My personal trading hero is Jim Simons, a differential geometer who founded the world-famous hedge fund Renaissance Technologies — more on him in another post. But from a social welfare perspective, I’d have to award the MVP of the financial markets over the last 50 years to Warren Buffet.
Unlike the single-expiration oil futures market we’ve been discussing thus far, equity markets don’t have a definite expiration date, and it’s harder to draw clean lines between long-term suppliers, long-term demanders, and traders. However, no matter how you define these terms, Warren Buffet is a long-term demander — he loves buying great companies at attractive prices and holding them forever.
Buffet’s activity reduces the noise in the price level on a macroscopic scale. In addition, he’s so big that his decisions alone can cause companies to literally become worth more — markets are not just about predicting the future, they’re also about allocating capital, and his investments into good companies during times of crisis helped make them better. Finally, his price sensitivity reduces the pie — when long-term investors are smart about where they get in, there’s less surplus remaining for shorter-term actors. The influence of his approach has extended well beyond Berkshire’s portfolio; the man is truly a giant.
How did Buffet get so big? Aside from time in the game, one major factor is that his signals have a lot of capacity, because they are long-term in nature.
Moving back to oil: suppose the price of oil is currently at $60, and consider the following two speculation signals that our trading firm might have:
S1: oil will be worth $61 in a minute;
S2: oil will be worth $61 in an hour.
To simplify issues of counterfactual price with vs. without our actions, let’s assume that these signals are known only to us, and are driven by advance knowledge of fundamental news events (e.g. OPEC) that are released publicly and atomically; everybody will see these events and immediately agree that the new price is $61, allowing us to close our position for free.
Which of the two signals is worth more to us? S1 gives us a higher “return per unit time”; the information advantage it provides us is sharper relative to the background noise in the price. But S2 is more valuable for all but the most extreme levels of low risk tolerance and high opportunity cost of capital, because we will be able to get more size off before saturating the signal. This is because the price impact of exogenous flows tends to decay over time.
The markets are not mathematical; there is no proof of the above statement, and in certain situations it won’t hold. But here is an intuitive argument for why we might expect it to be true:
- Frequently, participants need to trade significantly more than the size posted on the orderbook. Therefore, they split up their orders into many small pieces.
- As a result of 1), everybody expects “flows” to exhibit serial correlation over time.
- That implies that during a flow, the price in part reflects the market’s expectations of how long the current flow will continue.
- Immediately after a flow ends, those expectations ought to decrease, and we should observe reversion in the price.
Those with physics backgrounds may find the implications of this equilibrium interesting — Brownian motion does not behave this way.
Given impact decay, it’s clear that S2 can be sized bigger than S1: we can copy the same trading plan we used to trade S1, wait a few minutes for some impact to decay, then trade more size. The market may wise up eventually and stop providing liquidity depending on how detectable our activity is.
So: slower signals are worth more than faster signals of the same size. What other considerations shape trading firms’ decisions about what timescales to prioritize during signal search?
- More data is available on shorter timescales, so “dumb exhaustive” ML architectures can be used to search a large space of ideas automatically without running into the noise wall.
- It’s more fun to make $1000 in a minute than in a month.
- Slower signals are worth more because you can trade more size in their direction; but once you saturate your risk-taking capacity, the fraction of the value of the signal you are able to capture starts to go down. If you knew that the S&P 500 was going to go up 1% tomorrow, conservatively you’d need to trade billions of dollars before saturating the signal.
- Very long (>1 year) holding periods can take advantage of the long-term capital gains tax.
- Signals that are smaller than the bid-ask spread may be difficult to execute, and it’s harder to find greater-than-spread 1 hour signals than 1 day signals.
With the rise of cheap compute and public data analysis tooling over the last few decades, consideration 1) has become more important. Simultaneously, the bid-ask spread has decreased due to the rise of algorithms, so consideration 5) has become less binding. Consequently, we should expect that the marketplace is doing much, much more research into short-term signals than long-term signals, relative to before.
I conjecture that this shift in focus has played a significant role in the macroscopic efficiency decline observed by Cliff Asness.