More Web Proxy on the site http://driver.im/

Article

Algorithms for portfolio management based on the Newton method

Authors:

Robert E. SchapireAuthors Info & Claims

ICML '06: Proceedings of the 23rd international conference on Machine learning

Pages 9 - 16

https://doi.org/10.1145/1143844.1143846

Published: 25 June 2006 Publication History

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.

References

[1]

Agarwal, A., & Hazan, E. (2005). New algorithms for repeated play and universal portfolio management. Princeton University Technical Report TR-740-05.

[2]

Algoet, P., & Cover, T. (1988). Asymptotic optimality and asymptotic equipartition properties of log-optimum investment. Annals of Probability, 2, 876--898.

[3]

Bell, R., & Cover, T. (1980). Competitive optimality of logarithmic investment. Mathematics of Operations Research, 2, 161--166.

Digital Library

[4]

Bell, R., & Cover, T. (1988). Game-theoretic optimal portfolios. Management Science, 6, 724--733.

Digital Library

[5]

Blum, A., & Kalai, A. (1999). Universal portfolios with and without transaction costs. Machine Learning, 35, 193--205.

Digital Library

[6]

Borodin, A., El-Yaniv, R., & Gogan, V. (2004). Can we learn to beat the best stock. Journal of Artificial Intelligence Research, 21, 579--594.

Digital Library

[7]

Brookes, M. (2005). The matrix reference manual. {online} www.ee.ic.ac.uk/hp/staff/dmb/matrix/intro.html.

[8]

Cover, T. (1991). Universal portfolios. Mathematical Finance, 1, 1--19.

[9]

Hannan, J. (1957). Approximation to bayes risk in repeated play. In M. Dresher, A. W. Tucker and P. Wolfe, editors, Contributions to the Theory of Games, III, 97--139.

[10]

Hazan, E., Kalai, A., Kale, S., & Agarwal, A. (2006). Logarithmic regret algorithms for online convex optimization. To appear in the 19th Annual Conference on Learning Theory (COLT).

Digital Library

[11]

Helmbold, D., Schapire, R., Singer, Y., & Warmuth., M. (1998). On-line portfolio selection using multiplicative updates. Mathematical Finance, 8, 325--347.

[12]

Kalai, A., & Vempala, S. (2003). Efficient algorithms for universal portfolios. Journal Machine Learning Research, 3, 423--440.

Digital Library

[13]

Kalai, A., & Vempala, S. (2005). Efficient algorithms for on-line optimization. Journal of Computer and System Sciences, 71(3), 291--307.

Digital Library

[14]

Kelly, J. (1956). A new interpretation of information rate. Bell Systems Technical Journal, 917--926.

[15]

Larson, D. C. (1986). Growth optimal trading strategies. Ph.D. dissertation, Stanford Univ., Stanford, CA.

Digital Library

[16]

Lovász, L., & Vempala, S. (2003a). The geometry of logconcave functions and an O*(n3) sampling algorithm (Technical Report MSR-TR-2003-04). Microsoft Research.

[17]

Lovász, L., & Vempala, S. (2003b). Simulated annealing in convex bodies and an O*(n4) volume algorithm. Proceedings of the 44th Symposium on Foundations of Computer Science (FOCS) (pp. 650--659).

Digital Library

[18]

Luenberger, D. G. (1998). Investment science. Oxford: Oxford University Press.

[19]

Merhav, N., & Feder, M. (1992). Universal sequential learning and decision from individual data sequences. 5th COLT (pp. 413--427). Pittsburgh, Pennsylvania, United States.

Digital Library

[20]

Ordentlich, E., & Cover, T. M. (1996). On-line portfolio selection. 9th COLT (pp. 310--313). Desenzano del Garda, Italy.

Digital Library

[21]

Stoltz, G., & Lugosi, G. (2005). Internal regret in on-line portfolio selection. Machine Learning, 59, 125--159.

Digital Library

Cited By

Tsang MSit TWong H(2025)Adaptive robust online portfolio selectionEuropean Journal of Operational Research10.1016/j.ejor.2024.09.002321:1(214-230)Online publication date: Feb-2025
https://doi.org/10.1016/j.ejor.2024.09.002
Lin CHe X(2024)Portfolio Optimization with Multi-Trend Objective and Accelerated Quasi-Newton MethodSymmetry10.3390/sym1607082116:7(821)Online publication date: 30-Jun-2024
https://doi.org/10.3390/sym16070821
Xie KYin JYu HFu HChu Y(2024)Passive Aggressive Ensemble for Online Portfolio SelectionMathematics10.3390/math1207095612:7(956)Online publication date: 23-Mar-2024
https://doi.org/10.3390/math12070956
Show More Cited By

Index Terms

Algorithms for portfolio management based on the Newton method
1. Mathematics of computing
  1. Probability and statistics
    1. Statistical paradigms
      1. Statistical graphics

Recommendations

Portfolio Optimization Using Evolutionary Algorithms
Downside Loss Aversion and Portfolio Management

Downside loss-averse preferences have seen a resurgence in the portfolio management literature. This is due to the increasing use of derivatives in managing equity portfolios and the increased use of quantitative techniques for bond portfolio ...
Tutorial on portfolio credit risk management
WSC '04: Proceedings of the 36th conference on Winter simulation

The distribution of possible future losses for a portfolio of credit risky corporate assets, such as bonds or loans, shows strongly asymmetric behavior and a fat tail as the consequence of the limited upside of credit (the promised coupon payment) and ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image ACM Other conferences

ICML '06: Proceedings of the 23rd international conference on Machine learning

June 2006

1154 pages

ISBN:1595933832

DOI:10.1145/1143844

Program Chairs:
William Cohen,
Andrew Moore

Copyright © 2006 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

Publisher

Association for Computing Machinery

New York, NY, United States

Publication History

Published: 25 June 2006

Permissions

Request permissions for this article.

Request Permissions

Check for updates

Qualifiers

Article

Acceptance Rates

ICML '06 Paper Acceptance Rate 140 of 548 submissions, 26%;

Overall Acceptance Rate 140 of 548 submissions, 26%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

136
Total Citations
View Citations
1,477
Total Downloads

Downloads (Last 12 months)78
Downloads (Last 6 weeks)13

Reflects downloads up to 14 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

Tsang MSit TWong H(2025)Adaptive robust online portfolio selectionEuropean Journal of Operational Research10.1016/j.ejor.2024.09.002321:1(214-230)Online publication date: Feb-2025
https://doi.org/10.1016/j.ejor.2024.09.002
Lin CHe X(2024)Portfolio Optimization with Multi-Trend Objective and Accelerated Quasi-Newton MethodSymmetry10.3390/sym1607082116:7(821)Online publication date: 30-Jun-2024
https://doi.org/10.3390/sym16070821
Xie KYin JYu HFu HChu Y(2024)Passive Aggressive Ensemble for Online Portfolio SelectionMathematics10.3390/math1207095612:7(956)Online publication date: 23-Mar-2024
https://doi.org/10.3390/math12070956
Shi RPalomar D(2024)SAOFTRL: A Novel Adaptive Algorithmic Framework for Enhancing Online Portfolio SelectionIEEE Transactions on Signal Processing10.1109/TSP.2024.349569672(5291-5305)Online publication date: 2024
https://doi.org/10.1109/TSP.2024.3495696
Lai ZLi CWu XGuan QFang L(2024)Multitrend Conditional Value at Risk for Portfolio OptimizationIEEE Transactions on Neural Networks and Learning Systems10.1109/TNNLS.2022.318389135:2(1545-1558)Online publication date: Feb-2024
https://doi.org/10.1109/TNNLS.2022.3183891
Lian HWu DHou BWu JHe Y(2024)Online Learning From Evolving Feature Spaces With Deep Variational ModelsIEEE Transactions on Knowledge and Data Engineering10.1109/TKDE.2023.332636536:8(4144-4162)Online publication date: Aug-2024
https://doi.org/10.1109/TKDE.2023.3326365
Zheng ZShao JDeng SZhu AShen HZhou X(2024)Cross-Insight Trader: A Trading Approach Integrating Policies with Diverse Investment Horizons for Portfolio Management2024 IEEE 40th International Conference on Data Engineering (ICDE)10.1109/ICDE60146.2024.00356(4685-4698)Online publication date: 13-May-2024
https://doi.org/10.1109/ICDE60146.2024.00356
Ding WChen ZJiang HLin YLin F(2024)Trend-Heuristic Reinforcement Learning Framework for News-Oriented Stock Portfolio ManagementICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447993(5120-5124)Online publication date: 14-Apr-2024
https://doi.org/10.1109/ICASSP48485.2024.10447993
Tu XLi B(2024)Online portfolio selection with parameterized characteristicsJournal of Accounting Literature10.1108/JAL-06-2024-0114Online publication date: 21-Oct-2024
https://doi.org/10.1108/JAL-06-2024-0114
Zhang YLin HLi JYang X(2024)Combined peak price tracking strategies for online portfolio selection based on the meta-algorithmJournal of the Operational Research Society10.1080/01605682.2023.229597575:10(2032-2051)Online publication date: 6-Mar-2024
https://doi.org/10.1080/01605682.2023.2295975
Show More Cited By

View Options

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Media

Figures

Other

Tables

View Table of Contents