Multiple Optimal Solutions in the Portfolio Selection Model with Short-Selling

2003 ◽  
Vol 06 (07) ◽  
pp. 703-720
Author(s):  
A. Schianchi ◽  
L. Bongini ◽  
M. D. Esposti ◽  
C. Giardinà

In this paper an extension of the Lintner model [1] is considered: the problem of portfolio optimization is studied when short-selling is allowed through the mechanism of margin requirements. This induces a non-linear constraint on the wealth. When interest on deposited margin is present, Lintner ingeniously solved the problem by recovering the unique optimal solution of the linear model (no margin requirements). In this paper an alternative and more realistic approach is explored: the nonlinear constraint is maintained but no interest is perceived on the money deposited against short-selling. This leads to a fully non-linear problem which admits multiple and unstable solutions very different among themselves but corresponding to similar risk levels. Our analysis is built on a seminal idea by Galluccio, Bouchaud and Potters [3], who have re-stated the problem of finding solutions of the portfolio optimization problem in futures markets in terms of a spin glass problem. In order to get the best portfolio (i.e. the one lying on the efficiency frontier), we have to implement a two-step procedure. A worked example with real data is presented.

2021 ◽  
Vol 47 (3) ◽  
pp. 1236-1242
Author(s):  
Collether John

Portfolio can be defined as a collection of investments. Portfolio optimization usually is about maximizing expected return and/or minimising risk of a portfolio. The mean-variance model makes simplifying assumptions to solve portfolio optimization problem. Presence of realistic constraints leads to a significant different and complex problem. Also, the optimal solution under realistic constraints cannot always be derived from the solution for the frictionless market. The heuristic algorithms are alternative approaches to solve the extended problem. In this research, a heuristic algorithm is presented and improved for higher efficiency and speed. It is a hill climbing algorithm to tackle the extended portfolio optimization problem. The improved algorithm is Hill Climbing Simple–with Reducing Thresh-hold Percentage, named HC-S-R. It is applied in standard portfolio optimization problem and benchmarked with the quadratic programing method and the Threshold Accepting algorithm, a well-known heuristic algorithm for portfolio optimization problem. The results are also compared with its original algorithm HC-S. HC-S-R proves to be a lot faster than HC-S and TA and more effective and efficient than TA. Keywords: Portfolio optimization; Hill climbing algorithm; Threshold percentage; Reducing sequence; Threshold Acceptance algorithm


2021 ◽  
Vol 26 (2) ◽  
pp. 36
Author(s):  
Alejandro Estrada-Padilla ◽  
Daniela Lopez-Garcia ◽  
Claudia Gómez-Santillán ◽  
Héctor Joaquín Fraire-Huacuja ◽  
Laura Cruz-Reyes ◽  
...  

A common issue in the Multi-Objective Portfolio Optimization Problem (MOPOP) is the presence of uncertainty that affects individual decisions, e.g., variations on resources or benefits of projects. Fuzzy numbers are successful in dealing with imprecise numerical quantities, and they found numerous applications in optimization. However, so far, they have not been used to tackle uncertainty in MOPOP. Hence, this work proposes to tackle MOPOP’s uncertainty with a new optimization model based on fuzzy trapezoidal parameters. Additionally, it proposes three novel steady-state algorithms as the model’s solution process. One approach integrates the Fuzzy Adaptive Multi-objective Evolutionary (FAME) methodology; the other two apply the Non-Dominated Genetic Algorithm (NSGA-II) methodology. One steady-state algorithm uses the Spatial Spread Deviation as a density estimator to improve the Pareto fronts’ distribution. This research work’s final contribution is developing a new defuzzification mapping that allows measuring algorithms’ performance using widely known metrics. The results show a significant difference in performance favoring the proposed steady-state algorithm based on the FAME methodology.


2014 ◽  
Vol 09 (02) ◽  
pp. 1440001 ◽  
Author(s):  
MARC S. PAOLELLA

Simple, fast methods for modeling the portfolio distribution corresponding to a non-elliptical, leptokurtic, asymmetric, and conditionally heteroskedastic set of asset returns are entertained. Portfolio optimization via simulation is demonstrated, and its benefits are discussed. An augmented mixture of normals model is shown to be superior to both standard (no short selling) Markowitz and the equally weighted portfolio in terms of out of sample returns and Sharpe ratio performance.


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