5,740 research outputs found

    Ortalama-varyans portföy optimizasyonunda genetik algoritma uygulamaları üzerine bir literatür araştırması

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    Mean-variance portfolio optimization model, introduced by Markowitz, provides a fundamental answer to the problem of portfolio management. This model seeks an efficient frontier with the best trade-offs between two conflicting objectives of maximizing return and minimizing risk. The problem of determining an efficient frontier is known to be NP-hard. Due to the complexity of the problem, genetic algorithms have been widely employed by a growing number of researchers to solve this problem. In this study, a literature review of genetic algorithms implementations on mean-variance portfolio optimization is examined from the recent published literature. Main specifications of the problems studied and the specifications of suggested genetic algorithms have been summarized

    A hybrid approach for portfolio selection with higher-order moments: Empirical evidence from Shanghai Stock Exchange

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    Abstract(#br)Skewness and kurtosis, the third and fourth order moments, are statistics to summarize the shape of a distribution function. Recent studies show that investors would take these higher-order moments into consideration to make a profitable investment decision. Unfortunately, due to the difficulties in solving the multi-objective problem with higher-order moments, the literature on portfolio selection problem with higher-order moments is few. This paper proposes a new hybrid approach to solve the portfolio selection problem with skewness and kurtosis, which includes not only the multi-objective optimization but also the data-driven asset selection and return prediction, where the techniques of two-stage clustering, radial basis function neural network and genetic algorithm are employed. With the historical data from Shanghai stock exchange, we find that the out-of-sample performance of our model with higher-order moments is significantly better than that of traditional mean-variance model and verify the robustness of our hybrid algorithm

    An application of Markowitz theorem on Tehran Stock Exchange

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    During the past 65 years, there have been tremendous efforts on portfolio selection problem. The standard Markowitz mean–variance model to portfolio selection includes tracing out an efficient frontier, a continuous curve demonstrating the tradeoff between return and risk. This frontier can be often detected via standard quadratic programming, categorized in convex optimization. Traditional Markowitz problem has been recently extended into a new form of mixed integer nonlinear problems by considering various constraints such as cardinality constraints, industry limitation, etc. This paper proposes a mixed integer nonlinear programming to determine optimal asset allocation on Tehran Stock Exchange. The results have indicated that a petrochemical firm named Farabi has gained 44% of the portfolio followed by a drug firm named Kosar Pharmacy gaining 28%. In addition, banking sector was the third winning firm where Eghtesad Novin bank gained nearly 10% of the portfolio. Minerals and mining firms were the next sector in our portfolio where Gol Gohar Iron Ore and Tehran Cement collected 0.73% and 0.57% of the portfolio, respectively. In our survey, auto industry gained only 0.26% of the portfolio, which belonged to Saipa group

    Determination of Optimal Portfolio by Calculating Transaction Costs using Genetic Algorithms on the Jakarta Islamic Index

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    The optimal portfolio is a portfolio that can provide maximum returns at the same level of risk. In investing, the term "high return, high risk" is known, meaning that the higher the return, the higher the risk. Therefore, investors need to develop an optimal portfolio to obtain the maximum return on investment at the same level of risk. This study aims to determine the optimal formation of a stock portfolio by calculating transaction costs using the genetic algorithm method on stocks that are members of the Jakarta Islamic Index. This research uses data of daily return on stocks included in Jakarta Islamic Index from 1 August 2020-1 August 2022. The dataset consists of two variables: the date of observation and daily stock returns. The method used in this study is the minimum variance method and the genetic algorithm. Data analysis was divided into two stages: model formulation and model testing through case studies. The analysis of optimal portfolio formation using genetic algorithms shows that in terms of performance, the minimum variance portfolio is superior to the genetic algorithm portfolio, as indicated by the Sharpe ratio value. Meanwhile, the genetic algorithm portfolio is superior to the minimum variance portfolio regarding transaction costs. The genetic algorithm portfolio can provide a fairly high total return, small transaction costs, and good performance compared to the minimum portfolio. Hence, the genetic algorithm portfolio is worthy of recommendation to investors

    A survey on financial applications of metaheuristics

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    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)

    Geneettinen Algoritmi Optimaalisten Investointistrategioiden Määrittämiseen

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    Investors including banks, insurance companies and private investors are in a constant need for new investment strategies and portfolio selection methods. In this work we study the developed models, forecasting methods and portfolio management approaches. The information is used to create a decision-making system, or investment strategy, to form stock investment portfolios. The decision-making system is optimized using a genetic algorithm to find profitable low risk investment strategies. The constructed system is tested by simulating its performance with a large set of real stock market and economic data. The tests reveal that the constructed system requires a large sample of stock market and economic data before it finds well performing investment strategies. The parameters of the decision-making system converge surprisingly fast and the available computing capacity turned out to be sufficient even when a large amount of data is used in the system calibration. The model seems to find logics that govern stock market behavior. With a sufficient large amount of data for the calibration, the decision-making model finds strategies that work with regard to profit and portfolio diversification. The recommended strategies worked also outside the sample data that was used for system parameter identification (calibration). This work was done at Unisolver Ltd.Investoijat kuten pankit, vakuutusyhtiöt ja yksityissijoittajat tarvitsevat jatkuvasti uusia investointistrategioita portfolioiden määrittämiseen. Tässä työssä tutkitaan aiemmin kehitettyjä sijoitusmalleja, ennustemenetelmiä ja sijoitussalkun hallinnassa yleisesti käytettyjä lähestymistapoja. Löydettyä tietoa hyödyntäen kehitetään uusi päätöksentekomenetelmä (investointistrategia), jolla määritetään sijoitussalkun sisältö kunakin ajanhetkenä. Päätöksentekomalli optimoidaan geneettisellä algoritmilla. Tavoitteena on löytää tuottavia ja pienen riskin investointistrategioita. Kehitetyn mallin toimintaa simuloidaan suurella määrällä todellista pörssi- ja talousaineistoa. Testausvaihe osoittaakin, että päätöksentekomallin optimoinnissa tarvitaan suuri testiaineisto toimivien strategioiden löytämiseksi. Rakennetun mallin parametrit konvergoivat optimointivaiheessa nopeasti. Käytettävissä oleva laskentateho osoittautui riittäväksi niissäkin tilanteissa, joissa toisten menetelmien laskenta laajan aineiston takia hidastuu. Malli vaikuttaa löytävän logiikkaa, joka ymmärtää pörssikurssien käyttäytymistä. Riittävän suurella testiaineistolla malli löytää strategioita, joilla saavutetaan hyvä tuotto ja pieni riski. Strategiat toimivat myös mallin kalibroinnissa käytetyn aineiston ulkopuolella, tuottaen hyviä sijoitussalkkuja. Työ tehtiin Unisolver Oy:ssä

    An Evolutionary Approach to Multistage Portfolio Optimization

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    Portfolio optimization is an important problem in quantitative finance due to its application in asset management and corporate financial decision making. This involves quantitatively selecting the optimal portfolio for an investor given their asset return distribution assumptions, investment objectives and constraints. Analytical portfolio optimization methods suffer from limitations in terms of the problem specification and modelling assumptions that can be used. Therefore, a heuristic approach is taken where Monte Carlo simulations generate the investment scenarios and' a problem specific evolutionary algorithm is used to find the optimal portfolio asset allocations. Asset allocation is known to be the most important determinant of a portfolio's investment performance and also affects its risk/return characteristics. The inclusion of equity options in an equity portfolio should enable an investor to improve their efficient frontier due to options having a nonlinear payoff. Therefore, a research area of significant importance to equity investors, in which little research has been carried out, is the optimal asset allocation in equity options for an equity investor. A purpose of my thesis is to carry out an original analysis of the impact of allowing the purchase of put options and/or sale of call options for an equity investor. An investigation is also carried out into the effect ofchanging the investor's risk measure on the optimal asset allocation. A dynamic investment strategy obtained through multistage portfolio optimization has the potential to result in a superior investment strategy to that obtained from a single period portfolio optimization. Therefore, a novel analysis of the degree of the benefits of a dynamic investment strategy for an equity portfolio is performed. In particular, the ability of a dynamic investment strategy to mimic the effects ofthe inclusion ofequity options in an equity portfolio is investigated. The portfolio optimization problem is solved using evolutionary algorithms, due to their ability incorporate methods from a wide range of heuristic algorithms. Initially, it is shown how the problem specific parts ofmy evolutionary algorithm have been designed to solve my original portfolio optimization problem. Due to developments in evolutionary algorithms and the variety of design structures possible, a purpose of my thesis is to investigate the suitability of alternative algorithm design structures. A comparison is made of the performance of two existing algorithms, firstly the single objective stepping stone island model, where each island represents a different risk aversion parameter, and secondly the multi-objective Non-Dominated Sorting Genetic Algorithm2. Innovative hybrids of these algorithms which also incorporate features from multi-objective evolutionary algorithms, multiple population models and local search heuristics are then proposed. . A novel way is developed for solving the portfolio optimization by dividing my problem solution into two parts and then applying a multi-objective cooperative coevolution evolutionary algorithm. The first solution part consists of the asset allocation weights within the equity portfolio while the second solution part consists 'ofthe asset allocation weights within the equity options and the asset allocation weights between the different asset classes. An original portfolio optimization multiobjective evolutionary algorithm that uses an island model to represent different risk measures is also proposed.Imperial Users onl
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