Stairs Up, Elevator Down
Vol #30 - March 19th, 2021
The market always seems to drop faster than it climbs.
When markets are marching upwards, the compounding effects of quarter and half percentage gains are barely noticeable. Every once in a while there’s a big green day that makes the headlines for its explainability and patriotism.
But when the markets drop, it can feel like the bottom is falling out. Naively optimistic offers that were a tick away at entry, hang hopelessly far away just minutes later. The pennies we ground into dollars seem like rounding errors on the sea of red.
Part of this is behavioral. We’re hard wired to feel losses more distinctly than gains. It’s been shown that it takes almost $2000 of gains to offset $1000 in losses. We also tend to anchor at high water marks, and any drop from the top blinds out the medium and long term gains.
In the options world, this is often explained by the existence of “skew” in market prices. The foundational Black Scholes model takes a probabilistic approach to valuation - how much does this stock move on average and how many days does it have to do that. Skew takes that a step further and says, “stock moves X% here, but if it starts dropping, it’s going to move 2x%”.
This still only models a static world, and nice line graphs don’t explain the messiness of reality. The train schedule might be within a minute of accuracy 95% of the time, but when it’s off, you’re a half hour late to your very important date. (For NYC commuters, Grand Central actually builds in an extra minute for you.)
Before I’d ever traded an option, I read Benoit Mandelbrot’s book “The Misbehavior of Markets” during my junior year of college. Whether it was the pretty pictures of his famous fractals, or the romanticism of reading a French mathematician’s work in a Parisian cafe, his thoughts on market chaos stuck with me. I’ve recently picked it up again to help think about risk and price distributions in crypto markets.
It was only at the turn of the last century that important work by Louis Bachelier was done around probability and markets. This groundbreaking research to describe bond prices was leaps and bounds ahead of his peers, but it still assumed normal distribution. Mandelbrot built on this to explain how chaos replicates itself.
There are plenty of real world examples of normal distributions, like the birthweight of babies or the circumference of cucumbers. But for processes that involve feedback cycles - get ready for wild, gnarly, unfettered fat tails.
Market volatility tends to cluster, and trouble runs in streaks. This happens at the microstructure order book level, where flickering bids and offers can trip a chain reaction from algorithms. It also happens at the macro level where a bank dropping a LIBOR bid creates a cross asset sell-off amidst rising inflation fears.
Mandelbrot suggests there is market time, which is different from real world time. Traders call these fast markets. While you need to fill the dog days of summer with intern eating contests, when the market moves, there are never enough seconds on the clock.
As investors we need to balance this feedback with our objectives. No matter what your strategy is, the news cycle will be full of dissonance. While we might live on a regular schedule of daily news and monthly brokerage statements, the market operates on its own agenda.
Assets are risky because we can’t predict their prices in the near term. The most powerful weapon against uncertainty is time. Playing the long game means those intense moments of red and those long lulls of green all fade together.