Three types of systematic strategy that “work”
Broadly, there are three types of systematic trading strategy that can “work.” In order of increasing turnover they are:
- Risk premia harvesting
- Economically-sensible, statistically-quantifiable slow-converging inefficiencies
- Trading fast-converging supply/demand imbalances
This post provides an overview of each.
1. Risk Premia Harvesting
Risk Premia Harvesting is typically the domain of wealth management, but it’s important to any trader who likes money.
A risk premium is the excess return you might expect over and above risk-free cashflows for taking on certain unattractive risks. Equity Risk Premium, for example, is how you say “Stonks, they go up” if you work for Blackrock (though, they’re really referring to the extent to which they “go up” more than an equivalent less risky thing).
The basics of risk premia harvesting are:
- Intentionally expose your portfolio to diverse sources of risk that tend to be rewarded (noting that not all risks are rewarded)
- Manage risk sensibly so no risk dominates at any time
- Be patient and chill (the hardest part for most)
A very simple example of such a risk premia harvesting strategy is the 60/40 stock/bond portfolio.
More balanced implementations include Bridgewater’s All-Weather portfolio and Risk Parity strategies generally, which attempt to equalize risk across assets or sources of risk premia.
Doing useful things like providing liquidity in a highly stressed market, or making a two-sided market at all times is also arguably risk premia harvesting since you’re taking on the risk of getting run over by those who can choose when to trade.
Risk premia harvesting is something nearly every trader should do. In fact, we like to treat it as the foundation of a serious independent trading operation, to which other edges and strategies can be added over time.
Active traders need to be aware of risk premia too. For example, if you’re shorting stocks, you’re facing a big hurdle in the form of the equity risk premium. You need to be right over and above the expected drift in the asset. On the other hand, if you’re long stocks in an active strategy, there’s a decent chance you’ll make money even if your reason for getting into the position was wrong.
It’s better to play easy games than hard ones, and it’s nice to still make money when you’re wrong.
2. Economically-sensible, statistically-quantifiable slow-converging inefficiencies
These are noisy tendencies for assets to trade too cheap or expensive at certain times due to behavioral or structural effects. Examples include momentum effects, seasonal regularities, and effects due to indexing inclusion/exclusion. We can probably lump style factors (momentum, value, carry, quality, low vol) and most medium frequency statistical arbitrage approaches in this bucket too.
This stuff tends to be noisy and slow to converge, so you have to analyze it in aggregate over large data sets.
It also means that your P&L is very slow to converge to expected returns – which can be challenging to sit through.
So to trade these edges effectively we need:
- To understand why the inefficiency would persist.
- Faster converging metrics around what we’re exploiting so we’re not the last to know when the inefficiency disappears.
- Patience and discipline to keep swinging the bat – you have to let the noisy edge play out over a large number of bets.
Useful sources for finding these trades include:
- Expected Returns, Antti Ilmanen
- Efficiently Inefficient, Lasse Pedersen
- Positional Option Trading, Euan Sinclair (which literally gives you stuff to trade)
- Active Portfolio Management, Grinhold and Kahn
Generally, these edges are less reliable than risk premia harvesting and fast-converging flow effects that we’ll discuss below.
In my experience, independent traders usually spend too much time on these edges, and too little on risk premia harvesting.
3. Fast-converging supply-demand imbalances
This stuff is the bread-and-butter of proprietary trading firms.
Short term supply and demand imbalances create dislocations in prices which fast traders can “disperse” by trading against them and offsetting risk elsewhere.
These trades are conceptually simple and economically sound. For example, I might buy futures on Shanghai INE and buy a similar contract cheaper on Singapore SGX for a profit (after costs).
That’s a simple arb, but it carries risks because we can’t trade instantaneously. Generally, we’re doing riskier trades than this simple arb, that we expect to work out on average.
Often, these trades involve looking to buy cheap and sell high based on simple relative-value models, the assumption being that deviations from (relative) fair value will converge. So trading models in this space are less about predicting the future (as per 2, above) and more about extrapolating the present (Q vs P).
Advantages of these trades include:
- They’re easy to understand and economically simple
- They converge fast to expected returns. You know quickly when your model is out or you don’t have an edge anymore. They fit nicely with Kris Abdelmessih’s “measurement and normalization” paradigm
The disadvantages of these trades are that they are capital constrained and require significant investment in infrastructure and staff.
Whilst this area isn’t practical for “home gamers”, the lessons here are crucial for a good understanding of the market.
Systematic trading strategies that “work” can be grouped into three categories:
- Risk premia harvesting
- Slow converging inefficiencies based on economic or structural effects
- Fast converging inefficiencies based on deviation from some notion of relative fair value
Independent traders should, generally, start off by focusing on (1), over time add a little of (2), but not as much of this as (1), and dig into (3) to understand and appreciate the efficiency of the markets.