scholarly journals Sparse Portfolio Selection Via the Sorted L1 - Norm

2017 ◽  
Author(s):  
Philipp J. Kremer ◽  
Sangkyun Lee ◽  
Malgorzata Bogdan ◽  
Sandra Paterlini
Keyword(s):  
Portfolio Selection ◽  
L1 Norm ◽  
Sparse Portfolio

A penalty PALM method for sparse portfolio selection problems

2016 ◽  
Vol 32 (1) ◽  
pp. 126-147 ◽  
Author(s):  
Yue Teng ◽  
Li Yang ◽  
Bo Yu ◽  
Xiaoliang Song
Keyword(s):  
Portfolio Selection ◽  
Selection Problems ◽  
Sparse Portfolio

scholarly journals A Scalable Algorithm for Sparse Portfolio Selection

2022 ◽  
Author(s):  
Dimitris Bertsimas ◽  
Ryan Cory-Wright
Keyword(s):  
Portfolio Selection ◽  
Minimum Variance ◽  
Outer Approximation ◽  
Approximation Procedure ◽  
Expected Return ◽  
Risky Assets ◽  
Scalable Algorithm ◽  
Second Order Cone ◽  
Real World Problem ◽  
Sparse Portfolio

The sparse portfolio selection problem is one of the most famous and frequently studied problems in the optimization and financial economics literatures. In a universe of risky assets, the goal is to construct a portfolio with maximal expected return and minimum variance, subject to an upper bound on the number of positions, linear inequalities, and minimum investment constraints. Existing certifiably optimal approaches to this problem have not been shown to converge within a practical amount of time at real-world problem sizes with more than 400 securities. In this paper, we propose a more scalable approach. By imposing a ridge regularization term, we reformulate the problem as a convex binary optimization problem, which is solvable via an efficient outer-approximation procedure. We propose various techniques for improving the performance of the procedure, including a heuristic that supplies high-quality warm-starts, and a second heuristic for generating additional cuts that strengthens the root relaxation. We also study the problem’s continuous relaxation, establish that it is second-order cone representable, and supply a sufficient condition for its tightness. In numerical experiments, we establish that a conjunction of the imposition of ridge regularization and the use of the outer-approximation procedure gives rise to dramatic speedups for sparse portfolio selection problems.

Sparse portfolio selection with uncertain probability distribution

2021 ◽  
Author(s):  
Ripeng Huang ◽  
Shaojian Qu ◽  
Xiaoguang Yang ◽  
Fengmin Xu ◽  
Zeshui Xu ◽  
...  
Keyword(s):  
Probability Distribution ◽  
Portfolio Selection ◽  
Sparse Portfolio

scholarly journals Sparse Portfolio Selection via Bayesian Multiple Testing

Sankhya B ◽  
2020 ◽  
Author(s):  
Sourish Das ◽  
Rituparna Sen
Keyword(s):  
Portfolio Selection ◽  
Multiple Testing ◽  
Sparse Portfolio

Fast algorithms for sparse portfolio selection considering industries and investment styles

2020 ◽  
Vol 78 (4) ◽  
pp. 763-789 ◽  
Author(s):  
Zhi-Long Dong ◽  
Fengmin Xu ◽  
Yu-Hong Dai
Keyword(s):  
Portfolio Selection ◽  
Fast Algorithms ◽  
Sparse Portfolio ◽  
Investment Styles

Dynamic multi-period sparse portfolio selection model with asymmetric investors’ sentiments

2021 ◽  
pp. 114945
Author(s):  
Ju Wei ◽  
Yongxin Yang ◽  
Mingzhu Jiang ◽  
JianGuo Liu
Keyword(s):  
Portfolio Selection ◽  
Selection Model ◽  
Sparse Portfolio ◽  
Portfolio Selection Model

scholarly journals Sparse portfolio selection via the sorted ℓ1-Norm

2020 ◽  
Vol 110 ◽  
pp. 105687 ◽  
Author(s):  
Philipp J. Kremer ◽  
Sangkyun Lee ◽  
Małgorzata Bogdan ◽  
Sandra Paterlini
Keyword(s):  
Portfolio Selection ◽  
Sparse Portfolio

scholarly journals A concave optimization-based approach for sparse portfolio selection

2012 ◽  
Vol 27 (6) ◽  
pp. 983-1000 ◽  
Author(s):  
D. Di Lorenzo ◽  
G. Liuzzi ◽  
F. Rinaldi ◽  
F. Schoen ◽  
M. Sciandrone
Keyword(s):  
Portfolio Selection ◽  
Sparse Portfolio

A portfolio selection framework for choice under conflict

1995 ◽  
Author(s):  
Chen-Fu Chien ◽  
Francois Sainfort
Keyword(s):  
Portfolio Selection ◽  
Selection Framework
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