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optimization</title> <meta name="description" content="Search results for: portfolio optimization"> <meta name="keywords" content="portfolio optimization"> <meta name="viewport" content="width=device-width, initial-scale=1, minimum-scale=1, maximum-scale=1, user-scalable=no"> <meta charset="utf-8"> <link href="https://cdn.waset.org/favicon.ico" type="image/x-icon" rel="shortcut icon"> <link href="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/css/bootstrap.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/plugins/fontawesome/css/all.min.css" rel="stylesheet"> <link href="https://cdn.waset.org/static/css/site.css?v=150220211555" rel="stylesheet"> </head> <body> <header> <div class="container"> <nav class="navbar navbar-expand-lg navbar-light"> <a class="navbar-brand" href="https://waset.org"> <img src="https://cdn.waset.org/static/images/wasetc.png" alt="Open Science Research Excellence" title="Open Science Research Excellence" /> </a> <button class="d-block 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action="https://publications.waset.org/abstracts/search"> <div id="custom-search-input"> <div class="input-group"> <i class="fas fa-search"></i> <input type="text" class="search-query" name="q" placeholder="Author, Title, Abstract, Keywords" value="portfolio optimization"> <input type="submit" class="btn_search" value="Search"> </div> </div> </form> </div> </div> <div class="row mt-3"> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Commenced</strong> in January 2007</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Frequency:</strong> Monthly</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Edition:</strong> International</div> </div> </div> <div class="col-sm-3"> <div class="card"> <div class="card-body"><strong>Paper Count:</strong> 3460</div> </div> </div> </div> <h1 class="mt-3 mb-3 text-center" style="font-size:1.6rem;">Search results for: portfolio optimization</h1> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3460</span> A Mean–Variance–Skewness Portfolio Optimization Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is one of the most important topics in finance. This paper proposes a mean–variance–skewness (MVS) portfolio optimization model. Traditionally, the portfolio optimization problem is solved by using the mean–variance (MV) framework. In this study, we formulate the proposed model as a three-objective optimization problem, where the portfolio's expected return and skewness are maximized whereas the portfolio risk is minimized. For solving the proposed three-objective portfolio optimization model we apply an adapted version of the non-dominated sorting genetic algorithm (NSGAII). Finally, we use a real dataset from FTSE-100 for validating the proposed model. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title="evolutionary algorithms">evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=skewness" title=" skewness"> skewness</a>, <a href="https://publications.waset.org/abstracts/search?q=stock%20selection" title=" stock selection"> stock selection</a> </p> <a href="https://publications.waset.org/abstracts/102472/a-mean-variance-skewness-portfolio-optimization-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/102472.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">198</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3459</span> Mathematical Programming Models for Portfolio Optimization Problem: A Review</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mazura%20Mokhtar">Mazura Mokhtar</a>, <a href="https://publications.waset.org/abstracts/search?q=Adibah%20Shuib"> Adibah Shuib</a>, <a href="https://publications.waset.org/abstracts/search?q=Daud%20Mohamad"> Daud Mohamad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization problem has received a lot of attention from both researchers and practitioners over the last six decades. This paper provides an overview of the current state of research in portfolio optimization with the support of mathematical programming techniques. On top of that, this paper also surveys the solution algorithms for solving portfolio optimization models classifying them according to their nature in heuristic and exact methods. To serve these purposes, 40 related articles appearing in the international journal from 2003 to 2013 have been gathered and analyzed. Based on the literature review, it has been observed that stochastic programming and goal programming constitute the highest number of mathematical programming techniques employed to tackle the portfolio optimization problem. It is hoped that the paper can meet the needs of researchers and practitioners for easy references of portfolio optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title="portfolio optimization">portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=mathematical%20programming" title=" mathematical programming"> mathematical programming</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20programming" title=" multi-objective programming"> multi-objective programming</a>, <a href="https://publications.waset.org/abstracts/search?q=solution%20approaches" title=" solution approaches"> solution approaches</a> </p> <a href="https://publications.waset.org/abstracts/2654/mathematical-programming-models-for-portfolio-optimization-problem-a-review" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/2654.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">348</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3458</span> Leveraging Deep Q Networks in Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Deep Q networks (DQNs) represent a significant advancement in reinforcement learning, utilizing neural networks to approximate the optimal Q-value for guiding sequential decision processes. This paper presents a comprehensive introduction to reinforcement learning principles, delves into the mechanics of DQNs, and explores its application in portfolio optimization. By evaluating the performance of DQNs against traditional benchmark portfolios, we demonstrate its potential to enhance investment strategies. Our results underscore the advantages of DQNs in dynamically adjusting asset allocations, offering a robust portfolio management framework. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=deep%20reinforcement%20learning" title="deep reinforcement learning">deep reinforcement learning</a>, <a href="https://publications.waset.org/abstracts/search?q=deep%20Q%20networks" title=" deep Q networks"> deep Q networks</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-period%20optimization" title=" multi-period optimization"> multi-period optimization</a> </p> <a href="https://publications.waset.org/abstracts/189031/leveraging-deep-q-networks-in-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/189031.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">32</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3457</span> The Impact of Transaction Costs on Rebalancing an Investment Portfolio in Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=B.%20Marasovi%C4%87">B. Marasović</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20Pivac"> S. Pivac</a>, <a href="https://publications.waset.org/abstracts/search?q=S.%20V.%20Vukasovi%C4%87"> S. V. Vukasović</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Constructing a portfolio of investments is one of the most significant financial decisions facing individuals and institutions. In accordance with the modern portfolio theory maximization of return at minimal risk should be the investment goal of any successful investor. In addition, the costs incurred when setting up a new portfolio or rebalancing an existing portfolio must be included in any realistic analysis. In this paper rebalancing an investment portfolio in the presence of transaction costs on the Croatian capital market is analyzed. The model applied in the paper is an extension of the standard portfolio mean-variance optimization model in which transaction costs are incurred to rebalance an investment portfolio. This model allows different costs for different securities, and different costs for buying and selling. In order to find efficient portfolio, using this model, first, the solution of quadratic programming problem of similar size to the Markowitz model, and then the solution of a linear programming problem have to be found. Furthermore, in the paper the impact of transaction costs on the efficient frontier is investigated. Moreover, it is shown that global minimum variance portfolio on the efficient frontier always has the same level of the risk regardless of the amount of transaction costs. Although efficient frontier position depends of both transaction costs amount and initial portfolio it can be concluded that extreme right portfolio on the efficient frontier always contains only one stock with the highest expected return and the highest risk. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Croatian%20capital%20market" title="Croatian capital market">Croatian capital market</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz%20model" title=" Markowitz model"> Markowitz model</a>, <a href="https://publications.waset.org/abstracts/search?q=fractional%20quadratic%20programming" title=" fractional quadratic programming"> fractional quadratic programming</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=transaction%20costs" title=" transaction costs"> transaction costs</a> </p> <a href="https://publications.waset.org/abstracts/21383/the-impact-of-transaction-costs-on-rebalancing-an-investment-portfolio-in-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/21383.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">385</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3456</span> Portfolio Selection with Active Risk Monitoring</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Marc%20S.%20Paolella">Marc S. Paolella</a>, <a href="https://publications.waset.org/abstracts/search?q=Pawel%20Polak"> Pawel Polak</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The paper proposes a framework for large-scale portfolio optimization which accounts for all the major stylized facts of multivariate financial returns, including volatility clustering, dynamics in the dependency structure, asymmetry, heavy tails, and non-ellipticity. It introduces a so-called risk fear portfolio strategy which combines portfolio optimization with active risk monitoring. The former selects optimal portfolio weights. The latter, independently, initiates market exit in case of excessive risks. The strategy agrees with the stylized fact of stock market major sell-offs during the initial stage of market downturns. The advantages of the new framework are illustrated with an extensive empirical study. It leads to superior multivariate density and Value-at-Risk forecasting, and better portfolio performance. The proposed risk fear portfolio strategy outperforms various competing types of optimal portfolios, even in the presence of conservative transaction costs and frequent rebalancing. The risk monitoring of the optimal portfolio can serve as an early warning system against large market risks. In particular, the new strategy avoids all the losses during the 2008 financial crisis, and it profits from the subsequent market recovery. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=comfort" title="comfort">comfort</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20crises" title=" financial crises"> financial crises</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20monitoring" title=" risk monitoring"> risk monitoring</a> </p> <a href="https://publications.waset.org/abstracts/28504/portfolio-selection-with-active-risk-monitoring" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28504.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">524</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3455</span> Multi-Period Portfolio Optimization Using Predictive Machine Learning Models</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Peng%20Liu">Peng Liu</a>, <a href="https://publications.waset.org/abstracts/search?q=Chyng%20Wen%20Tee"> Chyng Wen Tee</a>, <a href="https://publications.waset.org/abstracts/search?q=Xiaofei%20Xu"> Xiaofei Xu</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper integrates machine learning forecasting techniques into the multi-period portfolio optimization framework, enabling dynamic asset allocation based on multiple future periods. We explore both theoretical foundations and practical applications, employing diverse machine learning models for return forecasting. This comprehensive guide demonstrates the superiority of multi-period optimization over single-period approaches, particularly in risk mitigation through strategic rebalancing and enhanced market trend forecasting. Our goal is to promote wider adoption of multi-period optimization, providing insights that can significantly enhance the decision-making capabilities of practitioners and researchers alike. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=multi-period%20portfolio%20optimization" title="multi-period portfolio optimization">multi-period portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=look-ahead%20constrained%20optimization" title=" look-ahead constrained optimization"> look-ahead constrained optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=machine%20learning" title=" machine learning"> machine learning</a>, <a href="https://publications.waset.org/abstracts/search?q=sequential%20decision%20making" title=" sequential decision making"> sequential decision making</a> </p> <a href="https://publications.waset.org/abstracts/186542/multi-period-portfolio-optimization-using-predictive-machine-learning-models" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/186542.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">48</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3454</span> Portfolio Risk Management Using Quantum Annealing</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Thomas%20Doutre">Thomas Doutre</a>, <a href="https://publications.waset.org/abstracts/search?q=Emmanuel%20De%20Meric%20De%20Bellefon"> Emmanuel De Meric De Bellefon</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper describes the application of local-search metaheuristic quantum annealing to portfolio opti- mization. Heuristic technics are particularly handy when Markowitz’ classical Mean-Variance problem is enriched with additional realistic constraints. Once tailored to the problem, computational experiments on real collected data have shown the superiority of quantum annealing over simulated annealing for this constrained optimization problem, taking advantages of quantum effects such as tunnelling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=optimization" title="optimization">optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20risk%20management" title=" portfolio risk management"> portfolio risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=quantum%20annealing" title=" quantum annealing"> quantum annealing</a>, <a href="https://publications.waset.org/abstracts/search?q=metaheuristic" title=" metaheuristic"> metaheuristic</a> </p> <a href="https://publications.waset.org/abstracts/40564/portfolio-risk-management-using-quantum-annealing" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/40564.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3453</span> Advanced Technologies and Algorithms for Efficient Portfolio Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Liagkouras">Konstantinos Liagkouras</a>, <a href="https://publications.waset.org/abstracts/search?q=Konstantinos%20Metaxiotis"> Konstantinos Metaxiotis</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper we present a classification of the various technologies applied for the solution of the portfolio selection problem according to the discipline and the methodological framework followed. We provide a concise presentation of the emerged categories and we are trying to identify which methods considered obsolete and which lie at the heart of the debate. On top of that, we provide a comparative study of the different technologies applied for efficient portfolio construction and we suggest potential paths for future work that lie at the intersection of the presented techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title="portfolio selection">portfolio selection</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization%20techniques" title=" optimization techniques"> optimization techniques</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20models" title=" financial models"> financial models</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic" title=" stochastic"> stochastic</a>, <a href="https://publications.waset.org/abstracts/search?q=heuristics" title=" heuristics"> heuristics</a> </p> <a href="https://publications.waset.org/abstracts/31917/advanced-technologies-and-algorithms-for-efficient-portfolio-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/31917.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">432</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3452</span> Dynamic Correlations and Portfolio Optimization between Islamic and Conventional Equity Indexes: A Vine Copula-Based Approach</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imen%20Dhaou">Imen Dhaou</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study examines conditional Value at Risk by applying the GJR-EVT-Copula model, and finds the optimal portfolio for eight Dow Jones Islamic-conventional pairs. Our methodology consists of modeling the data by a bivariate GJR-GARCH model in which we extract the filtered residuals and then apply the Peak over threshold model (POT) to fit the residual tails in order to model marginal distributions. After that, we use pair-copula to find the optimal portfolio risk dependence structure. Finally, with Monte Carlo simulations, we estimate the Value at Risk (VaR) and the conditional Value at Risk (CVaR). The empirical results show the VaR and CVaR values for an equally weighted portfolio of Dow Jones Islamic-conventional pairs. In sum, we found that the optimal investment focuses on Islamic-conventional US Market index pairs because of high investment proportion; however, all other index pairs have low investment proportion. These results deliver some real repercussions for portfolio managers and policymakers concerning to optimal asset allocations, portfolio risk management and the diversification advantages of these markets. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=CVaR" title="CVaR">CVaR</a>, <a href="https://publications.waset.org/abstracts/search?q=Dow%20Jones%20Islamic%20index" title=" Dow Jones Islamic index"> Dow Jones Islamic index</a>, <a href="https://publications.waset.org/abstracts/search?q=GJR-GARCH-EVT-pair%20copula" title=" GJR-GARCH-EVT-pair copula"> GJR-GARCH-EVT-pair copula</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a> </p> <a href="https://publications.waset.org/abstracts/81937/dynamic-correlations-and-portfolio-optimization-between-islamic-and-conventional-equity-indexes-a-vine-copula-based-approach" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/81937.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">256</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3451</span> Portfolio Optimization with Reward-Risk Ratio Measure Based on the Mean Absolute Deviation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Wlodzimierz%20Ogryczak">Wlodzimierz Ogryczak</a>, <a href="https://publications.waset.org/abstracts/search?q=Michal%20Przyluski"> Michal Przyluski</a>, <a href="https://publications.waset.org/abstracts/search?q=Tomasz%20Sliwinski"> Tomasz Sliwinski</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In problems of portfolio selection, the reward-risk ratio criterion is optimized to search for a risky portfolio with the maximum increase of the mean return in proportion to the risk measure increase when compared to the risk-free investments. In the classical model, following Markowitz, the risk is measured by the variance thus representing the Sharpe ratio optimization and leading to the quadratic optimization problems. Several Linear Programming (LP) computable risk measures have been introduced and applied in portfolio optimization. In particular, the Mean Absolute Deviation (MAD) measure has been widely recognized. The reward-risk ratio optimization with the MAD measure can be transformed into the LP formulation with the number of constraints proportional to the number of scenarios and the number of variables proportional to the total of the number of scenarios and the number of instruments. This may lead to the LP models with huge number of variables and constraints in the case of real-life financial decisions based on several thousands scenarios, thus decreasing their computational efficiency and making them hardly solvable by general LP tools. We show that the computational efficiency can be then dramatically improved by an alternative model based on the inverse risk-reward ratio minimization and by taking advantages of the LP duality. In the introduced LP model the number of structural constraints is proportional to the number of instruments thus not affecting seriously the simplex method efficiency by the number of scenarios and therefore guaranteeing easy solvability. Moreover, we show that under natural restriction on the target value the MAD risk-reward ratio optimization is consistent with the second order stochastic dominance rules. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title="portfolio optimization">portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=reward-risk%20ratio" title=" reward-risk ratio"> reward-risk ratio</a>, <a href="https://publications.waset.org/abstracts/search?q=mean%20absolute%20deviation" title=" mean absolute deviation"> mean absolute deviation</a>, <a href="https://publications.waset.org/abstracts/search?q=linear%20programming" title=" linear programming"> linear programming</a> </p> <a href="https://publications.waset.org/abstracts/61665/portfolio-optimization-with-reward-risk-ratio-measure-based-on-the-mean-absolute-deviation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/61665.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">406</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3450</span> Optimization of Black-Litterman Model for Portfolio Assets Allocation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=A.%20Hidalgo">A. Hidalgo</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Desportes"> A. Desportes</a>, <a href="https://publications.waset.org/abstracts/search?q=E.%20Bonin"> E. Bonin</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Kadaoui"> A. Kadaoui</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Bouaricha"> T. Bouaricha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Present paper is concerned with portfolio management with Black-Litterman (B-L) model. Considered stocks are exclusively limited to large companies stocks on US market. Results obtained by application of the model are presented. From analysis of collected Dow Jones stock data, remarkable explicit analytical expression of optimal B-L parameter τ, which scales dispersion of normal distribution of assets mean return, is proposed in terms of standard deviation of covariance matrix. Implementation has been developed in Matlab environment to split optimization in Markovitz sense from specific elements related to B-L representation. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Black-Litterman" title="Black-Litterman">Black-Litterman</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz" title=" Markowitz"> Markowitz</a>, <a href="https://publications.waset.org/abstracts/search?q=market%20data" title=" market data"> market data</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20manager%20opinion" title=" portfolio manager opinion"> portfolio manager opinion</a> </p> <a href="https://publications.waset.org/abstracts/6712/optimization-of-black-litterman-model-for-portfolio-assets-allocation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/6712.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">260</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3449</span> Mathematical Model of Corporate Bond Portfolio and Effective Border Preview</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Sergey%20Podluzhnyy">Sergey Podluzhnyy</a> </p> <p class="card-text"><strong>Abstract:</strong></p> One of the most important tasks of investment and pension fund management is building decision support system which helps to make right decision on corporate bond portfolio formation. Today there are several basic methods of bond portfolio management. They are duration management, immunization and convexity management. Identified methods have serious disadvantage: they do not take into account credit risk or insolvency risk of issuer. So, identified methods can be applied only for management and evaluation of high-quality sovereign bonds. Applying article proposes mathematical model for building an optimal in case of risk and yield corporate bond portfolio. Proposed model takes into account the default probability in formula of assessment of bonds which results to more correct evaluation of bonds prices. Moreover, applied model provides tools for visualization of the efficient frontier of corporate bonds portfolio taking into account the exposure to credit risk, which will increase the quality of the investment decisions of portfolio managers. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=corporate%20bond%20portfolio" title="corporate bond portfolio">corporate bond portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=default%20probability" title=" default probability"> default probability</a>, <a href="https://publications.waset.org/abstracts/search?q=effective%20boundary" title=" effective boundary"> effective boundary</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization%20task" title=" portfolio optimization task"> portfolio optimization task</a> </p> <a href="https://publications.waset.org/abstracts/59174/mathematical-model-of-corporate-bond-portfolio-and-effective-border-preview" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/59174.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">318</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3448</span> Smart Beta Portfolio Optimization</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Saud%20Al%20Mahdi">Saud Al Mahdi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Traditionally,portfolio managers have been discouraged from timing the market. This means, for example, that equity managers have been forced to adhere strictly to a benchmark with static or relatively stable components, such as the SP 500 or the Russell 3000. This means that the portfolio’s exposures to all risk factors should mimic as closely as possible the corresponding exposures of the benchmark. The main risk factor, of course, is the market itself. Effectively, a long-only portfolio would be constrained to have a beta 1. More recently, however, managers have been given greater discretion to adjust their portfolio’s risk exposures (in particular, the beta of their portfolio) dynamically to match the manager’s beliefs about future performance of the risk factors themselves. This freedom translates into the manager’s ability to adjust the portfolio’s beta dynamically. These strategies have come to be known as smart beta strategies. Adjusting beta dynamically amounts to attempting to "time" the market; that is, to increase exposure when one anticipates that the market will rise, and to decrease it when one anticipates that the market will fall. Traditionally, market timing has been believed to be impossible to perform effectively and consistently. Moreover, if a majority of market participants do it, their combined actions could destabilize the market. The aim of this project is to investigate so-called smart beta strategies to determine if they really can add value, or if they are merely marketing gimmicks used to sell dubious investment strategies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=beta" title="beta">beta</a>, <a href="https://publications.waset.org/abstracts/search?q=alpha" title=" alpha"> alpha</a>, <a href="https://publications.waset.org/abstracts/search?q=active%20portfolio%20management" title=" active portfolio management"> active portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=trading%20strategies" title=" trading strategies "> trading strategies </a> </p> <a href="https://publications.waset.org/abstracts/28119/smart-beta-portfolio-optimization" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/28119.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">355</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3447</span> Optimization Technique for the Contractor’s Portfolio in the Bidding Process</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Taha%20Anjamrooz">Taha Anjamrooz</a>, <a href="https://publications.waset.org/abstracts/search?q=Sareh%20Rajabi"> Sareh Rajabi</a>, <a href="https://publications.waset.org/abstracts/search?q=Salwa%20Bheiry"> Salwa Bheiry</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Selection between the available projects in bidding processes for the contractor is one of the essential areas to concentrate on. It is important for the contractor to choose the right projects within its portfolio during the tendering stage based on certain criteria. It should align the bidding process with its origination strategies and goals as a screening process to have the right portfolio pool to start with. Secondly, it should set the proper framework and use a suitable technique in order to optimize its selection process for concertation purpose and higher efforts during the tender stage with goals of success and winning. In this research paper, a two steps framework proposed to increase the efficiency of the contractor’s bidding process and the winning chance of getting the new projects awarded. In this framework, initially, all the projects pass through the first stage screening process, in which the portfolio basket will be evaluated and adjusted in accordance with the organization strategies to the reduced version of the portfolio pool, which is in line with organization activities. In the second stage, the contractor uses linear programming to optimize the portfolio pool based on available resources such as manpower, light equipment, heavy equipment, financial capability, return on investment, and success rate of winning the bid. Therefore, this optimization model will assist the contractor in utilizing its internal resource to its maximum and increase its winning chance for the new project considering past experience with clients, built-relation between two parties, and complexity in the exertion of the projects. The objective of this research will be to increase the contractor's winning chance in the bidding process based on the success rate and expected return on investment. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=bidding%20process" title="bidding process">bidding process</a>, <a href="https://publications.waset.org/abstracts/search?q=internal%20resources" title=" internal resources"> internal resources</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=contracting%20portfolio%20management" title=" contracting portfolio management"> contracting portfolio management</a> </p> <a href="https://publications.waset.org/abstracts/131963/optimization-technique-for-the-contractors-portfolio-in-the-bidding-process" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/131963.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">142</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3446</span> Market Solvency Capital Requirement Minimization: How Non-linear Solvers Provide Portfolios Complying with Solvency II Regulation</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Abraham%20Castellanos">Abraham Castellanos</a>, <a href="https://publications.waset.org/abstracts/search?q=Christophe%20Durville"> Christophe Durville</a>, <a href="https://publications.waset.org/abstracts/search?q=Sophie%20Echenim"> Sophie Echenim</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this article, a portfolio optimization problem is performed in a Solvency II context: it illustrates how advanced optimization techniques can help to tackle complex operational pain points around the monitoring, control, and stability of Solvency Capital Requirement (SCR). The market SCR of a portfolio is calculated as a combination of SCR sub-modules. These sub-modules are the results of stress-tests on interest rate, equity, property, credit and FX factors, as well as concentration on counter-parties. The market SCR is non convex and non differentiable, which does not make it a natural optimization criteria candidate. In the SCR formulation, correlations between sub-modules are fixed, whereas risk-driven portfolio allocation is usually driven by the dynamics of the actual correlations. Implementing a portfolio construction approach that is efficient on both a regulatory and economic standpoint is not straightforward. Moreover, the challenge for insurance portfolio managers is not only to achieve a minimal SCR to reduce non-invested capital but also to ensure stability of the SCR. Some optimizations have already been performed in the literature, simplifying the standard formula into a quadratic function. But to our knowledge, it is the first time that the standard formula of the market SCR is used in an optimization problem. Two solvers are combined: a bundle algorithm for convex non- differentiable problems, and a BFGS (Broyden-Fletcher-Goldfarb- Shanno)-SQP (Sequential Quadratic Programming) algorithm, to cope with non-convex cases. A market SCR minimization is then performed with historical data. This approach results in significant reduction of the capital requirement, compared to a classical Markowitz approach based on the historical volatility. A comparative analysis of different optimization models (equi-risk-contribution portfolio, minimizing volatility portfolio and minimizing value-at-risk portfolio) is performed and the impact of these strategies on risk measures including market SCR and its sub-modules is evaluated. A lack of diversification of market SCR is observed, specially for equities. This was expected since the market SCR strongly penalizes this type of financial instrument. It was shown that this direct effect of the regulation can be attenuated by implementing constraints in the optimization process or minimizing the market SCR together with the historical volatility, proving the interest of having a portfolio construction approach that can incorporate such features. The present results are further explained by the Market SCR modelling. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20risk" title="financial risk">financial risk</a>, <a href="https://publications.waset.org/abstracts/search?q=numerical%20optimization" title=" numerical optimization"> numerical optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20management" title=" portfolio management"> portfolio management</a>, <a href="https://publications.waset.org/abstracts/search?q=solvency%20capital%20requirement" title=" solvency capital requirement"> solvency capital requirement</a> </p> <a href="https://publications.waset.org/abstracts/127464/market-solvency-capital-requirement-minimization-how-non-linear-solvers-provide-portfolios-complying-with-solvency-ii-regulation" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/127464.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">117</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3445</span> Portfolio Optimization under a Hybrid Stochastic Volatility and Constant Elasticity of Variance Model</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Jai%20Heui%20Kim">Jai Heui Kim</a>, <a href="https://publications.waset.org/abstracts/search?q=Sotheara%20Veng"> Sotheara Veng</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This paper studies the portfolio optimization problem for a pension fund under a hybrid model of stochastic volatility and constant elasticity of variance (CEV) using asymptotic analysis method. When the volatility component is fast mean-reverting, it is able to derive asymptotic approximations for the value function and the optimal strategy for general utility functions. Explicit solutions are given for the exponential and hyperbolic absolute risk aversion (HARA) utility functions. The study also shows that using the leading order optimal strategy results in the value function, not only up to the leading order, but also up to first order correction term. A practical strategy that does not depend on the unobservable volatility level is suggested. The result is an extension of the Merton's solution when stochastic volatility and elasticity of variance are considered simultaneously. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asymptotic%20analysis" title="asymptotic analysis">asymptotic analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=constant%20elasticity%20of%20variance" title=" constant elasticity of variance"> constant elasticity of variance</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20optimal%20control" title=" stochastic optimal control"> stochastic optimal control</a>, <a href="https://publications.waset.org/abstracts/search?q=stochastic%20volatility" title=" stochastic volatility"> stochastic volatility</a> </p> <a href="https://publications.waset.org/abstracts/50103/portfolio-optimization-under-a-hybrid-stochastic-volatility-and-constant-elasticity-of-variance-model" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/50103.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">299</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3444</span> Multi-Criteria Based Robust Markowitz Model under Box Uncertainty</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pulak%20Swain">Pulak Swain</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20K.%20Ojha"> A. K. Ojha</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Portfolio optimization is based on dealing with the problems of efficient asset allocation. Risk and Expected return are two conflicting criteria in such problems, where the investor prefers the return to be high and the risk to be low. Using multi-objective approach we can solve those type of problems. However the information which we have for the input parameters are generally ambiguous and the input values can fluctuate around some nominal values. We can not ignore the uncertainty in input values, as they can affect the asset allocation drastically. So we use Robust Optimization approach to the problems where the input parameters comes under box uncertainty. In this paper, we solve the multi criteria robust problem with the help of E- constraint method. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title="portfolio optimization">portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=%CF%B5%20-%20constraint%20method" title=" ϵ - constraint method"> ϵ - constraint method</a>, <a href="https://publications.waset.org/abstracts/search?q=box%20uncertainty" title=" box uncertainty"> box uncertainty</a>, <a href="https://publications.waset.org/abstracts/search?q=robust%20optimization" title=" robust optimization"> robust optimization</a> </p> <a href="https://publications.waset.org/abstracts/118411/multi-criteria-based-robust-markowitz-model-under-box-uncertainty" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/118411.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">139</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3443</span> Numerical Solution of Portfolio Selecting Semi-Infinite Problem</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Alina%20Fedossova">Alina Fedossova</a>, <a href="https://publications.waset.org/abstracts/search?q=Jose%20Jorge%20Sierra%20Molina"> Jose Jorge Sierra Molina </a> </p> <p class="card-text"><strong>Abstract:</strong></p> SIP problems are part of non-classical optimization. There are problems in which the number of variables is finite, and the number of constraints is infinite. These are semi-infinite programming problems. Most algorithms for semi-infinite programming problems reduce the semi-infinite problem to a finite one and solve it by classical methods of linear or nonlinear programming. Typically, any of the constraints or the objective function is nonlinear, so the problem often involves nonlinear programming. An investment portfolio is a set of instruments used to reach the specific purposes of investors. The risk of the entire portfolio may be less than the risks of individual investment of portfolio. For example, we could make an investment of M euros in N shares for a specified period. Let yi> 0, the return on money invested in stock i for each dollar since the end of the period (i = 1, ..., N). The logical goal here is to determine the amount xi to be invested in stock i, i = 1, ..., N, such that we maximize the period at the end of ytx value, where x = (x1, ..., xn) and y = (y1, ..., yn). For us the optimal portfolio means the best portfolio in the ratio "risk-return" to the investor portfolio that meets your goals and risk ways. Therefore, investment goals and risk appetite are the factors that influence the choice of appropriate portfolio of assets. The investment returns are uncertain. Thus we have a semi-infinite programming problem. We solve a semi-infinite optimization problem of portfolio selection using the outer approximations methods. This approach can be considered as a developed Eaves-Zangwill method applying the multi-start technique in all of the iterations for the search of relevant constraints' parameters. The stochastic outer approximations method, successfully applied previously for robotics problems, Chebyshev approximation problems, air pollution and others, is based on the optimal criteria of quasi-optimal functions. As a result we obtain mathematical model and the optimal investment portfolio when yields are not clear from the beginning. Finally, we apply this algorithm to a specific case of a Colombian bank. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=outer%20approximation%20methods" title="outer approximation methods">outer approximation methods</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20problem" title=" portfolio problem"> portfolio problem</a>, <a href="https://publications.waset.org/abstracts/search?q=semi-infinite%20programming" title=" semi-infinite programming"> semi-infinite programming</a>, <a href="https://publications.waset.org/abstracts/search?q=numerial%20solution" title=" numerial solution"> numerial solution</a> </p> <a href="https://publications.waset.org/abstracts/29163/numerical-solution-of-portfolio-selecting-semi-infinite-problem" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/29163.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">309</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3442</span> The Empirical Analysis and Comparisons Using TAIEX Derivatives</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Pao-Peng%20Hsu">Pao-Peng Hsu</a>, <a href="https://publications.waset.org/abstracts/search?q=Ying-Hsiu%20Chen"> Ying-Hsiu Chen</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Historical data shows that there were high correlations among TAIEX Futures, Electronic Sector Index Futures, Finance Sector Index Futures and Taiwan Top 50 ETF. The performance under various futures is also discussed. We found that the worst portfolio is consisted of T50-ETF and T50-ETF futures and best portfolio is consisted of T50-ETF and TF. It implies that the annual return of a portfolio increases if a portfolio’s risk diversifies. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=arbitrage%20opportunities" title="arbitrage opportunities">arbitrage opportunities</a>, <a href="https://publications.waset.org/abstracts/search?q=ETF" title=" ETF"> ETF</a>, <a href="https://publications.waset.org/abstracts/search?q=futures" title=" futures"> futures</a>, <a href="https://publications.waset.org/abstracts/search?q=TAIEX" title=" TAIEX"> TAIEX</a> </p> <a href="https://publications.waset.org/abstracts/35758/the-empirical-analysis-and-comparisons-using-taiex-derivatives" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/35758.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">383</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3441</span> Optimization of a High-Growth Investment Portfolio for the South African Market Using Predictive Analytics</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Mia%20Fran%C3%A7oise">Mia Françoise</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This report aims to develop a strategy for assisting short-term investors to benefit from the current economic climate in South Africa by utilizing technical analysis techniques and predictive analytics. As part of this research, value investing and technical analysis principles will be combined to maximize returns for South African investors while optimizing volatility. As an emerging market, South Africa offers many opportunities for high growth in sectors where other developed countries cannot grow at the same rate. Investing in South African companies with significant growth potential can be extremely rewarding. Although the risk involved is more significant in countries with less developed markets and infrastructure, there is more room for growth in these countries. According to recent research, the offshore market is expected to outperform the local market over the long term; however, short-term investments in the local market will likely be more profitable, as the Johannesburg Stock Exchange is predicted to outperform the S&P500 over the short term. The instabilities in the economy contribute to increased market volatility, which can benefit investors if appropriately utilized. Price prediction and portfolio optimization comprise the two primary components of this methodology. As part of this process, statistics and other predictive modeling techniques will be used to predict the future performance of stocks listed on the Johannesburg Stock Exchange. Following predictive data analysis, Modern Portfolio Theory, based on Markowitz's Mean-Variance Theorem, will be applied to optimize the allocation of assets within an investment portfolio. By combining different assets within an investment portfolio, this optimization method produces a portfolio with an optimal ratio of expected risk to expected return. This methodology aims to provide a short-term investment with a stock portfolio that offers the best risk-to-return profile for stocks listed on the JSE by combining price prediction and portfolio optimization. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=financial%20stocks" title="financial stocks">financial stocks</a>, <a href="https://publications.waset.org/abstracts/search?q=optimized%20asset%20allocation" title=" optimized asset allocation"> optimized asset allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=prediction%20modelling" title=" prediction modelling"> prediction modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=South%20Africa" title=" South Africa"> South Africa</a> </p> <a href="https://publications.waset.org/abstracts/168328/optimization-of-a-high-growth-investment-portfolio-for-the-south-african-market-using-predictive-analytics" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168328.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">97</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3440</span> Median-Based Nonparametric Estimation of Returns in Mean-Downside Risk Portfolio Frontier</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=H.%20Ben%20Salah">H. Ben Salah</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Gannoun"> A. Gannoun</a>, <a href="https://publications.waset.org/abstracts/search?q=C.%20de%20Peretti"> C. de Peretti</a>, <a href="https://publications.waset.org/abstracts/search?q=A.%20Trabelsi"> A. Trabelsi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> The Downside Risk (DSR) model for portfolio optimisation allows to overcome the drawbacks of the classical mean-variance model concerning the asymetry of returns and the risk perception of investors. This model optimization deals with a positive definite matrix that is endogenous with respect to portfolio weights. This aspect makes the problem far more difficult to handle. For this purpose, Athayde (2001) developped a new recurcive minimization procedure that ensures the convergence to the solution. However, when a finite number of observations is available, the portfolio frontier presents an appearance which is not very smooth. In order to overcome that, Athayde (2003) proposed a mean kernel estimation of the returns, so as to create a smoother portfolio frontier. This technique provides an effect similar to the case in which we had continuous observations. In this paper, taking advantage on the the robustness of the median, we replace the mean estimator in Athayde's model by a nonparametric median estimator of the returns. Then, we give a new version of the former algorithm (of Athayde (2001, 2003)). We eventually analyse the properties of this improved portfolio frontier and apply this new method on real examples. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=Downside%20Risk" title="Downside Risk">Downside Risk</a>, <a href="https://publications.waset.org/abstracts/search?q=Kernel%20Method" title=" Kernel Method"> Kernel Method</a>, <a href="https://publications.waset.org/abstracts/search?q=Median" title=" Median"> Median</a>, <a href="https://publications.waset.org/abstracts/search?q=Nonparametric%20%20Estimation" title=" Nonparametric Estimation"> Nonparametric Estimation</a>, <a href="https://publications.waset.org/abstracts/search?q=Semivariance" title=" Semivariance"> Semivariance</a> </p> <a href="https://publications.waset.org/abstracts/19062/median-based-nonparametric-estimation-of-returns-in-mean-downside-risk-portfolio-frontier" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/19062.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">492</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3439</span> Analyzing Essential Patents of Mobile Communication Based on Patent Portfolio: Case Study of Long Term Evolution-Advanced </h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kujhin%20Jeong">Kujhin Jeong</a>, <a href="https://publications.waset.org/abstracts/search?q=Sungjoo%20Lee"> Sungjoo Lee</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In the past, cross-licensing was made up of various application or commercial patents. Today, cross-licensing is restricted to essential patents, which has emphasized their importance significantly. Literature has shown that patent portfolio provides information for patent protection or strategy decision-making, but little empirical research has found strategic tool of essential patents. This paper will highlight four types of essential patent portfolio and analysis about each strategy in the field of LTE-A. Specifically we collected essential patents of mobile communication company through ETSI (European Telecommunication Standards Institute) and build-up portfolio activity, concentration, diversity, and quality. Using these portfolios, we can understand each company’s strategic character about the technology of LTE-A and comparison analysis of financial results. Essential patents portfolio displays a mobile communication company’s strategy and its strategy’s impact on the performance of a company. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=essential%20patent" title="essential patent">essential patent</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio" title=" portfolio"> portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=patent%20portfolio" title=" patent portfolio"> patent portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=essential%20patent%20portfolio" title=" essential patent portfolio"> essential patent portfolio</a> </p> <a href="https://publications.waset.org/abstracts/43598/analyzing-essential-patents-of-mobile-communication-based-on-patent-portfolio-case-study-of-long-term-evolution-advanced" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43598.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">393</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3438</span> Financial Portfolio Optimization in Turkish Electricity Market via Value at Risk</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20G%C3%B6kg%C3%B6z">F. Gökgöz</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Atmaca"> M. E. Atmaca</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electricity has an indispensable role in human daily life, technological development and economy. It is a special product or service that should be instantaneously generated and consumed. Sources of the world are limited so that effective and efficient use of them is very important not only for human life and environment but also for technological and economic development. Competitive electricity market is one of the important way that provides suitable platform for effective and efficient use of electricity. Besides benefits, it brings along some risks that should be carefully managed by a market player like Electricity Generation Company. Risk management is an essential part in market players’ decision making. In this paper, risk management through diversification is applied with the help of Value at Risk methods for case studies. Performance of optimal electricity sale solutions are measured and the portfolio performance has been evaluated via Sharpe-Ratio, and compared with conventional approach. Biennial historical electricity price data of Turkish Day Ahead Market are used to demonstrate the approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20market" title="electricity market">electricity market</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management" title=" risk management"> risk management</a>, <a href="https://publications.waset.org/abstracts/search?q=value%20at%20risk" title=" value at risk"> value at risk</a> </p> <a href="https://publications.waset.org/abstracts/52928/financial-portfolio-optimization-in-turkish-electricity-market-via-value-at-risk" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52928.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">313</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3437</span> Optimal Portfolio Selection under Treynor Ratio Using Genetic Algorithms</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Imad%20Zeyad%20Ramadan">Imad Zeyad Ramadan</a> </p> <p class="card-text"><strong>Abstract:</strong></p> In this paper a genetic algorithm was developed to construct the optimal portfolio based on the Treynor method. The GA maximizes the Treynor ratio under budget constraint to select the best allocation of the budget for the companies in the portfolio. The results show that the GA was able to construct a conservative portfolio which includes companies from the three sectors. This indicates that the GA reduced the risk on the investor as it choose some companies with positive risks (goes with the market) and some with negative risks (goes against the market). <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=oOptimization" title="oOptimization">oOptimization</a>, <a href="https://publications.waset.org/abstracts/search?q=genetic%20algorithm" title=" genetic algorithm"> genetic algorithm</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title=" portfolio selection"> portfolio selection</a>, <a href="https://publications.waset.org/abstracts/search?q=Treynor%20method" title=" Treynor method"> Treynor method</a> </p> <a href="https://publications.waset.org/abstracts/43388/optimal-portfolio-selection-under-treynor-ratio-using-genetic-algorithms" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/43388.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">449</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3436</span> Financial Portfolio Optimization in Electricity Markets: Evaluation via Sharpe Ratio</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=F.%20G%C3%B6kg%C3%B6z">F. Gökgöz</a>, <a href="https://publications.waset.org/abstracts/search?q=M.%20E.%20Atmaca"> M. E. Atmaca</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Electricity plays an indispensable role in human life and the economy. It is a unique product or service that must be balanced instantaneously, as electricity is not stored, generation and consumption should be proportional. Effective and efficient use of electricity is very important not only for society, but also for the environment. A competitive electricity market is one of the best ways to provide a suitable platform for effective and efficient use of electricity. On the other hand, it carries some risks that should be carefully managed by the market players. Risk management is an essential part in market players’ decision making. In this paper, risk management through diversification is applied with the help of Markowitz’s Mean-variance, Down-side and Semi-variance methods for a case study. Performance of optimal electricity sale solutions are measured and evaluated via Sharpe-Ratio, and the optimal portfolio solutions are improved. Two years of historical weekdays’ price data of the Turkish Day Ahead Market are used to demonstrate the approach. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=electricity%20market" title="electricity market">electricity market</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=risk%20management%20in%20electricity%20market" title=" risk management in electricity market"> risk management in electricity market</a>, <a href="https://publications.waset.org/abstracts/search?q=sharpe%20ratio" title=" sharpe ratio"> sharpe ratio</a> </p> <a href="https://publications.waset.org/abstracts/52925/financial-portfolio-optimization-in-electricity-markets-evaluation-via-sharpe-ratio" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/52925.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">365</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3435</span> About the Case Portfolio Management Algorithms and Their Applications</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=M.%20Chumburidze">M. Chumburidze</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Salia"> N. Salia</a>, <a href="https://publications.waset.org/abstracts/search?q=T.%20Namchevadze"> T. Namchevadze</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This work deal with case processing problems in business. The task of strategic credit requirements management of cases portfolio is discussed. The information model of credit requirements in a binary tree diagram is considered. The algorithms to solve issues of prioritizing clusters of cases in business have been investigated. An implementation of priority queues to support case management operations has been presented. The corresponding pseudo codes for the programming application have been constructed. The tools applied in this development are based on binary tree ordering algorithms, optimization theory, and business management methods. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=credit%20network" title="credit network">credit network</a>, <a href="https://publications.waset.org/abstracts/search?q=case%20portfolio" title=" case portfolio"> case portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=binary%20tree" title=" binary tree"> binary tree</a>, <a href="https://publications.waset.org/abstracts/search?q=priority%20queue" title=" priority queue"> priority queue</a>, <a href="https://publications.waset.org/abstracts/search?q=stack" title=" stack"> stack</a> </p> <a href="https://publications.waset.org/abstracts/168639/about-the-case-portfolio-management-algorithms-and-their-applications" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/168639.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">150</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3434</span> Markowitz and Implementation of a Multi-Objective Evolutionary Technique Applied to the Colombia Stock Exchange (2009-2015)</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Feijoo%20E.%20Colomine%20Duran">Feijoo E. Colomine Duran</a>, <a href="https://publications.waset.org/abstracts/search?q=Carlos%20E.%20Pe%C3%B1aloza%20Corredor"> Carlos E. Peñaloza Corredor</a> </p> <p class="card-text"><strong>Abstract:</strong></p> There modeling component selection financial investment (Portfolio) a variety of problems that can be addressed with optimization techniques under evolutionary schemes. For his feature, the problem of selection of investment components of a dichotomous relationship between two elements that are opposed: The Portfolio Performance and Risk presented by choosing it. This relationship was modeled by Markowitz through a media problem (Performance) - variance (risk), ie must Maximize Performance and Minimize Risk. This research included the study and implementation of multi-objective evolutionary techniques to solve these problems, taking as experimental framework financial market equities Colombia Stock Exchange between 2009-2015. Comparisons three multiobjective evolutionary algorithms, namely the Nondominated Sorting Genetic Algorithm II (NSGA-II), the Strength Pareto Evolutionary Algorithm 2 (SPEA2) and Indicator-Based Selection in Multiobjective Search (IBEA) were performed using two measures well known performance: The Hypervolume indicator and R_2 indicator, also it became a nonparametric statistical analysis and the Wilcoxon rank-sum test. The comparative analysis also includes an evaluation of the financial efficiency of the investment portfolio chosen by the implementation of various algorithms through the Sharpe ratio. It is shown that the portfolio provided by the implementation of the algorithms mentioned above is very well located between the different stock indices provided by the Colombia Stock Exchange. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=finance" title="finance">finance</a>, <a href="https://publications.waset.org/abstracts/search?q=optimization" title=" optimization"> optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio" title=" portfolio"> portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=Markowitz" title=" Markowitz"> Markowitz</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a> </p> <a href="https://publications.waset.org/abstracts/56680/markowitz-and-implementation-of-a-multi-objective-evolutionary-technique-applied-to-the-colombia-stock-exchange-2009-2015" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/56680.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">302</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3433</span> An Expert System Designed to Be Used with MOEAs for Efficient Portfolio Selection</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Metaxiotis">Kostas Metaxiotis</a>, <a href="https://publications.waset.org/abstracts/search?q=Kostas%20Liagkouras"> Kostas Liagkouras</a> </p> <p class="card-text"><strong>Abstract:</strong></p> This study presents an Expert System specially designed to be used with Multiobjective Evolutionary Algorithms (MOEAs) for the solution of the portfolio selection problem. The validation of the proposed hybrid System is done by using data sets from Hang Seng 31 in Hong Kong, DAX 100 in Germany and FTSE 100 in UK. The performance of the proposed system is assessed in comparison with the Non-dominated Sorting Genetic Algorithm II (NSGAII). The evaluation of the performance is based on different performance metrics that evaluate both the proximity of the solutions to the Pareto front and their dispersion on it. The results show that the proposed hybrid system is efficient for the solution of this kind of problems. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=expert%20systems" title="expert systems">expert systems</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20optimization" title=" multi-objective optimization"> multi-objective optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=evolutionary%20algorithms" title=" evolutionary algorithms"> evolutionary algorithms</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20selection" title=" portfolio selection"> portfolio selection</a> </p> <a href="https://publications.waset.org/abstracts/3509/an-expert-system-designed-to-be-used-with-moeas-for-efficient-portfolio-selection" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/3509.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">439</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3432</span> Optimization of Smart Beta Allocation by Momentum Exposure</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=J.%20B.%20Frisch">J. B. Frisch</a>, <a href="https://publications.waset.org/abstracts/search?q=D.%20Evandiloff"> D. Evandiloff</a>, <a href="https://publications.waset.org/abstracts/search?q=P.%20Martin"> P. Martin</a>, <a href="https://publications.waset.org/abstracts/search?q=N.%20Ouizille"> N. Ouizille</a>, <a href="https://publications.waset.org/abstracts/search?q=F.%20Pires"> F. Pires </a> </p> <p class="card-text"><strong>Abstract:</strong></p> Smart Beta strategies intend to be an asset management revolution with reference to classical cap-weighted indices. Indeed, these strategies allow a better control on portfolios risk factors and an optimized asset allocation by taking into account specific risks or wishes to generate alpha by outperforming indices called 'Beta'. Among many strategies independently used, this paper focuses on four of them: Minimum Variance Portfolio, Equal Risk Contribution Portfolio, Maximum Diversification Portfolio, and Equal-Weighted Portfolio. Their efficiency has been proven under constraints like momentum or market phenomenon, suggesting a reconsideration of cap-weighting.  To further increase strategy return efficiency, it is proposed here to compare their strengths and weaknesses inside time intervals corresponding to specific identifiable market phases, in order to define adapted strategies depending on pre-specified situations.  Results are presented as performance curves from different combinations compared to a benchmark. If a combination outperforms the applicable benchmark in well-defined actual market conditions, it will be preferred. It is mainly shown that such investment 'rules', based on both historical data and evolution of Smart Beta strategies, and implemented according to available specific market data, are providing very interesting optimal results with higher return performance and lower risk.  Such combinations have not been fully exploited yet and justify present approach aimed at identifying relevant elements characterizing them. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=smart%20beta" title="smart beta">smart beta</a>, <a href="https://publications.waset.org/abstracts/search?q=minimum%20variance%20portfolio" title=" minimum variance portfolio"> minimum variance portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=equal%20risk%20contribution%20portfolio" title=" equal risk contribution portfolio"> equal risk contribution portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=maximum%20diversification%20portfolio" title=" maximum diversification portfolio"> maximum diversification portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=equal%20weighted%20portfolio" title=" equal weighted portfolio"> equal weighted portfolio</a>, <a href="https://publications.waset.org/abstracts/search?q=combinations" title=" combinations"> combinations</a> </p> <a href="https://publications.waset.org/abstracts/9011/optimization-of-smart-beta-allocation-by-momentum-exposure" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/9011.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">340</span> </span> </div> </div> <div class="card paper-listing mb-3 mt-3"> <h5 class="card-header" style="font-size:.9rem"><span class="badge badge-info">3431</span> Strategic Asset Allocation Optimization: Enhancing Portfolio Performance Through PCA-Driven Multi-Objective Modeling</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/abstracts/search?q=Ghita%20Benayad">Ghita Benayad</a> </p> <p class="card-text"><strong>Abstract:</strong></p> Asset allocation, which affects the long-term profitability of portfolios by distributing assets to fulfill a range of investment objectives, is the cornerstone of investment management in the dynamic and complicated world of financial markets. This paper offers a technique for optimizing strategic asset allocation with the goal of improving portfolio performance by addressing the inherent complexity and uncertainty of the market through the use of Principal Component Analysis (PCA) in a multi-objective modeling framework. The study's first section starts with a critical evaluation of conventional asset allocation techniques, highlighting how poorly they are able to capture the intricate relationships between assets and the volatile nature of the market. In order to overcome these challenges, the project suggests a PCA-driven methodology that isolates important characteristics influencing asset returns by decreasing the dimensionality of the investment universe. This decrease provides a stronger basis for asset allocation decisions by facilitating a clearer understanding of market structures and behaviors. Using a multi-objective optimization model, the project builds on this foundation by taking into account a number of performance metrics at once, including risk minimization, return maximization, and the accomplishment of predetermined investment goals like regulatory compliance or sustainability standards. This model provides a more comprehensive understanding of investor preferences and portfolio performance in comparison to conventional single-objective optimization techniques. While applying the PCA-driven multi-objective optimization model to historical market data, aiming to construct portfolios better under different market situations. As compared to portfolios produced from conventional asset allocation methodologies, the results show that portfolios optimized using the proposed method display improved risk-adjusted returns, more resilience to market downturns, and better alignment with specified investment objectives. The study also looks at the implications of this PCA technique for portfolio management, including the prospect that it might give investors a more advanced framework for navigating financial markets. The findings suggest that by combining PCA with multi-objective optimization, investors may obtain a more strategic and informed asset allocation that is responsive to both market conditions and individual investment preferences. In conclusion, this capstone project improves the field of financial engineering by creating a sophisticated asset allocation optimization model that integrates PCA with multi-objective optimization. In addition to raising concerns about the condition of asset allocation today, the proposed method of portfolio management opens up new avenues for research and application in the area of investment techniques. <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/abstracts/search?q=asset%20allocation" title="asset allocation">asset allocation</a>, <a href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization" title=" portfolio optimization"> portfolio optimization</a>, <a href="https://publications.waset.org/abstracts/search?q=principle%20component%20analysis" title=" principle component analysis"> principle component analysis</a>, <a href="https://publications.waset.org/abstracts/search?q=multi-objective%20modelling" title=" multi-objective modelling"> multi-objective modelling</a>, <a href="https://publications.waset.org/abstracts/search?q=financial%20market" title=" financial market"> financial market</a> </p> <a href="https://publications.waset.org/abstracts/183175/strategic-asset-allocation-optimization-enhancing-portfolio-performance-through-pca-driven-multi-objective-modeling" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/abstracts/183175.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">47</span> </span> </div> </div> <ul class="pagination"> <li class="page-item disabled"><span class="page-link">&lsaquo;</span></li> <li class="page-item active"><span class="page-link">1</span></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization&page=2">2</a></li> <li class="page-item"><a class="page-link" href="https://publications.waset.org/abstracts/search?q=portfolio%20optimization&page=3">3</a></li> <li class="page-item"><a class="page-link" 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