kaal:claim:3409548-025

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.

Source quote, verbatim

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, p. 22
https://ssrn.com/abstract=3409548 · source PDF

Cite as

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

Holds when

large order execution

Classification

mechanismsupport: assertedai-and-agentseconomics

Verify

The quote above is an exact substring of the source PDF, whose sha256 is 74227ab2656b06bfe4a29c942fc2ba26df9f476917e0a9f85ec038b8c3402c40. Extraction method: pdf-text-layer.
Attestation record: colloquium/attestations/4d64841f823dadc8...json
Verify the binding yourself: curl -s https://wulfkaal.github.io/claims/3409548-025.md | sha256sum

Positions extending this scholarly claim

Qualification: Integrating Generative AI and Model Context Protocol (MCP) with Applied Machine Learning for Advanced Agentic AI Systems