The best answer to that question comes from my ex-boss, mentor and more importantly dude friend: “You are at capacity when inertia sets in”
This means that when managers become reluctant to take a trade, this is when they reach capacity. It might be at 100M or at 2B. It is after all subjective. The same can be said about algorithmic strategies.
Algorithmic strategies are more scalable than humans. They can be deployed across larger universes and shorter periodicities. So, diminishing returns kick in later. Market impacts happens and returns come down eventually.
There are three reasons:
- Volume market impact: some strategies arbitrage inefficiencies. So, trading naturally correct them. They have built in capacity constraint
- Competition: market participants copy each other. Pie does not grow, it gets fragmented
- Conceptual shortcomings: that is the hardest problem to solve. Problems are often solved at a different level than they were created. There are four ways it can be solved
- go wider: expand your coverage universe
- go bigger: accept market impact as a necessary cost of doing business. This means expand limit orders, but it also means refine signals so as mitigate slippage
- go deeper: elicit trading: bait other market participants to take the other side so as to create volume. This is the new old thing. Remember “Reminiscence of a stock operator” when the veteran trader tests the market by observing how fast his orders were filled. HFT have perfected that craft.
- go different: money management is the new new old thing. Getting in is a choice, getting out is a necessity. Trades do not have to be all-in and all-out. Scaling in and out mitigate capacity issues