---
title: Adaptive Online Portfolio Selection
url: https://www.ml-quant.com/papers/repec/eee-ejores-v-321-y-2025-i-1-p-214-230/
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: RePEc:eee:ejores:v:321:y:2025:i:1:p:214-230
source_url: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0377221724006933%3Bh%3Drepec%3Aeee%3Aejores%3Av%3A321%3Ay%3A2025%3Ai%3A1%3Ap%3A214-230
featured: 2025-01-01
citations: unknown
topic: Portfolio & Allocation
---


# Adaptive Online Portfolio Selection

The article introduces a new online portfolio selection strategy that considers transaction costs and uses an adaptive scheme for sequential parameter decision, yielding higher cumulative returns and competitive Sharpe ratios than existing strategies.

- Source: https://econpapers.repec.org/scripts/redir.pf?u=http%3A%2F%2Fwww.sciencedirect.com%2Fscience%2Farticle%2Fpii%2FS0377221724006933%3Bh%3Drepec%3Aeee%3Aejores%3Av%3A321%3Ay%3A2025%3Ai%3A1%3Ap%3A214-230
- Identifier: RePEc:eee:ejores:v:321:y:2025:i:1:p:214-230
- Released: 2025-01-01
- First featured: Quant Letter No. 80 (2025-01-01): https://www.ml-quant.com/issues/2025-01-01/
- Citations (Semantic Scholar): not tracked
- Published in: not yet
- Topic: Portfolio & Allocation

## Related

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- [Adaptive Online Portfolio Selection with Transaction Costs](https://www.ml-quant.com/papers/repec/taf-quantf-v-24-y-2023-i-1-p-59-82/): The research proposes a new algorithm for online portfolio selection that improves return prediction accuracy by considering peer impact.
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