“When we try to pick out anything by itself, we find it hitched to everything else in the Universe.” – John Muir, Mountaineer
It’s one thing to build models and share insights about specific players in the trading and asset management universe. It’s an entirely different thing to perform comparative analysis of that specific modeling to develop various rankings of a community of players. This latter point is precisely where the accumulation of Alphacution’s modeling and research is now taking us, and that new level of insight is, frankly, a bit mindblowing.
Our previous Feed post that illuminated an apparent anomaly with Jane Street’s stock selection strategy was one of our first examples. For this one, we look at a ranking of average stock positions by shares for a selection of leading quant hedge fund managers, market makers and proprietary trading firms. Now, what’s truly fascinating here is if you consider these players in order of average stock position (by shares) you realize that what you are simultaneously looking at is a ranking by strategy turnover frequency – or, in short, strategy speed.
Let’s find out…
Here’s the same exhibit presented in log scale to spread out the data:
The basic logic behind our claim that average stock position in shares is indicative of strategy turnover frequency is this:
Market liquidity is the key limiting factor – along with explicit trading costs – for how much “edge” can be harvested without degrading performance. In other words, market liquidity helps define strategy capacity. Furthermore, faster strategies – those with high turnover and short holding periods – tend to have lower capacities. Think of it like this: The more a strategy needs to interact with market liquidity with specific timing, the lower its capacity is likely to be. Also, faster strategies tend not to accumulate much in the way of positions because their outsized performance is dependent precisely on the fact that they hold the minimum amount of risk.
Therefore, one way to expand the capacity of fast strategies is not to build positions but to expand the roster of products that are subject to the strategy. Alternatively, slower strategies tend to have higher capacities (for the opposite reason as stated above) and tend to accumulate bigger positions. Here, too, the capacity of slower strategies can be further expanded by expanding the roster of products targeted by the strategy.
So, generally speaking, the more/less capital you need to put to work in the market (while maintaining performance expectations), the slower/faster you will likely need to trade – and the bigger/smaller your average position is likely to be…
Now, go back to the charts and see if this logic makes sense to you.
Either way, send feedback…
One last thing: This analysis is like opening Pandora’s box. There will be a ton of room for debate around the edges. For instances, most of these firms are multi-strats. How do we account for the impact of that on average turnover frequency? And, any firm can purposefully trade small. Have we taken this into consideration? Certainly, there are numerous other factors to deconstruct and defend. We have arguments for many of these pushbacks that we can lay out in a longer post or deep-dive case study at some point…
This is simply a first pass at the idea that there is an empirical method available to measure and rank some of the most mysterious and mythological phenomena in the global markets ecosystem (such as how Renaissance figured out how to trade so slow on an automated basis). In time, and with further development of the modeling – including certain position level liquidity modeling – we may be able to assign actual average durations to each strategy.
Who knows? We never know what we will learn as we embark upon on each new puzzle-solving adventure. We only expect that we will learn something new and useful along the way which can be converted into intelligence for market actors and their stakeholders…
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