Convex optimization approaches to maximally predictable portfolio selection

DOI10.1080/02331934.2012.741237zbMath1306.90117OpenAlexW2132237858MaRDI QIDQ2926485

Katsuki Fujisawa, Jun-Ya Gotoh

Publication date: 24 October 2014

Published in: Optimization (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1080/02331934.2012.741237

zbMATH Keywords

semidefinite programming relaxationnonlinear fractional programmaximal predictability portfolionormalized linearization algorithm

Mathematics Subject Classification ID

Semidefinite programming (90C22) Applications of mathematical programming (90C90) Nonconvex programming, global optimization (90C26) Fractional programming (90C32) Portfolio theory (91G10)

Cites Work

• An efficient algorithm for solving convex-convex quadratic fractional programs
• Decomposition branch and bound method for globally solving linearly constrained indefinite quadratic minimization problems
• Minimization of the ratio of functions defined as sums of the absolute values
• A maximal predictability portfolio using absolute deviation reformulation
• Lectures on Modern Convex Optimization
• A MAXIMAL PREDICTABILITY PORTFOLIO MODEL: ALGORITHM AND PERFORMANCE EVALUATION
• A MAXIMAL PREDICTABILITY PORTFOLIO USING DYNAMIC FACTOR SELECTION STRATEGY
• MAXIMIZING PREDICTABILITY IN THE STOCK AND BOND MARKETS
• Common risk factors in the returns on stocks and bonds
• On Nonlinear Fractional Programming
• Maximization of the ratio of two convex quadratic functions over a polytope

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