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Algorithms for portfolio management based on the Newton method
Abstract
We experimentally study on-line investment algorithms first proposed by Agarwal and Hazan and extended by Hazan et al. which achieve almost the same wealth as the best constant-rebalanced portfolio determined in hindsight. These algorithms are the first to combine optimal logarithmic regret bounds with efficient deterministic computability. They are based on the Newton method for offline optimization which, unlike previous approaches, exploits second order information. After analyzing the algorithm using the potential function introduced by Agarwal and Hazan, we present extensive experiments on actual financial data. These experiments confirm the theoretical advantage of our algorithms, which yield higher returns and run considerably faster than previous algorithms with optimal regret. Additionally, we perform financial analysis using mean-variance calculations and the Sharpe ratio.
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Citations
Cited by
Introduction to Online Convex Optimization
Hazan, E. (2016) · Foundations and Trends in Optimization · doi:10.1561/2400000013 · Cited by 747
Self-citationA Deep Reinforcement Learning Framework for the Financial Portfolio Management Problem
Jiang, Z., Xu, D., & Liang, J. (2017) · arXiv · doi:10.48550/arxiv.1706.10059 · Cited by 321
Metadata
- Authors
- Agarwal, A., Hazan, E., Kale, S., & Schapire, R. E.
- Venue
- Proceedings of the 23rd international conference on Machine learning - ICML '06
- Publisher
- ACM Press
- Published
- 2006
- Pages
- 9-16
- OpenAlex
- W2068643490
- Cited by
- 230
- References
- 0
- FWCI
- 3.61
- Type
- conference paper
- Language
- en
- Open access
- closed
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- CoChat. (2026). Citation check: Algorithms for portfolio management based on the Newton method. https://app.cochat.ai/papers/10.1145/1143844.1143846
- The paper
- Agarwal, A., Hazan, E., Kale, S., & Schapire, R. E. (2006). Algorithms for portfolio management based on the Newton method. Proceedings of the 23rd international conference on Machine learning - ICML '06. https://doi.org/10.1145/1143844.1143846
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Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
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