---
title: Dynamic Portfolio Choice with Transaction Costs using Machine Learning
url: https://www.ml-quant.com/papers/ssrn/4642269/
site: ML-Quant (https://www.ml-quant.com)
updated: 2026-09-26
license: Summaries CC BY 4.0; links go to the original sources
index: https://www.ml-quant.com/llms.txt
identifier: SSRN 4642269
source_url: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4642269
featured: 2023-11-29
citations: unknown
topic: Portfolio & Allocation
---


# Dynamic Portfolio Choice with Transaction Costs using Machine Learning

A new computational framework is introduced for solving dynamic portfolio choice problems, using Gaussian process regression and Bayesian active learning, suggesting that more assets can mitigate some illiquidity.

- Source: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=4642269
- Identifier: SSRN 4642269
- Released: 2023-08-18
- First featured: Quant Letter No. 27 (2023-11-29): https://www.ml-quant.com/issues/2023-11-29/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

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