savageinfidel

In last week’s post I mentioned three stages in the evolution of a market: Identifying full-blown bubbles is easy. What’s not so easy is identifying the transitions that bookend a bubble. It’s not easy to know precisely when a rational, fundamentals-driven boom will morph into an irrational, sell-to-the-greater-fool frenzy. It’s not easy to know precisely when an irrational frenzy will reverse into an equally irrational stampede for the exits. These three stages – rational boom, frenzied bubble, irrational panic – are in fact just three out of a total of six stages in my own idiosyncratic (and highly unscientific) taxonomy of bull and bear markets. Here’s how it works. The first stage in any bull market is what I like to call the bounce . A sector or asset class that has been moribund for years or even decades suddenly starts rising in price. This could be due to exogenous shocks such as regulatory or technological changes; it could be due to Schumpeterian creative destruction , wh...

Human beings are good at interpolation, passable at extrapolation, bad at identifying inflexion points, and downright terrible at processing one-off events. It’s no coincidence that these skills are, sequentially, associated with increasing investment success: the harder it is to do something, the more money one makes for doing it. This particular progression from easy to difficult is not merely the artifact of some deep-seated behavioral tendency or evolutionary bias. Deterministic and presumably unbiased algorithms, faced with unprecedented events, perform just as badly as humans. This is only to be expected: the very word ‘unprecedented’ implies that there is no baseline to build from or compare with, a circumstance under which most algorithmic approaches tend to flounder. Unfortunately for all concerned, real life is full of one-off events. What we call history is, as Rudge memorably puts it, just one bloody thing after another . And that’s precisely why I’m suspicious of atte...

In a previous post , I mentioned that bubbles were characterized by – indeed, defined by – positive feedback. This idea, and more generally, the importance of feedback in driving market dynamics, deserves a lot more ink. Here’s a first installment. Classical economics is often concerned with analyzing various equilibrium outcomes (“comparative statics”). These outcomes are usually generated or maintained by some sort of negative feedback. The simplest example is that of security prices. Under the efficient markets hypothesis, each security has a fair price reflecting its ‘fundamental value’; furthermore, this fundamental value is known to market participants in aggregate. If the actual market price drops below this value, people step in to buy the security; if the price rises above it, people step in to sell. As a result of this negative feedback, the market price equilibriates to its natural or fundamental value. Unfortunately markets do not always tend to equilibrium. Negative...

In yesterday’s post I mentioned that bubbles were exponential, scale-invariant and self-similar, making it virtually impossible to time their collapse. Let’s flesh out this assertion by looking at a particular market index. For the first 17 years of its existence, this index had a mean of 100 and a standard deviation of 56. (Prices have been scaled to avoid easy recognition). That’s a pretty stable time series. Then something happened. Over the next 8.5 years, the index went from a starting value of 200 (already near the upper end of its previous range) to a value of 700. What’s more, this rise took on exponential, maybe even super-exponential characteristics, as the graph below makes clear. Would you sell? If you did, you’d be out of luck. Because over the next 25 months, the index went from 700 to 1100. Once again the rise looked exponential or better: (Note that this graph has the same start date as the previous one, but different scales on each axis). Would you sell? If you...

Yesterday’s post revealed how (and why) a large portion of the financial industry’s revenues came to depend on explicit regulatory arbitrage. This is fairly common knowledge, and should come as no surprise to industry observers. What’s not so well known is that many ‘classic’ arbitrages, which appear at first glance to be regulation-independent, also depend implicitly on regulatory asymmetries to work. The textbook example is bond futures arbitrage. While anyone can buy bonds, some market participants are forbidden to sell bonds short. To express a bearish view, this latter group has to sell bond futures. This makes bond futures systematically cheap relative to cash bonds. Arbitrageurs have only to take the opposite side of this transaction to make easy money. Of course, the classic bond futures arbitrage no longer exists (‘”too many eyeballs”), but other, subtler examples abound. Consider a trade that was very popular with fixed income arbitrageurs earlier this decade: the puts...