Barry Ritholtz

This is Part One of a two part interview with highly acclaimed Macro-Strategist Barry Ritholtz.

In the movie The Matrix, the character Morpheus serves as a guide to reveal objective Truth. Although we are challenged to discover anything objective in the highly subjective, pseudo-scientific field of economics, independent macro-strategist Barry Ritholtz is leading a clique of newly emerging economic realists. I liken this moment to that in which independent record labels such as Sub Pop Records (Nirvana) flanked the major labels to gain mainstream attention. Barry is our Sub Pop.

Although Barry has become increasingly popular over the years, he exploded onto the scene while blogging critical under-reported data during the housing and credit bubbles. While the mainstream media cheered irresponsible borrowing and spending, Barry’s uber-popular The Big Picture blog chronicled the bare facts and logically induced prescient warnings of the inevitable collapse. As a result, Barry had the rough draft of an indispensable book about the financial crisis and subsequent bailouts.

Despite a political controversy with original publisher McGraw-Hill, Bailout Nation was recently released. I sat down with Barry to discuss his career, his cross-disciplinary framework, his ballsy indictment of the crooks who perpetrated “the greatest heist ever made,” the legal training that helped his efforts, some legendary stories, and his outlook for the economy.

Damien Hoffman: Barry, we are both lawyers by training. What caused you to also forgo a legal career in favor of finance?

Barry: Mostly other lawyers [both laugh]. I actually enjoyed law school and just found myself not loving being a lawyer. One day about 20 years ago I ended up doing a deal closing at a friend’s trading shop. I got a tour of the trading room and basically I said, “This stuff looks like Nintendo for money.” And that was pretty much it. I was hooked. It didn’t take much.

Damien: Was that before or after the stock market crash in 1987?

Barry: It was after the crash in ’87. Remember, that event was only a correction [laughing] … or so we were told. And in hindsight, turns out that conclusion was pretty close to accurate. The market finished the year up — which people seem to forget. After a 16-year bear market, you end up with a situation where the first five years of a rally from those lows got a little ahead of themselves. Not surprisingly, we found ourselves with a debacle: creaking internal plumbing in the New York Stock Exchange combined with portfolio insurance (portfolio insurance was, at the time, newfangled derivatives that promised to guarantee against losses). Lo & behold, the crash was one of the exceptions: a true correction that did not spill over into the broader economy.

Damien: Did you enter the trading business then, coming off the ’87 lows?

Barry: No. I wish. I got into the trading business a couple years later. I graduated law school in ’89 and practiced for a couple of years. Then I made the switch to finance in the early to mid ’90s. I started out in the business as a trader.

Damien: Did you have a specific methodology at that point with the legal background?

Barry: I knew trading was very challenging, but couldn’t figure why some people did well and others did poorly. Right from the beginning, I started trying to figure out what made better traders and what made worse traders. I ended up looking at a lot of different strategies and styles, but always with a very skeptical, mathematical approach that needed to be proven to me — just show it to me mathematically. My undergraduate work was both physics and applied mathematics. Then, in my fifth year of college I switched to Poly Sci just to graduate and get the hell out.

Now add a lawyer’s skepticism — which as you know you can’t just say something is, you have to demonstrate and prove it. So when you approach things with both of those philosophies, you end up with a very unusual approach. You don’t take things for granted. That leads you to being a little skeptical when someone says, “Hey, buy these stocks cause the P/E is low.” It makes me say, “Well, why do we want to own low P/E stocks? Are these always the better stocks?” We found out the answer was “no” — as with the homebuilders in 2005, the commercial banks in 2006, and the investment banks in 2007. The original claim was a canard. There are lots of those on Wall Street. So, going from a trader to a researcher to a strategist required me to identify these myths of Wall Street and figure out what was right and wrong. It was a major process.

Damien: So do you like to start with your lawyer’s mind and look into things from a fundamental perspective, break them down and come up with your trading ideas first, then move over to the more mathematical, technical side to decide entries and exit points? Or do you like to look at charts first and then be the lawyer and do the investigative due diligence on the company?

Barry: We start from the bottom up. We look at what sectors are doing well and what stocks in those sectors are doing well. We look at what is quantitatively highly ranked in our system and what is poorly ranked in our system. And then I layer the legal approach — the critical reasoning and analytical thinking — on top of the math. That’s how I understand why is this sector doing well or poorly.

A perfect example is the homebuilder stocks in 2005. Every time someone would say homebuilders are cheap based on Price Earnings (P/E), our firm would come back and say rates were low and going higher,  and profits were at all-time highs. Therefore it’s hard to imagine that cyclically the stocks were going to stay elevated forever. Also home sales were at record highs relative to population. Why would we expect this cycle to not turn? So, everything we looked at in the homebuilders from a top-down perspective had us concluding, “Gee, this is really unlikely to continue.”

But the math got us looking at the homebuilders. Same thing elsewhere. We had pretty aggressive sells and short sells on Bear Stearns, Lehman, AIG, and Fannie Mae. It wasn’t that we were rocket scientists on this. These things had popped up in our system as very poorly ranked and having been higher ranked fairly recently. So then we tried to figure out what was the issue. Eventually that led us to AIG carrying a lot of derivatives that were potentially problematic. In August 2008, I was on a Bloomberg television show when AIG earnings came out. That was the day their earnings were delayed for about 15 minutes. Never a good sign.

Damien: Unless you’re short.

Barry: [Laughing] I have the producer of the show in my ear saying, “You gotta stretch it out. The earnings still haven’t come out.” I didn’t even want to do the earnings because it’s a 50/50 thing whether they beat or miss. Our firm was short AIG. We were pretty confident they were going to stink up the joint. But we didn’t expect a complete and total meltdown. So for 15 minutes the anchor is like, “Why are you guys short AIG?” I said, “Well, we think they are not managing their risk well in the financial products division. Their hedge within AIG is a potential disaster. Obviously, the market has been sniffing something because AIG was a $70 stock and it’s now $35. We think it goes lower from here.” The experience was fascinating. When the numbers finally came out, the anchor misread them. She said, “Oh this is a big upside surprise! AIG lost 67 cents and analysts were expecting 55 cents.” I said, “No, no. They lost more money! This isn’t a good thing. This is a bad thing! They were supposed to make 55 cents but they lost 67 cents!” She said, “Oh. Oh, there’s a dollar swing. That’s huge.” Or whatever the numbers were. I can’t recall exactly. Anyway, that is a classic example of the math identifying a problematic sector or a problematic stock, then having to do the homework and figure out what the math means. Sometimes we’re wrong on the explanation. But as long as the math is correct, the trade is very forgiving.

Damien: When you say “the math,” do you mean the technicals and price of the stock action?

Barry: Our firm, FusionIQ, literally runs a quantitative research tool that takes 8,000 stocks and ranks them from 0 to 100 based on 15 metrics we’ve back-tested. The metrics are a combination of things like short-term and medium-term trends, institutional ownership, money flow, short interest relative to the group, etc. We put a numerical ranking on all these stocks. That is how we end up with a 0 to 100. That is what I mean by “the math.” Then on top of that we overlay an algorithm that generates a buy or sell signal depending on a number of factors. We use that system as a basis of our asset management. Last year, 2008, with the market down 42%, our long-only accounts lost about 8%. In a normal year that’s disastrous. But when the market gets practically cut in half, being down single digits is not too shabby.

Damien: It’s incredible.

Barry: And that’s just running with a lot of risk management and awareness that, “Gee, the market was rolling over. This is not the ideal environment in which to be long.” So you raise cash and find the sectors that are not only going to outperform relatively, but also do better. Sometimes we get it more right than others. But in 2008 we did a nice job in terms of preserving our client’s capital.

Truth-seeker Morpheus from The Matrix

Damien: That is really interesting, Barry. While reading your blog for the past four years I get the feeling there are two Barrys. First there is trader Barry who is very aligned with the FusionIQ system. Then there is econ Barry who is the pragmatic, skeptical economist posting why the government numbers are wrong and the National Association for Real Estate Agents is spinning their data. I always thought the blend of the two made you an economic realist because you kept a focus on the reality of the markets and the raw data while not spinning. Is your investing and trading framework a blend of the two Barrys?

Barry: They’re two sides of the same coin. In fact, this is related to the way we came up with the name Fusion. My partners and I have been on Wall Street for a good chunk of years. One of them worked at Morgan Stanley for quite a while. The other has always been on the research side. We couldn’t help but notice that research from traditional Wall Street firms was sometimes really good, and sometimes really awful. We couldn’t figure out which analysts we should listen to. At the same time, we noticed some of the technicians were really good. However, there was a weird phenomenon almost like declaring a major or finding a religion. Some people were technicians and others were fundamentalists, and never the twain shall meet.

Well, that doesn’t make a whole lot of sense. We’re agnostic. If something works, we’ll use it. If you can come up with a good fundamental basis for why a certain stock is going to do well, why ignore that? But on the other hand, we know stocks that go up tend to keep going up, and stocks that go down tend to keep going down. So, why ignore trends? Why not make that a significant part of a model? We know sometimes we could use a combination of charts and sentiment data.

My favorite example comes from my partner Kevin Lane who is a very well-regarded technician. When Enron was in the high $80s he very famously said just about every Wall Street analyst had a “Strong Buy” or a “Buy” [rating] on the stock. All the institutions had a 93-94% ownership. In terms of institutional ownership, that’s as high as it gets. So, who’s left to come in and buy? Why shouldn’t someone be able to use those elements in an analysis? So, the idea behind Fusion was we were going to use the technicals and the fundamentals, then include them in a purely objective mathematical ranking.

Damien: What about Barry the economist? The Barry who relentlessly tracks economic data releases?

Barry: When I look at economic data I think I approach it more as a statistician than as an economist. I ask, “What do these numbers really mean? How can I contextualize them? How can I figure out their significance?” My favorite recent example is new home sales. Today is the first month when new home sales have come out with a margin of error less than the actual number. Meaning, until now, every month we see an uptick in new home starts or permits around 3-4% with a margin of error of 14%. Therefore, we have no idea what the hell’s going on! Are we up or down? There is no context if the margin of error is greater than the actual data point. Well, it is not statistically significant. So, the current plus 17% — up from record lows — just means things have gone down so far they were just due for a bounce. Plus 17% was actually better than the 14% margin of error. So, for the first time in who knows how long, we actually had a monthly data point which was higher than the statistical error. Oh, by the way, year-over-year we’re down 47% which means housing is still awful!

Damien: But we’re beating the margin of error …

Barry: [Laughing] But, we have to be willing to look at month-to-month data as well as the margin of error and annual numbers that do a nice job staying away from the seasonality adjustments. The math really affects the ability to say, “Is this number real? What does it mean? How significant is it?” Also, I’m a big fan of behavioral economics. I get skeptical when people start getting crazy excited over one data point. Hey, a single data point does not make a trend. But latching onto a single data point is a function of things like the recency effect. The recency effect occurs when we latch onto the most recent data and put too much stress on it. For example, if we’ve seen a bunch of positive numbers that were followed by 5 negative numbers, there is a tendency to latch onto the recent negative data. There is a whole bunch of psychological foibles like that.

So, to some degree, if you want to be a good investor you not only have to be an accountant, but you also have to be a mathematician, a psychologist, and a historian. You must have the ability to think from a lot of different fields to look at what each data point really means. You must ask, “What do these numbers really mean that come out each month? Are they significant? Are they not?” And you could do the same analysis with individual stocks. It doesn’t have to be only economic releases.

Damien: Do you admire certain economists or other people who use your cross-disciplinary approach?

Barry: Absolutely. Noticing the good guys is something that has gotten lost in the whole debacle and finger-pointing at Wall Street. First, within Wall Street there was only a small handful or percentage of people that screwed the pooch. Look at AIG. It has about 40,000 employees, but only 300 people in the division that brought the company down. When you look at Merrill Lynch, at one point in time they had 16,000 brokers and 50,000 employees. Again only a handful of people, about 500 people in the mortgage and derivatives department, brought Merrill down. You could go company by company and find the same pattern. A teeny-tiny percentage capsize the entire ship.

But, one of the things I’ve loved about Wall Street is people are incredibly generous with their time and expertise. Whether it’s guys like Doug Kass, Jim Bianco, or David Rosenberg, there are a lot of people who are especially realists. I mean I could give you a run of guys who I have been reading for years. I just think these guys get ‘It’. They don’t get sucked into the spin. They’re not cheerleaders. They call it as they see it. A lot of these guys also publish at my blog [The Big Picture].

When it comes to banking analysts, you are not going to do any better than Chris Whalen and Josh Rosner. These guys have been dead right on everything with banking. I also look at guys like Rosenberg or technical guys like John Roque. His big picture views have been really, really good. There is a great book out from years ago called ‘When to Sell’ by Justin Mamis. I also recommend his subscription list if you are lucky enough to get on. It’s not cheap, but he basically has a fabulous feel for real short-term market moves. When you contact these people and ask them questions they are just unbelievably generous with their time and insights. That is one of the really great things about Wall Street. There is this a mentoring relationship you can develop with people — and they’re top notch. My biggest concern about the collapse of the big Wall Street firms is that the mentoring might go away. Although, most of the guys I named were all at big firms earlier in their career and are now at independents or their own firms. So, hopefully that [mentoring] tradition will continue.

Damien: Given that we’re at the beginning of the Information Age and great people who are doing great work can find better ways to distribute more honest information, how much longer do you think the government and other institutions can manipulate economic data? Do you think the economic realists will shift the balance of power over time, or do you think we are destined to live in a propagandous environment?

Barry: I’m not sure the problem is as much propaganda as a noisy data flow. For example, there was clearly an issue with the Establishment Survey at the start of the recovery. There are two surveys for unemployment: one is the Establishment Survey and the other is a Household Survey. So, one of them measures tax receipts to determine the number of people working, and the other is a survey where they ask households, “Are you working? have you been working? etc.” At the start of recessions the Establishment Survey lags the system and the household survey picks up reality first. So, they attempt to correct this issue with an artificial birth-death adjustment. This was a credible attempt to try to make up for the lag error. The problem is they simply do a poor job modeling it.

For example, during the past two years we’ve seen the birth-death adjustment show a lot of new jobs in finance and construction. We know this is absolutely absurd. In fact, in 2007 we changed from a purely measured or 92% measured data point in the non-farm payroll to 75% of new jobs being attributed to the birth-death adjustment. The birth-death adjustment is a derivative of how many new companies are incorporating in various states, then determining how many people are working relative to that. Therefore, these are people who either started new companies or got laid off and went the independent route. There is a huge difference between working for a company and getting a steady paycheck, then being an independent contractor who may or may not get paid. It’s going to impact the way you shop for a car, house, or whatever. That’s why you want to know the non-farm payroll changes and not just to slap a number on it.

But what does this mean for future economic activity? I find the data is less overtly manipulated and more screwed by politics and shitty models. The economists create models and the bosses — entities like the Bureau of Labor Statistics and Bureau of Economic Analysis — are politicians. They don’t want to show very bad data. So, there is a lot of pressure to make their models a little prettier.

All models are wrong, but not completely worthless. Anytime you are going to attempt to depict reality with a mathematical construct, the model will never be precise. The questions are, “Is the model accurate and precise? How consistently does it miss the mark and how close does it come to the mark? Is it random?” So, there is a lot of math explaining why these models are only mediocre.

Unfortunately, they are the best we have and you want to make them better. But, I look at them very much askance because the way the politicians and others take the output of those models. They take the output and spin it as best they can because they really think, “If I could just build up the confidence of the public, they will go out shopping [laughing].” They pretend you could take a house with rotting timbers and main beams, slap on a coat of white paint, some shutters and flowers, and everything will be lovely. That doesn’t work. You have to fix what is wrong fundamentally before you start working on the confidence. The confidence takes care of itself. Things get better and confidence just works.

Damien: If the government hired you and sent you in with a team to work on some of these models, do you think the political pressure would still be there, thus continuing to cause a lot of these problems?

Barry: Well, the pressure would not be on me, but whoever comes after me [laughing]. If they hire me they know what they are getting. They are getting someone who is going to say, “Let’s go back to the Boskin Commission.” The Boskin Commission determined inflation was overstated by one-point-something percent and they came up with some hair brain ways of lowering it. For example, when steak goes up in price and a consumer purchases chicken instead of steak, the economists call that ‘substitution’. Therefore, the Boskin Commission says this substitution doesn’t reflect a price increase in the basket of goods that that consumer purchased. Thus, they say inflation is moderate.

Now, anybody else would look at that and say, “No, you stupid sons of bitches. I was just priced out of steak and now I’m buying the cheaper meat because I can’t afford the more expensive meat.” Anybody with two eyes and a brain should be able to figure that out. Boskin is obviously sheer absolute and unmitigated nonsense. If steak goes up in price, that’s inflation by definition. The fact that I can no longer afford steak doesn’t mean I’m buying something else or there’s no inflation. It means I’ve been priced out of that product!

I hold Boskin indirectly responsible for the whole credit collapse and the entire stock market crash because his lame hair brained rationales for understating inflation

Required Reading for Every US Citizen

gave Greenspan the ability to say, “Well, rates are dangerously low back in ’01, ’02, ’03, but look inflation is contained so it’s really not too bad.” I make that connection in the book [Bailout Nation] in a chapter called ‘Strange Connections, Unintended Consequences.’ The chapter explains how all these weird things took place and they all ended up having extremely bizarre consequences. Boskin is a perfect example.

Hedonic adjustments are another example. I think there is a terrible mistake economists make: they don’t understand technology. They don’t understand the life cycle of a new technology that comes out and there are economies of scale unfolding. For example, when the first plasma screen came out it was $100,000 and eight of them were sold. They cost that much because only eight of them were sold. Then, you build a factory and amortize the cost of the factory, and a few years later you are selling 10,000 of these things a year. Now they are going for $10,000 and $12,000. Then, a few years later you’ve gone from a small factory to a big factory and are selling a million of these a year. At this point plasmas are down to $6,000 a piece.

So, you had the early adopters who were unsure about the technology but bought it. Then you had the later adopters. Fast forward a few years later until finally the item becomes a mainstream product and you have huge factories all over the place. You are cranking out 100 million of these a year and these 50″ plasmas are going for under $1,000. The economists would have you believe this is proof inflation is contained. However, in reality the product cycle is a normal, natural cycle for all technologies and has nothing to do with inflation. This cycle happened with the cell phone, the iPod, PCs, laptops, etc. The cycle doesn’t mean that there is less inflation. The cycle means there is a normal life cycle of a product.

Meanwhile, you’re buying all these goods that didn’t exists 20 years ago. You didn’t have an iPod, an iPhone or a plasma screen. So, you are actually spending more of your discretionary income on all these toys. All the economists listening to this are going to say, “No. He’s wrong. Those are prices coming down. It’s deflationary.” I disagree. This stuff happens with every technology. It doesn’t matter what is going on with the money supply, the deficit, issuing dollars, etc. Every product goes through that life cycle. It’s normal and natural. It sure as hell isn’t deflationary. When something new comes out, you can expect that eventually — as it goes from a limited custom product to a more luxury product, to a more mainstream product, to a ubiquitous product — the economies of scale bring the price down. I don’t believe we can accurately describe that  process as deflationary or disinflationary.

Damien: That opens up a nice segue to talk about your book Bailout Nation which I reviewed last month. However, I have one last question in the area of economics. What advice do you have for young students of economics who are surrounded by classical models with false underlying presuppositions like the one you just described about inflation?

Several days from now, in Part 2 of the interview with Barry Ritholtz, Barry’s wit and humor tackle the following:

• His advice for young econ students;
• His legendary stories from the housing and credit bubbles;
• His book Bailout Nation and the reaction to the release;
• His vision of Wall Street’s future;
• His personal line of demarcation between tragic truth and fringe conspiracy theory;
• His thoughts on how the U.S. can reclaim economic greatness;
• His recommendations for investors; and,
• His top stock picks from Barry’s outperforming firm FusionIQ.

Read Part 2 of our interview with Barry now:

Barry Ritholtz: Part 2

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