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

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 similarity measure for the cardinality constrained frontier in the mean-variance optimization model

[EN] This paper proposes a new measure to find the cardinality constrained frontier in the meanvariance portfolio optimization problem. In previous research, assets belonging to the cardinality constrained portfolio change according to the desired level of expected return, so that the cardinality constraint can actually be violated if the fund manager wants to satisfy clients with different return requirements. We introduce a perceptual approach in the meanvariance cardinality constrained portfolio optimization problem by considering a novel similarity measure, which compares the cardinality constrained frontier with the unconstrained mean-variance frontier. We assume that the closer the cardinality constrained frontier to the mean-variance frontier, the more appealing it is for the decision maker. This makes the assets included in the portfolio invariant to any specific level of return, through focusing not on the optimal portfolio but on the optimal frontier.Guijarro, F. (2018). A similarity measure for the cardinality constrained frontier in the mean-variance optimization model. Journal of the Operational Research Society. 69(6):928-945. doi:10.1057/s41274-017-0276-6S92894569

Portfolio implementation risk management using evolutionary multiobjective optimization

Portfoliomanagementbasedonmean-varianceportfoliooptimizationissubjecttodifferent sources of uncertainty. In addition to those related to the quality of parameter estimates used in the optimization process, investors face a portfolio implementation risk. The potential temporary discrepancybetweentargetandpresentportfolios,causedbytradingstrategies,mayexposeinvestors to undesired risks. This study proposes an evolutionary multiobjective optimization algorithm aiming at regions with solutions more tolerant to these deviations and, therefore, more reliable. The proposed approach incorporates a user’s preference and seeks a fine-grained approximation of the most relevant efficient region. The computational experiments performed in this study are based on a cardinality-constrained problem with investment limits for eight broad-category indexes and 15 years of data. The obtained results show the ability of the proposed approach to address the robustness issue and to support decision making by providing a preferred part of the efficient set. The results reveal that the obtained solutions also exhibit a higher tolerance to prediction errors in asset returns and variance–covariance matrix.Sandra Garcia-Rodriguez and David Quintana acknowledge financial support granted by the Spanish Ministry of Economy and Competitivity under grant ENE2014-56126-C2-2-R. Roman Denysiuk and Antonio Gaspar-Cunha were supported by the Portuguese Foundation for Science and Technology under grant PEst-C/CTM/LA0025/2013 (Projecto Estratégico-LA 25-2013-2014-Strategic Project-LA 25-2013-2014).info:eu-repo/semantics/publishedVersio

Evolutionary approaches for portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. Markowitz’s mean variance (MV) model is widely regarded as the foundation of modern portfolio theory and provides a quantitative framework for portfolio optimization problems. In real market, investors commonly face real-world trading restrictions and it requires that the constructed portfolios have to meet trading constraints. When additional constraints are added to the basic MV model, the problem thus becomes more complex and the exact optimization approaches run into difficulties to deliver solutions within reasonable time for large problem size. By introducing the cardinality constraint alone already transformed the classic quadratic optimization model into a mixed-integer quadratic programming problem which is an NP-hard problem. Evolutionary algorithms, a class of metaheuristics, are one of the known alternatives for optimization problems that are too complex to be solved using deterministic techniques. This thesis focuses on single-period portfolio optimization problems with practical trading constraints and two different risk measures. Four hybrid evolutionary algorithms are presented to efficiently solve these problems with gradually more complex real world constraints. In the first part of the thesis, the mean variance portfolio model is investigated by taking into account real-world constraints. A hybrid evolutionary algorithm (PBILDE) for portfolio optimization with cardinality and quantity constraints is presented. The proposed PBILDE is able to achieve a strong synergetic effect through hybridization of PBIL and DE. A partially guided mutation and an elitist update strategy are proposed in order to promote the efficient convergence of PBILDE. Its effectiveness is evaluated and compared with other existing algorithms over a number of datasets. A multi-objective scatter search with archive (MOSSwA) algorithm for portfolio optimization with cardinality, quantity and pre-assignment constraints is then presented. New subset generations and solution combination methods are proposed to generate efficient and diverse portfolios. A learning-guided multi-objective evolutionary (MODEwAwL) algorithm for the portfolio optimization problems with cardinality, quantity, pre-assignment and round lot constraints is presented. A learning mechanism is introduced in order to extract important features from the set of elite solutions. Problem-specific selection heuristics are introduced in order to identify high-quality solutions with a reduced computational cost. An efficient and effective candidate generation scheme utilizing a learning mechanism, problem specific heuristics and effective direction-based search methods is proposed to guide the search towards the promising regions of the search space. In the second part of the thesis, an alternative risk measure, VaR, is considered. A non-parametric mean-VaR model with six practical trading constraints is investigated. A multi-objective evolutionary algorithm with guided learning (MODE-GL) is presented for the mean-VaR model. Two different variants of DE mutation schemes in the solution generation scheme are proposed in order to promote the exploration of the search towards the least crowded region of the solution space. Experimental results using historical daily financial market data from S &P 100 and S & P 500 indices are presented. When the cardinality constraints are considered, incorporating a learning mechanism significantly promotes the efficient convergence of the search

The financial crisis impact on the composition of an optimal portfolio in the stock market: study applied to portuguese index PSI 20

In order to maximize their utility function, investors select some assets over others, choosing the portfolio that will allow them to maximize their wealth. Each asset is chosen considering the relationship between the risk of that particular investment (usually measured by variance) - and the profitability it can offer, as well as the risk between this and other assets (measured by covariance). The purpose of this study consisted of constructing the minimum variance portfolio, using data from the PSI-20 (2008-2016) representative asset quotation, where investors are risk reluctant and wish to minimize risk while maintaining the same level of profitability, or on the other hand, maintaining the same level of risk but maximizing expected profit. In order to do this, a comparison of the optimal portfolio in 2004-2017 was carried out, compared to the minimum variance portfolio after the financial crisis (2008-2016). The method used to estimate each asset’s expected profitability that makes up the PSI-20 consists of extracting the obtained historical quotations. The optimal portfolio composition, in the period after the financial crisis, shows that the energy sector has an optimal portfolio weight reduction of 39.15%, that the big distribution sector (23.85%) was introduced into the portfolio and by last, the industrial sector stands its ground in the composition of the optimal portfolio.info:eu-repo/semantics/publishedVersio

The Financial Crisis Impact on the Composition of an Optimal Portfolio in the Stock Market - Study Applied to Portuguese Index PSI 20

In order to maximize their utility function, investors select some assets over others, choosing the portfolio that will allow them to maximize their wealth. Each asset is chosen considering the relationship between the risk of that particular investment (usually measured by variance) - and the profitability it can offer, as well as the risk between this and other assets (measured by covariance). The purpose of this study consisted of constructing the minimum variance portfolio, using data from the PSI-20 (2008-2016) representative asset quotation, where investors are risk reluctant and wish to minimize risk while maintaining the same level of profitability, or on the other hand, maintaining the same level of risk but maximizing expected profit. In order to do this, a comparison of the optimal portfolio in 2004-2017 was carried out, compared to the minimum variance portfolio after the financial crisis (2008-2016). The method used to estimate each asset’s expected profitability that makes up the PSI-20 consists of extracting the obtained historical quotations. The optimal portfolio composition, in the period after the financial crisis, shows that the energy sector has an optimal portfolio weight reduction of 39.15%, that the big distribution sector (23.85%) was introduced into the portfolio and by last, the industrial sector stands its ground in the composition of the optimal portfolio.info:eu-repo/semantics/publishedVersio

Evolutionary approaches for portfolio optimization

Portfolio optimization involves the optimal assignment of limited capital to different available financial assets to achieve a reasonable trade-off between profit and risk objectives. Markowitz’s mean variance (MV) model is widely regarded as the foundation of modern portfolio theory and provides a quantitative framework for portfolio optimization problems. In real market, investors commonly face real-world trading restrictions and it requires that the constructed portfolios have to meet trading constraints. When additional constraints are added to the basic MV model, the problem thus becomes more complex and the exact optimization approaches run into difficulties to deliver solutions within reasonable time for large problem size. By introducing the cardinality constraint alone already transformed the classic quadratic optimization model into a mixed-integer quadratic programming problem which is an NP-hard problem. Evolutionary algorithms, a class of metaheuristics, are one of the known alternatives for optimization problems that are too complex to be solved using deterministic techniques. This thesis focuses on single-period portfolio optimization problems with practical trading constraints and two different risk measures. Four hybrid evolutionary algorithms are presented to efficiently solve these problems with gradually more complex real world constraints. In the first part of the thesis, the mean variance portfolio model is investigated by taking into account real-world constraints. A hybrid evolutionary algorithm (PBILDE) for portfolio optimization with cardinality and quantity constraints is presented. The proposed PBILDE is able to achieve a strong synergetic effect through hybridization of PBIL and DE. A partially guided mutation and an elitist update strategy are proposed in order to promote the efficient convergence of PBILDE. Its effectiveness is evaluated and compared with other existing algorithms over a number of datasets. A multi-objective scatter search with archive (MOSSwA) algorithm for portfolio optimization with cardinality, quantity and pre-assignment constraints is then presented. New subset generations and solution combination methods are proposed to generate efficient and diverse portfolios. A learning-guided multi-objective evolutionary (MODEwAwL) algorithm for the portfolio optimization problems with cardinality, quantity, pre-assignment and round lot constraints is presented. A learning mechanism is introduced in order to extract important features from the set of elite solutions. Problem-specific selection heuristics are introduced in order to identify high-quality solutions with a reduced computational cost. An efficient and effective candidate generation scheme utilizing a learning mechanism, problem specific heuristics and effective direction-based search methods is proposed to guide the search towards the promising regions of the search space. In the second part of the thesis, an alternative risk measure, VaR, is considered. A non-parametric mean-VaR model with six practical trading constraints is investigated. A multi-objective evolutionary algorithm with guided learning (MODE-GL) is presented for the mean-VaR model. Two different variants of DE mutation schemes in the solution generation scheme are proposed in order to promote the exploration of the search towards the least crowded region of the solution space. Experimental results using historical daily financial market data from S &P 100 and S & P 500 indices are presented. When the cardinality constraints are considered, incorporating a learning mechanism significantly promotes the efficient convergence of the search

A Network Model of Financial Markets

This thesis introduces a network representation of equity markets.The model is based on the premise that assets share dependencies on abstract ‘factors’ resulting in exploitable patterns among asset price levels.The network model is a collection of long-run market trends estimated by a 3 layer machine learning framework.The network model’s comprehensive validity is established with 2 simulations in the fields of algorithmic trading, and systemic risk.The algorithmic trading validation applies expectations derived from the network model to estimating expected future returns. It further utilizes the network’s expectations to actively manage a theoretically market neutral portfolio.The validation demonstrates that the network model’s portfolio generates excess returns relative to 2 benchmarks. Over the time period of April, 2007 to January, 2014 the network model’s portfolio for assets drawn from the S&P/ASX 100 produced a Sharpe ratio of 0.674.This approximately doubles the nearest benchmark. The systemic risk validation utilized the network model to simulate shocks to select market sectors and evaluate the resulting financial contagion.The validation successfully differentiated sectors by systemic connectivity levels and suggested some interesting market features. Most notable was the identification of the ‘Financials’ sector as most systemically influential and ‘Basic Materials’ as the most systemically dependent. Additionally, there was evidence that ‘Financials’ may function as a hub of systemic risk which exacerbates losses from multiple market sectors