Random matrix approach for primal-dual portfolio optimization problems
September 14, 2017 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Daichi Tada, Hisashi Yamamoto, Takashi Shinzato
arXiv ID
1709.04620
Category
q-fin.PM
Cross-listed
cond-mat.dis-nn,
cs.CE,
cs.LG,
math.OC
Citations
2
Venue
arXiv.org
Last Checked
3 months ago
Abstract
In this paper, we revisit the portfolio optimization problems of the minimization/maximization of investment risk under constraints of budget and investment concentration (primal problem) and the maximization/minimization of investment concentration under constraints of budget and investment risk (dual problem) for the case that the variances of the return rates of the assets are identical. We analyze both optimization problems by using the Lagrange multiplier method and the random matrix approach. Thereafter, we compare the results obtained from our proposed approach with the results obtained in previous work. Moreover, we use numerical experiments to validate the results obtained from the replica approach and the random matrix approach as methods for analyzing both the primal and dual portfolio optimization problems.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β q-fin.PM
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Robo-advising: Learning Investors' Risk Preferences via Portfolio Choices
R.I.P.
π»
Ghosted
Adversarial Deep Reinforcement Learning in Portfolio Management
π
π
The Cartographer
Reap the Harvest on Blockchain: A Survey of Yield Farming Protocols
π
π
The Cartographer
Model-Free Reinforcement Learning for Financial Portfolios: A Brief Survey
R.I.P.
π»
Ghosted
Deep Portfolio Theory
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Neural Architecture Search with Reinforcement Learning
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted