Identifying Small Mean Reverting Portfolios

  title={Identifying Small Mean Reverting Portfolios},
  author={Alexandre d'Aspremont},
Given multivariate time series, we study the problem of form ing portfolios with maximum mean reversion while constraining the number of assets in th ese portfolios. We show that it can be formulated as a sparse canonical correlation analysi s and study various algorithms to solve the corresponding sparse generalized eigenvalue pro blems. After discussing penalized parameter estimation procedures, we study the sparsity ver sus predictability tradeoff and the impact of predictability in various… CONTINUE READING
Highly Cited
This paper has 25 citations. REVIEW CITATIONS
Related Discussions
This paper has been referenced on Twitter 4 times. VIEW TWEETS

From This Paper

Figures, tables, and topics from this paper.


Publications citing this paper.
Showing 1-10 of 18 extracted citations

Optimizing sparse mean reverting portfolios

Algorithmic Finance • 2013
View 9 Excerpts
Highly Influenced

Mean-Reverting Portfolio With Budget Constraint

IEEE Transactions on Signal Processing • 2018
View 4 Excerpts
Highly Influenced

Optimal Portfolio Design for Statistical Arbitrage in Finance

2018 IEEE Statistical Signal Processing Workshop (SSP) • 2018
View 2 Excerpts

GPGPU-based identification of cointegrated portfolios

2017 IEEE Symposium Series on Computational Intelligence (SSCI) • 2017
View 2 Excerpts

Mean-reverting portfolio design via majorization-minimization method

2016 50th Asilomar Conference on Signals, Systems and Computers • 2016
View 3 Excerpts


Publications referenced by this paper.
Showing 1-10 of 25 references

Dynamic portfolio selection in arbitrage

J. Jurek, H. Yang
Technical report, • 2006
View 4 Excerpts
Highly Influenced

Optimal hedging using cointegration’, Philosophical Transactions: Mathematical

C. Alexander
Physical and Engineering Sciences • 1999
View 6 Excerpts
Highly Influenced

A canonical analysis of multiple time series

G. E. Box, G. C. Tiao
Biometrika • 1977
View 2 Excerpts
Highly Influenced

Lasso - type recovery of spare representations for highdimensional data , Technical report , To appear in Annals of Statistics

N. Meinshausen, B. Yu

Similar Papers

Loading similar papers…