# kaal:claim:3409548-025

**Claim.** Machine learning applied to execution algorithms lets large orders be split into thousands of smaller transactions without moving the market, with the algorithm adjusting its aggressiveness to market conditions.

**Type.** mechanism  **Support.** asserted

**Holds when.**

- large order execution

**Source quote.**

> ML can be applied in execution algorithms that help execute large orders by dividing them into thousands of smaller transactions without influencing the market, while adjusting their aggressiveness to the market situation.

**From.** Kaal, *Financial Technology and Hedge Funds* (2019), III.1 Transaction Speed, page 22

**Cite as.** Kaal, Financial Technology and Hedge Funds (2019). SSRN: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3409548

**Verify.** sha256 of source PDF `74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40` at https://raw.githubusercontent.com/wulfkaal/Academic-Papers/main/papers/pdf/Kaal%20-%202019%20-%20Financial%20Technology%20and%20Hedge%20Funds.pdf

**Topics.** ai-and-agents, economics

**Keywords.** machine-learning, execution-algorithms, market-impact, transaction-speed

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