May 18
lindsayrgwattFinance housing
So in a few months I’ll be moving to Canada. And renting. For a long time. Possibly until I’m 40. And I’m not ashamed of it.
Given that so many people consider owning a house a measure of their identity and worth, why am I not interested in buying one?
Basically I think Canada’s real estate market is in a bubble and I don’t know how long I’ll be in Canada (at least five years, but past that, who knows) nor do I know when the market will burst. So, I won’t be buying.
Now, here’s a whimsical reason why I think it’s a bubble. And here’s the graph (plus explanation) that underlines the whimsy:
From here via here.
Two caveats: if the bubble bursts or I strike it rich I might buy a house. Otherwise, you’ll find me in my condo…
May 12
lindsayrgwattFinance, NYC, Random nassim nicholas taleb, philosophy, statistics
Last night Wen, Rich and I went and listened to Nassim Nicholas Taleb be interviewed at the powerHouse Arena. I’ve been a big fan of his books for years, but this was the first time I’d seen him speak. The interview started on a couple of false notes (he spent a few minutes telling us that they’d all just been out for drinks; the interviewer apologized for her French-accented English), but he had a couple of quotable points:
- The difference between a fool and a saint is timing
- If a problem is too hard to compute, the outcome is essentially random
- Black swans are not black swans for everyone: only for ‘suckers’. To be crass, the 9/11 terrorist attacks were a black swan for Americans; for the terrorists were exactly what they were expecting
- Debt levels map one-to-one with forecasting overconfidence
- If I told you that you have a 3.4% chance of losing everything on a trade, you probably wouldn’t take it. If I told you that a catastrophic failure only occurs every 30 years, you would
- Religion is not about beliefs, it’s about creating heuristics for people who otherwise couldn’t think them up themselves
- The best science is done by independents (Einstein, Darwin), not by people associated with institutions – those people try to please the tenure committee. There probably isn’t a perfect institution for creating better science, but abolishing tenure is likely a good start. (This feels very akin to how innovation in business occurs)
- ‘Forecast’ is ‘prophesize’ in Arabic – but how would you feel about next year’s business ‘prophecy’?
Basically, everything he said could boil down to the following:
- Almost everything that’s interesting in the world is nonlinear
- And no one really understands how nonlinear dynamics work
- So if anyone tells you they do, don’t believe them
- Instead, always compute the likelihood that something will happen…
- …and make sure that you’re never the ‘sucker’ based on those probabilities
He closed with an interesting comment that he wants to move from a world of true/false to sucker/non-sucker. An interesting thought; if you get a chance to see him speak, do so.
May 07
lindsayrgwattFinance Complexity, stock market
So, unless you live under a rock, you heard that yesterday, at 2:45 pm, the stock market dropped like a rock:
We don’t know why this yet happened, but there are two likely culprits – and neither should make you happy.
The first hypothesis is that this mini-crash was caused by ‘fat fingers’ – an errant trader entered $16 billion instead of million (this trader needs a dialing wand). Pause for a moment and think about that. Is this even possible? A trader can place a trade for $16 billion – larger than the GDP of 81 countries – without someone checking it? If this is even possible, then risk management is a joke and it’s time to shut down some of these trading companies.
The other hypothesis is based on programmed trading. Massive automated trading means that Wall Street 2010 is part Skynet, where the machines are in control. It’s possible that yesterday’s downward spike was caused by a computer-controlled model gone wrong. It interpreted some combination of signals as meaning that the market was turning and it started to sell. Hard.
This sell order was then picked up by other machines who then started to sell too. And the feedback loop began, rocketing prices downwards.
At this point the sell orders come in faster than a human can make sense of them and you get panic on the exchange. Moreover, no market maker is able to stabilize the market because the trades are coming in faster than they can interpret them.
The irony is that another batch of computers are looking at the market and see this as a buying opportunity. Proctor and Gamble is down by 30%. A computer buys it because it knows that, due to mean reversion, it’s unlikely that P&G will not gain back part of that loss very soon. And now the feedback loop kicks back in the opposite direction.
And the market stabilizes again, albeit down a few percentage points (which is a big deal).
If this turns out to be the reason for the spike, it’s very disheartening. These programmed trading models are proprietary and we have no idea what they interpret as sell signals. It could be some combination of statistics and trading data; it could be as esoteric as reading blogs for sentiment. Moreover, even if 99% of models are ‘correct’, the 1% that wrong can potentially set off a cascade.
Moreover, the fact that the NASDAQ has cancelled all the wild trades yesterday reduces some of the incentive to fix the system. Don’t get me wrong-whoever’s system set this off is working the weekend to figure out what happened. But, a lot of people lost a lot of money yesterday (and some made a fortune too) and having those trades cancelled sends a signal that market owners are going to smooth these things over. If you get carried away, they’ll just hit a big “undo” button (I wish I got this at my job).
The real reason this scares the living hell out of me, is that unpredictable volatility like this is typically a sign of a major instability in a complex system. Our financial system is a terribly complex system. When you see a major change like this, it’s a sign that your models of the system are wrong and you’re now flying the plane without a captain. I’m going on record as saying that no one on Wall Street really understands how the entire system works; the proof is in a thousand point decline -and 700 point rebound – in the span of 15 minutes. Statistically, this should never happen.
So what’s going to happen? I’m putting my money on a huge increase in volatility over the coming months and, unless there’s a change in the regulatory system (more disclosure of modeling, caps on trading limits or sudden price changes), increasingly shorter cycles of stability/volatility. It’s going to be an interesting ride; hold on.
Nov 08
lindsayrgwattBusiness, Finance
I either didn’t exist, or was way to young to remember it, but the ’7os were tough economic times. The reason: inflation. Prices were quickly going up, and when this happens faster than the economy grows, your standard of living drops. Here are some quick charts from the Berkeley Econ dep’t:
It took Paul Volcker raising interest rates to absurdly high levels in the early 1980′s – and thereby bringing the economy to a standstill – to tame inflation. This had a profound effect on society: the Fed send a clear signal that they would not tolerate a rapid increase in the prices of goods and services. If such a rise occurs, then they raise interest rates, even if it risks putting the economy into recession.
This has held true to today and as a result, we haven’t had any serious (excluding gasoline) increases in the prices of goods and services since 1981 or so. As a result, this type of inflation hasn’t caused this recession or any since the early 1980′s-and that’s a great thing.
However, notice that I’m being very specific in my choice of words: I’m defining inflation as the increase in the price of goods and services. Recently we’ve had all sorts of recessions caused by other types of inflation: the Tech Bubble of ’99/00, the Oil Shock of ’07/08 and the Housing Bubble. In fact, some folks believe that we’re seeing a bunch of other bubbles right now: Chinese property market, U.S. equities, and on and on and on.
So here’s the hypothesis: since the Fed is so tightly monitoring the prices of goods and services – because that’s how they define inflation – they’ve pushed ‘inflation’ into other markets. If you were to look at other prices and define ‘inflation’ as occurring when they rise too rapidly, you’d think to yourself “holy crap, we’ve got an inflation problem.”
There are some hints that people are starting to think this way. A recent New Yorker article on Larry Summers contained the following:
In 2007, Summers started looking at the looming economic crisis. Back in 2003, he had attended a Federal Reserve conference in Jackson Hole, Wyoming, in which economists were celebrating the fact that central bankers seemed to have mastered the use of monetary policy to tame inflation without causing the economy to slip into a recession, as had happened in the past. Summers warned that perhaps the victory over inflation meant only that the next recession would be caused by some new phenomenon.
And in a recent investment email, David Einhorn talked about asset price inflation:
Further, the Federal Open Market Committee members may not recognize inflation when they see it, as looking at inflation solely through the prices of goods and services, while ignoring asset inflation, can lead to a repeat of the last policy error of holding rates too low for too long.
This is a really tricky problem to solve. Taming inflation is straightforward: you just tell the public that you want inflation to be between 0.5-2% and then raise interest rates any time it looks like it might be higher. The public pretty quickly learns that you mean business and don’t misbehave.
But trying to prevent bubbles is a crazy hard problem. You can’t say something like “asset prices can’t increase more than 10% a year” because nobody is going to agree to that. There are legitimate times when a category of asset prices could increase way faster than that and it would require unprecedented (and unacceptable) government intervention to avoid it.
Instead, the challenge to the Fed has to abstract the problem and understand how to create a set of incentives to get people to behave properly. This is a really hard problem as first, everyone has to agree on what causes the problem (almost impossible as people who are making quick money have an incentive to disagree) and then Congress, etc. have to be convinced to actually implement regulation.
It’s going to be fascinating to see if the Fed and the Obama administration rise to the occasion and try to solve this problem. As the Chinese apocryphally said: “may you live in interesting times.”
Aug 18
lindsayrgwattFinance, Science behavioural economics, Psychology
I love reading about behavioural economics and how human nature limits our ability to make rational decisions. I love it even more when I find myself behaving irrationally despite being aware of it (this is all very meta).
Here are two recent examples:
1) On Sunday I went for a run. In the crazy heat. With my iPhone in my pocket. After two and a half hours in my sweaty pocket, it wouldn’t work.
I had turned my $0 run into a $450 run and mentally readied myself to go buy a new phone on Monday.
But then something great happened-the damn thing came back to life (I’m writing this blog post on it right now). I started to feel elated about the $450 I had ‘saved’ when, in reality, absolutely nothing about my financial situation had changed.
2) I sold a bunch of stocks the other day at a modest return. However, I kept tracking this portfolio just to see how it does. Unfortunately for me, it’s been doing great-or at least one stock has. This pains me-even though it’s just a shadow portfolio.
Moreover, when the market fell on Monday, I going myself checking to see how much I would have lost, even though I would still theoretically be up. Needless to say, this was getting so unhealthy that I just deleted the portfolio.
I’m quickly realizing that though it’s easy to read about this stuff it’s going to take a lifetime to master.
Aug 09
lindsayrgwattFinance chase, wamu
Well, it’s official. The WaMu in my building has now become a Chase:
I’ll be interested to see if Chase keeps any aspects of the old WaMu. While the bank was run by a bunch of terrible executives, they did provide one great thing: the experience of using the bank. You didn’t stand in a line, instead you went to a ‘greeter’ who took you to the right person to help you; you didn’t talk to someone via a reinforced bulletproof glass window, you did it side by side. (At one point, WaMu was recognized by Fortune magazine for this). I hope Chase realizes that the retail experience had nothing to do with the collapse of the bank and doesn’t do away with it completely…
May 10
lindsayrgwattFinance, Science Complexity, Economics, physics
This weekend I walked into a gallery on 21st street and saw this sculpture:
This photo captures the components of the system, but not the dynamics of it. Basically, you’re looking at two fans facing each other and connected by four pieces of fishing line. Around the fishing line are wrapped two circular streamers of magnetic tape.
This is fundamentally a simple system – the fans provide continuous input – but the outcome is unbelievably complex. The two streamers bounce back and forth between the two fans. At time they appear to stand still and then wildly gyrate in a new direction. At no time can you predict where they are going to go next, nor do they ever take the same path twice.
This art installation is a fantastic visual example of what is talked about in a recent paper, The (Unfortunate) Complexity of the Economy, by Jean-Phillipe Bouchaud. Bouchaud shreds the notion that our economy can be explained simply by supply and demand. Instead, he outlines how many of the behaviours we see in our economy (bubbles, markets that never settle on an equilibrium, etc.) can be explained by different physical analogues. For example, the fans above are an example of a system that is incredibly sensible to the slightest perturbation in its environment, meaning that is constantly and dramatically changing state (sound like the stock market of late ’08/early ’09?). What’s more, the system above is also governed by few simple actions (fans blow a tape wrapped around strings) yet incredibly complex action resuls (think about many people buying/selling a stock, yet prices gyrate madly).
If you read one academic paper this year, make it this one.
Mar 26
lindsayrgwattFinance markets, policy
When I was a kid my dad used to say that when politicians wanted to lie they spoke “with a marble in their mouth.” (I have yet to hear anyone else use this expression, but it’s stuck with me).
I couldn’t help but remember that today when John Paulson-the hedge fund manager who made billions last year-was in the New York Times saying that no hedge funds had failed in the current economic crisis ans hadn’t really causwd it and therefore shouldn’t be heavily regulated. After all, it’s the greedy bankers and insurers who caused this right?
Let’s check the facts. First, this crisis was accelerated by the failure of Lehman Brothers (that was the day the financial earth broadly stood still). Lehman brothers failed because their was a fall in confidence in it-which drove it’s stock price down and created a vicious cycle that ended in bankruptcy.
Now here’s the dirty little secret: when it failed, their were 38 million naked shorts on it that failed as trades. That means that essentially the entire market bet against it, trying to drive down it’s share price for profit-which of course reinforced it’s ultimate failure (Paul Kedrosky’s blog links to the facts).
So who made these trades? I’m pretty sure that it wasn’t an army of retail investors using eTrade. I’m betting that it was a bunch of hedge funds-which means that they’re now right at Ground Zero of the current debacle.
Of course, I can’t prove this-and that’s the magic. Hedge funds are surrounded by such a thin, slippery skein of regulation that there’s no way to tell. I love free markets but they only work when there’s an open flow of information-and that’s exactly what’s missing here.
Jan 26
lindsayrgwattFinance, Random Complexity, path dependence
Two recent articles I read have me thinking about path dependence. For those who don’t know, path dependence basically means that we (either individuals or institutions) are the sum of historical experiences – history matters. A corollary is that small events can build up over time to have large historical impacts (read the Wikipedia entry above for the story of VHS vs. Betamax).
The first article comes from the New Yorker and is about how we arrived at the current U.S. healthcare system (The author, Atul Gawande, is a pretty fascinating guy – read his recent NEJM paper on how simple checklists can significantly improve patient outcomes). The synopsis could be: nobody designed this system, rather many little decisions have now led us to what it is. This is classic path dependence (and his article calls it out). Anyone who wants to change the system is going to have to accommodate this and show that their solution is able to deal with all the challenges that got us here in the first place.
The same dialogue is going on right now in the world of finance. Check out Alan Blinder’s recent article in the New York Times. He outlines the six retrospectively obvious mistakes that we made to lead us into the current financial crisis we’re in. This again, is classic path dependence: a few independently made mistakes combines to create one massive mistake that was much great than the sum of its parts.
You might be thinking that path dependence is a bad thing, but that’s not true. In fact, it can lead to great outcomes. Before the Euro, one of the reasons that Germany consistently had a high standard of living was the Bundesbank’s focus on low inflation. They were adamant about keeping inflation low as the Bundesbank’s early governors had lived through the hyperinflation of the Weimar Republic and were obsessed with making sure that it never happened again.
Jan 11
lindsayrgwattFinance Palm, statistics, stocks
I know it’s almost impossible to have fun with stats (I’ve sat through many a boring stats course over the years), but a recent event reminded me of how misleading some statistics and analyses are – most notably anything involving time series and percentages. There’s a small library of books on how to lie with statistics, but the recent announcement of the Palm Pre provides a great example.
For background, Palm’s a company that was synonymous with mobile computing, until Microsoft got into the game with Windows Mobile and then two companies called RIM (Blackberry) and Apple came along. For the past few years, Palm has drifted and has been consistently losing market share. However, on January they announced the Pre and this propelled their stock price into the stratosphere:
Note that I’ve compared them with Sprint (whose share price went up as they’re the exclusive carrier for the Pre in the short term) so that you can see the percentage change (all the data comes from Google Finance). This graph is a dataset with a few hundred datapoints and a naive analysis would suggest that Palm was a go-nowhere stock until it announced the Pre.
However, the truth is a little more nuanced than that. In fact, Palm was up over 100% in the first part of December and has been on a tear since January 5:
As an aside, it’s interesting to note the run-up in the stock before December 22nd, when Elevation Partners invested $100M in the company. People who work at public companies go to jail for trading based on this sort of knowledge, yet somehow the market was able to guess that this was going to happen. On Friday the 19th, analysts couldn’t figure out why the stock price was going up, and then on Monday the 22nd they got the investment. Hmmm….
So, now you’re thinking, Palm must have been great to their shareholders – I mean, they’re up over 200% since early December – that’s awesome, right? Except that 2008 was such a bad year for them that they were down almost 80% for the year by the time December started, and now they’re almost back to scratch:
One of my favourite lessons regarding percentages is fully embodied in the graph above: if you’re down 80%, that means that you need a 400% increase to get back to where you started. This is one reason why the current financial crisis is going to be really painful for people who bought at the peak. You need a lot of “20% rallies” (about 7) to get you back to where you started if you are down 60-80%.
Speaking, of which, let’s look at how Palm has done over the years. They IPO’d back on March 2nd, 2000. They pretty much were the peak of the Internet bubble (another aside: through the magic of finance and arbitrage the publicly floated part of Palm was briefly worth more than its entire parent – which still owned 95% of the company):
They’re down a whopping 98.9% since then, so if you bought and held, you’re almost certainly never going to get back to where you started (unless they announce a heckuva lot more products like the Pre).
If we go back a little further, we can see one of my favourite facts about percentages in Sprint’s share price. For background, Sprint was a darling of the New Economy and gained almost 100% between 1999. It then declined and is down almost 99% over the past 10 years:
Two things I love here:
- If you’re up 100% and then you by twice what you gained (i.e., a $1 stock goes to $2 to $0.01), you’re not down, 200%, you’re down 99%
- People think there’s very little difference between a stock that’s down say 99% vs. 99.5%, but in fact the change is massive. The stock has to drop by 50% from when it was already down 99% to get to 99.5%. ($100 -> $1 -> $0.50). This is trivial if you’re already down 99%; soul-crushing if you bought in when it was down 99%.
So there you go, some Sunday evening stats. Hopefully it wasn’t too painful and maybe even insightful…
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