804 research outputs found
A note on evolutionary stochastic portfolio optimization and probabilistic constraints
In this note, we extend an evolutionary stochastic portfolio optimization
framework to include probabilistic constraints. Both the stochastic
programming-based modeling environment as well as the evolutionary optimization
environment are ideally suited for an integration of various types of
probabilistic constraints. We show an approach on how to integrate these
constraints. Numerical results using recent financial data substantiate the
applicability of the presented approach
Portfolio selection problems in practice: a comparison between linear and quadratic optimization models
Several portfolio selection models take into account practical limitations on
the number of assets to include and on their weights in the portfolio. We
present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset
Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional
Value-at-Risk (LACVaR) models, where the assets are limited with the
introduction of quantity and cardinality constraints. We propose a completely
new approach for solving the LAM model, based on reformulation as a Standard
Quadratic Program and on some recent theoretical results. With this approach we
obtain optimal solutions both for some well-known financial data sets used by
several other authors, and for some unsolved large size portfolio problems. We
also test our method on five new data sets involving real-world capital market
indices from major stock markets. Our computational experience shows that,
rather unexpectedly, it is easier to solve the quadratic LAM model with our
algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of
the best commercial codes for mixed integer linear programming (MILP) problems.
Finally, on the new data sets we have also compared, using out-of-sample
analysis, the performance of the portfolios obtained by the Limited Asset
models with the performance provided by the unconstrained models and with that
of the official capital market indices
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A survey on portfolio optimisation with metaheuristics.
A portfolio optimisation problem involves allocation
of investment to a number of different assets to maximize return
and minimize risk in a given investment period. The selected
assets in a portfolio not only collectively contribute to its return
but also interactively define its risk as usually measured by a
portfolio variance. This presents a combinatorial optimisation
problem that involves selection of both a number of assets as well
as its quantity (weight or proportion or units). The problem is
extremely complex due to a large number of selectable assets.
Furthermore, the problem is dynamic and stochastic in nature
with a number of constraints presenting a complex model which is
difficult to solve for exact solution. In the last decade research
publications have reported the applications of
metaheuristic-based optimisation methods with some success.,
This paper presents a review of these reported models,
optimisation problem formulations and metaheuristic approaches
for portfolio optimisation
Portfolio selection problems in practice: a comparison between linear and quadratic optimization models
Several portfolio selection models take into account practical limitations on the number of assets to include and on their weights in the portfolio. We present here a study of the Limited Asset Markowitz (LAM), of the Limited Asset Mean Absolute Deviation (LAMAD) and of the Limited Asset Conditional Value-at-Risk (LACVaR) models, where the assets are limited with the introduction of quantity and cardinality constraints. We propose a completely new approach for solving the LAM model, based on reformulation as a Standard Quadratic Program and on some recent theoretical results. With this approach we obtain optimal solutions both for some well-known financial data sets used by several other authors, and for some unsolved large size portfolio problems. We also test our method on five new data sets involving real-world capital market indices from major stock markets. Our computational experience shows that, rather unexpectedly, it is easier to solve the quadratic LAM model with our algorithm, than to solve the linear LACVaR and LAMAD models with CPLEX, one of the best commercial codes for mixed integer linear programming (MILP) problems. Finally, on the new data sets we have also compared, using out-of-sample analysis, the performance of the portfolios obtained by the Limited Asset models with the performance provided by the unconstrained models and with that of the official capital market indices
An Evolutionary Optimization Approach to Risk Parity Portfolio Selection
In this paper we present an evolutionary optimization approach to solve the
risk parity portfolio selection problem. While there exist convex optimization
approaches to solve this problem when long-only portfolios are considered, the
optimization problem becomes non-trivial in the long-short case. To solve this
problem, we propose a genetic algorithm as well as a local search heuristic.
This algorithmic framework is able to compute solutions successfully. Numerical
results using real-world data substantiate the practicability of the approach
presented in this paper
Stock Market Portfolio Management: A Walk-through
Stock market portfolio management has remained successful in drawing attention of number of researchers from the fields of computer science, finance and mathematics all around the world since years. Successfully managing stock market portfolio is the prime concern for investors and fund managers in the financial markets. This paper is aimed to provide a walk-through to the stock market portfolio management. This paper deals with questions like what is stock market portfolio, how to manage it, what are the objectives behind managing it, what are the challenges in managing it. As each coin has two sides, each portfolio has two elements – risk and return. Regarding this, Markowitz’s Modern Portfolio Theory, or Risk-Return Model, to manage portfolio is analyzed in detail along with its criticisms, efficient frontier, and suggested state-of-the-art enhancements in terms of various constraints and risk measures. This paper also discusses other models to manage stock market portfolio such as Capital Asset Pricing Model (CAPM) and Arbitrage Pricing Theory (APT) Model.
DOI: 10.17762/ijritcc2321-8169.150613
Time-limited Metaheuristics for Cardinality-constrained Portfolio Optimisation
A financial portfolio contains assets that offer a return with a certain
level of risk. To maximise returns or minimise risk, the portfolio must be
optimised - the ideal combination of optimal quantities of assets must be
found. The number of possible combinations is vast. Furthermore, to make the
problem realistic, constraints can be imposed on the number of assets held in
the portfolio and the maximum proportion of the portfolio that can be allocated
to an asset. This problem is unsolvable using quadratic programming, which
means that the optimal solution cannot be calculated. A group of algorithms,
called metaheuristics, can find near-optimal solutions in a practical computing
time. These algorithms have been successfully used in constrained portfolio
optimisation. However, in past studies the computation time of metaheuristics
is not limited, which means that the results differ in both quality and
computation time, and cannot be easily compared. This study proposes a
different way of testing metaheuristics, limiting their computation time to a
certain duration, yielding results that differ only in quality. Given that in
some use cases the priority is the quality of the solution and in others the
speed, time limits of 1, 5 and 25 seconds were tested. Three metaheuristics -
simulated annealing, tabu search, and genetic algorithm - were evaluated on
five sets of historical market data with different numbers of assets. Although
the metaheuristics could not find a competitive solution in 1 second, simulated
annealing found a near-optimal solution in 5 seconds in all but one dataset.
The lowest quality solutions were obtained by genetic algorithm.Comment: 51 pages, 8 tables, 3 figure
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