A new arXiv paper shows how optimal execution strategies must account for market maker reactions. The feedback loop changes the cost of trading high-volume stocks like Apple.
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A paper posted on arXiv this week models how large institutional orders interact with market maker quotes, creating a feedback loop that changes the cost of trading. Titled "Optimal Execution and Macroscopic Market Making," the model addresses the classic problem of splitting a large trade to reduce market impact. It adds a layer: market makers adjust prices in response to the order flow they see, which in turn affects the optimal execution schedule.
The paper models what it calls macroscopic market making – the idea that market makers do not simply set passive quotes but react to aggregated order flow. This creates a feedback loop. The trader's execution algorithm changes the market maker's view. The quotes shift. The optimal strategy adjusts. The model extends classic optimal execution frameworks like Almgren-Chriss by incorporating this learning dynamic.
For a stock like Apple (AAPL), which trades billions of dollars daily, the finding is directly relevant. An institutional trader moving a large block sees one price trajectory. The model suggests that trajectory shifts as the market maker recognizes the flow. In a name with high institutional participation, the aggregate order flow from multiple traders shapes the execution cost, not just a single algorithm's behavior.
Practical implications: optimal execution schedules are not fixed. They evolve with the market maker's learning. Algorithms that ignore this feedback may overestimate fills at the initial quoted spread. The paper provides a formal framework for thinking about timing and liquidity in high-volume names. No companion code has been released yet.
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