Portfolio optimization with linear and fixed transaction costs

By Miguel Sousa Lobo, Maryam Fazel, Stephen P. Boyd. Annals of Operations Research, 2007

397 citations recorded by the source corpus.

Subfields: Finance, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/adcdd11c15ab6bed838b15dfc5061a6b95d707d6

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Finance

In Management Science and Operations Research

Citations in the corpus

An Interior-Point Method for Large-Scale l1-Regularized Logistic Regression (2007, background): “The idea also comes up in signal processing (basis pursuit [11, 12] and wavelet thresholding [19]), portfolio optimization [34], control design [26], computer-aided design of integrated circuits [9, 56], and machine learning (sparse principal component analysis [15], maximum likelihood estimation of”
Design of Optimal Sparse Feedback Gains via the Alternating Direction Method of Multipliers (2011, methodology): “A reweighted algorithm that solves a sequence of weighted 1 optimization problems in which the weights are determined by the solution of the weighted 1 problem in the previous iteration was proposed in [22], [28].”
Synthesis of Sparse Arrays With Focused or Shaped Beampattern via Sequential Convex Optimizations (2012, methodology): “For the norm, larger coefficients are indeed penalized more heavily than smaller coefficients, whereas the norm penalizes uniformly all non-zero coefficients.”
Minimization Over the Nonconvex Sparsity Constraint Using A Hybrid First-order method (2021, background): “Log [33] =1 log(1 + κ | x i | ) Geman [22] =1 Arctan[11] Motivated by the success of nonconvex ℓ p norm, p ∈ (0 , 1), in pursuing sparsity [14, 47].”
Guaranteed Minimum-Rank Solutions of Linear Matrix Equations via Nuclear Norm Minimization (2007, methodology): “Since then, ‘1 minimization has been applied to a variety of cardinality minimization problems including image denoising [65], model selection in statistics [76], sparse approximation [20], portfolio optimization with xed transaction costs [ 49 ], design of sparse interconnect wiring in circuits [80”
Subjective measures of risk aversion, fixed costs, and portfolio choice (2011, neutral): “More recently, Lobo et al. (2007) develop a relaxation method yielding a computable upper bound via convex optimization when separable fixed transaction costs are considered.”
Beampattern Synthesis for Linear and Planar Arrays With Antenna Selection by Convex Optimization (2010, neutral): “8The algorithm is used for sparse signal recovery [20] and for portfolio optimization with real-valued variables [21].”
Sensor Selection via Convex Optimization (2009, background): “Other applications that use convex relaxations include portfolio optimization with transaction costs [35], controller design [36], and circuit design [37].”
1 Trend Filtering (2009, methodology): “The idea of 1 regularization has been used in other contexts, including portfolio optimization [69], control design [48], computer-aided design of integrated circuits [13], decoding of linear codes [15], and machine learning (sparse principal component analysis [25] and graphical model selection [2,”
Iterative reweighted algorithms for matrix rank minimization (2012, methodology): “It has also been observed empirically (e.g., Candes et al., 2008; Lobo et al., 2006) that by appropriately weighting the ℓ 1 norm and iteratively updating the weights, the recovery performance of the algorithm is enhanced.”