[NOTE: this post needs some work. In particular, I think my framework for talking about social welfare is very incomplete, and doesn’t touch enough on the positive benefits of the existence of and social belief the market in the first place + which participants contribute most to that state]
Mechanically, how do trading firms implement spread collection strategies?
Back in the 1970s, the answer was to hire ex-football players to yell at each other in a “pit”. Institutional investors delivered buy/sell orders to brokers on the floor of the pit. The brokers would go to the market makers, ask for a bid/offer market, and trade the order against it.
Nowadays, the vast majority of trading is done electronically, and algorithms provide continuous two-way markets for humans and other algorithms to trade against. This is much more efficient: algorithms are cheap to run, easy to tweak, and can react very quickly. Barriers to entry have been lowered. The resulting competition has driven down bid-offer spreads dramatically over the last 30 years.
A first-order algorithm might look like: Have some fair value for oil (let’s say $60/barrel). Always be willing to buy one contract for 0.25 below fair, and sell for 0.25 above fair. If you get filled on the offer, increase your fair value, and vice versa. At the end of the day / week, close out of your positions, and hope that the spreads you earned exceed the “got run over” costs you incurred.
This fully automated strategy, as simple as it sounds, is probably profitable today in the right market conditions. I haven’t looked at the data, but I would guess that it was profitable 20 years ago even on normal days. Profitable or not, here are four ideas for improving it:
- Sometimes, you can predict ahead of time that a lot of information is coming out about the underlying supply and demand forces driving the oil price (OPEC meetings, oil company earnings reports, etc.) Other times, you can infer that “something is interesting is happening” even without knowing anything about the fundamentals, because you can detect patterns in the market data itself: e.g. if prices are moving a lot today on heavy volume. In these situations, it’s probably better to ask for more edge on your trades.
- If oil stock prices spike up, maybe you should be wary of selling oil futures for a bit.
- You look at historical data, and notice that your sales lose money when the weather is unusually cold. You’re not entirely sure why, but the data is highly significant, so you back off your algorithm.
- You fit a big machine learning model using all the data you can think of to predict the PNL (profit “n” loss) of your trades, and turn your algorithm off when it predicts you’ll lose money.
Once you have something that makes at least a little money on average, you can hire a bunch of smart people to come up with and implement ideas for making it better. Now you’ve got a fully automated trading business. Every idea you implement makes you more money-per-day, as opposed to a one-time gain.
Of all the ideas you might try to pursue to improve your spread collection algorithm, which contribute more or less to overall market efficiency, as measured by reduction of noise in the price level and the amount of trading surplus captured by trading firms?
The noise question is kind of ambiguous — your actions serve to tamp down very short-term swings in the price, but also make it move a bit more slowly when information comes out. Overall, I think the second term is probably slightly dominant here, but neither matter much — your actions are having an almost imperceptible first-order effect on any kind of macroscopic timescale because you’re going back-and-forth so frequently. Real economy oil demanders and producers don’t care about the magnitude of short-term autocorrelations in the timeseries of price returns, they care about the average levels over longer time periods.
As for surplus, running this kind of algorithm in the first place reduces the trading firm value capture, since you are directly competing with other trading firms to reduce their spread collection profits. Any time you are willing to buy or sell against random people that might be end producers / demanders, you are effectively giving them more of the surplus in expectation. Ideas that get you to do more good providing trades will generally cut into other trading firms’ profits, while ideas that get you to do fewer bad providing trades will generally “increase the pie”.
Now recall that thus far in this narrative, I’ve assumed that spread collectors only trade against real producers / demanders. But once the spread collectors (electronic or otherwise) show up and start providing liquidity, a new class of trader will be incentivized to trade: the speculator. Speculators take liquidity as opposed to providing it, but they aren’t producers / demanders, just independent players with opinions about where the price is going to go. Marginal speculators tend to remove noise from the price level, especially longer-term speculators with large opinions and bankrolls. Their signals generally increase the pie, especially signals directly anticipating the activity of demanders / producers. There are probably also “noise traders” who are literally gambling, but I think the term noise trader is somewhat overused, and most people in the market are making money even though their behavior may not appear perfectly rational under a particular lens.1
As we can see, there are plenty of ways for trading firms to make money through increasing the pie, many of which don’t have a socially material effect on the macroscopic noise in the price level. This is somewhat concerning, because work in this direction doesn’t improve social welfare, so we should be worried that it’s over-incentivized. In particular, it’s not at all obvious that the system should get more efficient over time in the socially relevant sense.
The easiest way for trading firms to decrease the pie — namely, tighten spreads — incentivizes speculators to find ever-shorter horizon signals and increase the pie again2, potentially leading to a wasteful arms race that does not reduce macro-scale price noise or aggregate trading firm revenues. This has had significant implications for the frontier of trading firm competition and innovation over the past few decades, and perhaps the years to come. In the next post, I’ll flesh out this timescale issue a bit more carefully. You might want to check out this post in the meantime.