2,282 research outputs found
Clustering stock exchange data by using evolutionary algorithms for portfolio management
In present paper, imperialist competitive algorithm and ant colony algorithm and particle swarm optimization algorithm have been used to cluster stocks of Tehran stock exchange. Also results of the three algorithms have been compared with three famous clustering models so called k-means, Fcm and Som. After clustering, a portfolio has been made by choosing some stocks from each cluster and using NSGA-II algorithm. Results show superiority of ant colony algorithms and particle swarm optimization algorithm and imperialist competitive to other three methods for clustering stocks. Due to diversification of the portfolio, portfolio risk will be reduced while using data chosen from the clusters. The more efficient the clustering, the lower the risk is. Also, using clustering for portfolio management reduces time of portfolio selection.peer-reviewe
A survey on financial applications of metaheuristics
Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness
(TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program
(E-2015-36)
Algorithm Portfolio for Individual-based Surrogate-Assisted Evolutionary Algorithms
Surrogate-assisted evolutionary algorithms (SAEAs) are powerful optimisation
tools for computationally expensive problems (CEPs). However, a randomly
selected algorithm may fail in solving unknown problems due to no free lunch
theorems, and it will cause more computational resource if we re-run the
algorithm or try other algorithms to get a much solution, which is more serious
in CEPs. In this paper, we consider an algorithm portfolio for SAEAs to reduce
the risk of choosing an inappropriate algorithm for CEPs. We propose two
portfolio frameworks for very expensive problems in which the maximal number of
fitness evaluations is only 5 times of the problem's dimension. One framework
named Par-IBSAEA runs all algorithm candidates in parallel and a more
sophisticated framework named UCB-IBSAEA employs the Upper Confidence Bound
(UCB) policy from reinforcement learning to help select the most appropriate
algorithm at each iteration. An effective reward definition is proposed for the
UCB policy. We consider three state-of-the-art individual-based SAEAs on
different problems and compare them to the portfolios built from their
instances on several benchmark problems given limited computation budgets. Our
experimental studies demonstrate that our proposed portfolio frameworks
significantly outperform any single algorithm on the set of benchmark problems
A similarity-based cooperative co-evolutionary algorithm for dynamic interval multi-objective optimization problems
The file attached to this record is the author's final peer reviewed version. The Publisher's final version can be found by following the DOI link.Dynamic interval multi-objective optimization problems (DI-MOPs) are very common in real-world applications. However, there are few evolutionary algorithms that are suitable for tackling DI-MOPs up to date. A framework of dynamic interval multi-objective cooperative co-evolutionary optimization based on the interval similarity is presented in this paper to handle DI-MOPs. In the framework, a strategy for decomposing decision variables is first proposed, through which all the decision variables are divided into two groups according to the interval similarity between each decision variable and interval parameters. Following that, two sub-populations are utilized to cooperatively optimize decision variables in the two groups. Furthermore, two response strategies, rgb0.00,0.00,0.00i.e., a strategy based on the change intensity and a random mutation strategy, are employed to rapidly track the changing Pareto front of the optimization problem. The proposed algorithm is applied to eight benchmark optimization instances rgb0.00,0.00,0.00as well as a multi-period portfolio selection problem and compared with five state-of-the-art evolutionary algorithms. The experimental results reveal that the proposed algorithm is very competitive on most optimization instances
A long-term swarm intelligence hedging tool applied to electricity markets
This paper proposes a swarm intelligence long-term hedging tool to support electricity producers in competitive electricity markets. This tool investigates the long-term hedging opportunities available to electric power producers through the use of contracts with physical (spot and forward) and financial (options) settlement. To find the optimal portfolio the producer risk preference is stated by a utility function (U) expressing the trade-off between the expectation and the variance of the return. Variance estimation and the expected return are based on a forecasted scenario interval determined by a long-term price range forecast model, developed by the authors, whose explanation is outside the scope of this paper. The proposed tool makes use of Particle Swarm Optimization (PSO) and its performance has been evaluated by comparing it with a Genetic Algorithm (GA) based approach. To validate the risk management tool a case study, using real price historical data for mainland Spanish market, is presented to demonstrate the effectiveness of the proposed methodology
Trading Strategies: Earning More in Investment
Gold and bitcoin are not new to us, but with limited cash and time, given
only the past stream of the daily price of gold and bitcoin, it is a kind of
new problem for us to develop a certain model and determine the best strategy
to get the most return. Here, our team members analyzed the data provided and
finally made a unified system of models to predict the price and evaluate the
risk and return in our act of investment, and we name this series of models and
measurements as CTP Model. This is a model which can determine and describe
what transaction should the trader make each day and what is the certain
maximum return he will get under different risk levels
Hybrid approach based on particle swarm optimization for electricity markets participation
In many large-scale and time-consuming problems, the application of metaheuristics becomes essential, since these methods enable achieving very close solutions to the exact one in a much shorter time. In this work, we address the problem of portfolio optimization applied to electricity markets negotiation. As in a market environment, decision-making is carried out in very short times, the application of the metaheuristics is necessary. This work proposes a Hybrid model, combining a simplified exact resolution of the method, as a means to obtain the initial solution for a Particle Swarm Optimization (PSO) approach. Results show that the presented approach is able to obtain better results in the metaheuristic search process.This work has received funding from the European Union's Horizon 2020 research and innovation programme under project DOMINOES (grant agreement No 771066) and from FEDER Funds through COMPETE program and from National Funds through FCT under the project UID/EEA/00760/2019 and Ricardo Faia is supported by FCT Funds through and SFRH/BD/133086/2017 PhD scholarship.info:eu-repo/semantics/publishedVersio
- …