poster

Meta optimization and its application to portfolio selection

Authors:

Arindam BanerjeeAuthors Info & Claims

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

Pages 1163 - 1171

https://doi.org/10.1145/2020408.2020588

Published: 21 August 2011 Publication History

Abstract

Several data mining algorithms use iterative optimization methods for learning predictive models. It is not easy to determine upfront which optimization method will perform best or converge fast for such tasks. In this paper, we analyze Meta Algorithms (MAs) which work by adaptively combining iterates from a pool of base optimization algorithms. We show that the performance of MAs are competitive with the best convex combination of the iterates from the base algorithms for online as well as batch convex optimization problems. We illustrate the effectiveness of MAs on the problem of portfolio selection in the stock market and use several existing ideas for portfolio selection as base algorithms. Using daily S\&P500 data for the past 21 years and a benchmark NYSE dataset, we show that MAs outperform existing portfolio selection algorithms with provable guarantees by several orders of magnitude, and match the performance of the best heuristics in the pool.

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Cited By

Yang XZheng XZheng L(2024)Online Portfolio Selection Strategy with Side Information Based on Learning with Expert AdviceAsia-Pacific Journal of Operational Research10.1142/S021759592350041041:05Online publication date: 5-Feb-2024
https://doi.org/10.1142/S0217595923500410
Hayashi YNakamura A(2024)Risk Diversification Strategy with Moving Average Reversion for Automatic Portfolio Optimization2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825315(910-917)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825315
Kroo DGorgadze VOvchinnikov GBarger A(2024)Optimal Portfolios Of Liquidity Positions2024 6th International Conference on Blockchain Computing and Applications (BCCA)10.1109/BCCA62388.2024.10844460(774-781)Online publication date: 26-Nov-2024
https://doi.org/10.1109/BCCA62388.2024.10844460
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Index Terms

Meta optimization and its application to portfolio selection
1. Information systems
  1. Information systems applications
    1. Data mining

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cover image ACM Conferences

KDD '11: Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining

August 2011

1446 pages

ISBN:9781450308137

DOI:10.1145/2020408

General Chair:
Chid Apte
IBM Research
,
Program Chairs:
Joydeep Ghosh
UT Austin
,
Padhraic Smyth
UC Irvine

Copyright © 2011 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]

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Published: 21 August 2011

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KDD '11: The 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

August 21 - 24, 2011

California, San Diego, USA

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Overall Acceptance Rate 1,133 of 8,635 submissions, 13%

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Cited By

Yang XZheng XZheng L(2024)Online Portfolio Selection Strategy with Side Information Based on Learning with Expert AdviceAsia-Pacific Journal of Operational Research10.1142/S021759592350041041:05Online publication date: 5-Feb-2024
https://doi.org/10.1142/S0217595923500410
Hayashi YNakamura A(2024)Risk Diversification Strategy with Moving Average Reversion for Automatic Portfolio Optimization2024 IEEE International Conference on Big Data (BigData)10.1109/BigData62323.2024.10825315(910-917)Online publication date: 15-Dec-2024
https://doi.org/10.1109/BigData62323.2024.10825315
Kroo DGorgadze VOvchinnikov GBarger A(2024)Optimal Portfolios Of Liquidity Positions2024 6th International Conference on Blockchain Computing and Applications (BCCA)10.1109/BCCA62388.2024.10844460(774-781)Online publication date: 26-Nov-2024
https://doi.org/10.1109/BCCA62388.2024.10844460
Yang XZheng XLi JHuang Q(2024)Aggregating closing position experts for online portfolio selectionApplied Economics Letters10.1080/13504851.2024.2368267(1-12)Online publication date: 19-Aug-2024
https://doi.org/10.1080/13504851.2024.2368267
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
Lyu BWu BGuo SGu JChing W(2024)Robust online portfolio optimization with cash flowsOmega10.1016/j.omega.2024.103169129(103169)Online publication date: Dec-2024
https://doi.org/10.1016/j.omega.2024.103169
Song GZhao TMa XLin PCui C(2024)Reinforcement Learning-based Portfolio Optimization with Deterministic State TransitionInformation Sciences10.1016/j.ins.2024.121538(121538)Online publication date: Oct-2024
https://doi.org/10.1016/j.ins.2024.121538
Dai HLai FHuang CLv XZaidi F(2024)Novel online portfolio selection algorithm using deep sequence features and reversal informationExpert Systems with Applications10.1016/j.eswa.2024.124565255(124565)Online publication date: Dec-2024
https://doi.org/10.1016/j.eswa.2024.124565
Lin HZhang YYang X(2024)Online portfolio selection of integrating expert strategies based on mean reversion and trading volumeExpert Systems with Applications10.1016/j.eswa.2023.121472238(121472)Online publication date: Mar-2024
https://doi.org/10.1016/j.eswa.2023.121472
Guo SGu JChing WLyu B(2023)Adaptive online mean-variance portfolio selection with transaction costsQuantitative Finance10.1080/14697688.2023.228713424:1(59-82)Online publication date: 19-Dec-2023
https://doi.org/10.1080/14697688.2023.2287134
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