’ve learned many things from him, but perhaps the most significant is that it’s not whether you’re right or wrong, but how much money you make when you’re right and how much you lose when you’re wrong.”

There are no absolute sure-things in the investment world.  Trading successfully means properly analyzing the probabilities and acting accordingly.  Every good trader is going to be wrong a significant portion of the time.  There are simply too many variables in the system for it to be otherwise, especially because so many of those variables are unknown and even unknowable (think Frank Knight’s classic distinction between risk and uncertainty).

As Michael Mauboussin puts it in his book, More Than You Know:

So how should we think about risk and uncertainty? A logical starting place is Frank Knight’s distinction: Risk has an unknown outcome, but we know what the underlying outcome distribution looks like. Uncertainty also implies an unknown outcome, but we don’t know what the underlying distribution looks like. So games of chance like roulette or blackjack are risky, while the outcome of a war is uncertain. Knight said that objective probability is the basis for risk, while subjective probability underlies uncertainty.

A clear and objective process (to the extent one is possible) can help to guard us from letting our egos run away with our good sense.  The markets are humbling.  Sometimes it behooves us to “take our medicine” and move on.

Data and Analysis

The “email of the day” over at The Big Picture is a very funny one indeed.

“Have you ever considered switching from thoughtful to dogmatic?  It’s a lot easier and you make more friends.  Plus, sometimes they send you free bumper stickers in the mail.”

As one who tries consistently to make his decision-making process data-driven, I am more than a little sympathetic to the emailer’s point.  That it is pithy and well-stated is so much the better.

However, as I pointed out in the comments, it is often pretty hard to separate the proverbial sheep from the metaphorical goats: 

“On account of a significant number of cognitive biases, most crucially confirmation bias, distinguishing between the data and the ideology (or, perhaps more accurately, keeping one’s analysis and interpretation of the data reasonably objective — since analysis and interpretation are required for data to be actionable) is really, really hard even in the best of circumstances. Indeed, the data suggests that we all (and not just the people we disagree with) tend to start with our ideologies and then search out facts to support them.”

The difference between ideology and analysis sometimes depends solely upon whose ox is being gored.  Not surprisingly, we all tend to think that our decisions are data-driven while those we disagree with are blinded by ideology.