Heuristics for cardinality constrained portfolio optimisation

By T.-J. Chang, Nigel Meade, John E. Beasley, Yazid M. Sharaiha. Computers & Operations Research, 2000

945 citations recorded by the source corpus.

Subfields: Economics and Econometrics, Management Science and Operations Research, Ocean Engineering

View Paper: https://www.semanticscholar.org/paper/9a6e16a993307452b2ca7e07cb50844004aa8f20

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Citations in the corpus

Recent Advances in Mathematical Programming with Semi-continuous Variables and Cardinality Constraint (2013, background): “The model (MV) has been also investigated in finance literature in the context of limiteddiversification, small portfolios and portfolio selection models with real features (see, e.g., [8, 33, 34, 42, 46]).”
Simulated annealing for complex portfolio selection problems (2003, background): “This allows us to avoid some of the difficulties linked to the fact that, as explained in [5], the efficient frontier cannot possibly be mapped entirely in the Lagrangian approach, due to its discontinuity.”
Portfolio selection using neural networks (2005, methodology): “In particular, the general efficient frontier may be discontinuous [2,11].”
A Survey on Multiobjective Evolutionary Algorithms for the Solution of the Portfolio Optimization Problem and Other Finance and Economics Applications (2013, background): “[27]–[29], [27], [28], [31], [27], [33], Mean-variance [50], [51] [31], [33]–[36], [35], [38], [55] [33], [35], [36], [38], [42], [29], [34] [55] [38], [39], [61] [44] Downside risk/semivariance [26], [45], [46] [30] [30] VaR [47]–[49] [29], [31] [31] [29] Surplus variance [48] Expected shortfall [2”
Algorithm for cardinality-constrained quadratic optimization (2009, background): “For example, [17] proposes a linear mixed-integer optimization based heuristic, [5] introduces a dynamic programming based heuristic, and [6] proposes genetic algorithm, tabu search and simulated annealing approaches for the problem.”
An MCDM approach to portfolio optimization (2004, methodology): “Metaheuristics have also been used in Chang et al. (2000) to generate the efficient frontier of cardinality constrained portfolio optimization problems (in fact, this study also considered a tabu search, a simulated annealing, and a genetic algorithm implementation).”
Applications of multi-objective evolutionary algorithms in economics and finance: A survey (2007, background): “[15] use a GA with a linear aggregating function that considers the same two objectives as before: minimize the total variance (risk) associated with the portfolio and ensure that the portfolio has a certain expected return.”