portfolio optimization using evolutionary algorithms

... Multi-objective Optimization 1.2 Evolutionary Algorithms in Multi-objective Optimization 1.2.1 Evolutionary Algorithms 1.2.2 Multi-objective Evolutionary Algorithms ... Evolutionary Algorithms in Multi-objective Optimization Many optimization algorithms can be used to deal with MOPs In the literature, evolutionary algorithms (EAs) are one of the most popular ... of a typical EA and its basic process flow in multi-objective optimization are presented 1.2.1 Evolutionary Algorithms Evolutionary algorithms (EAs) [1] are computing paradigms, which mimic the...

Ngày tải lên: 09/09/2015, 10:07

... Hager Pricing Portfolio Credit Derivatives by Means of Evolutionary Algorithms GABLER EDITION WISSENSCHAFT Svenja Hager Pricing Portfolio Credit Derivatives by Means of Evolutionary Algorithms With ... 4.6 Genetic Algorithms 4.5.3 Regional Population Models 87 Evolutionary Algorithms in Finance: Literature 88 Evolutionary Algorithms ... Structures Allow for Flexible Portfolio Loss Distributions 3.9 65 Conclusion 65 Optimization by Means of Evolutionary Algorithms 73 4.1 Introduction...

Ngày tải lên: 07/03/2014, 19:20

... Approaches In Literature for portfolio optimization problem This is particularly trouble one because optimization routines are often characterized as error maximization algorithms Small changes of ... work of Markowitz (1952, 1959) on the mean-variance (MV) portfolio optimization procedure is a milestone in modern finance theory for optimal portfolio construction, asset allocation, and investment ... −1 and c= 1.2 The Markowitz Optimization Enigma where b= 1T Σ−1 1σ2 − µT Σ−1 µ1T Σ−1 − 1T Σ−1 µ The set of efficient feasible portfolios for all possible levels of portfolio risk forms the MV...

Ngày tải lên: 10/09/2015, 09:10

... Markowitz’s portfolio optimization is often times not practicable Michaud (1998) raised three categories of traditional criticisms of Mean-Variance optimization as follows: Mean-variance optimization ... times using parametric Monte Carlo simulation In a single iteration, an optimal portfolio risk is computed for given level of portfolio return Hence, after several iterations, for each level of portfolio ... Mean-Variance efficient portfolio The boundary of this confidence region sets the best and worse case scenarios for portfolio strategies Black and Litterman (1992) also cited that portfolio optimization...

Ngày tải lên: 12/09/2015, 10:54

... provided: ActivePortfolioManagementApp.class ActivePortfolioManagementAboutBox.class ActivePortfolioManagementEquity.class ActivePortfolioManagementHistory.class ActivePortfolioManagementPortfolio.class ... of steps ActivePortfolioManagementEquity: A class to emulate the behaviour of a stock ActivePortfolioManagemetnPortfolio: A class to emulate the behaviour of a portfolio ActivePortfolioManagementHistory: ... 23 3.3 ActivePortfolioManagementEquity class 25 3.4 ActivePortfolioManagementPortfolio class 25 3.5 ActivPortfolioManagementHistory class 25 3.6 ActivePortfolioManagementTick...

Ngày tải lên: 29/04/2013, 14:07

... overview on standard portfolio optimization Both passive and active portfolio management are considered Other results, such as risk measure minimization, are more recent V VI Portfolio Optimization and ... IX X II Portfolio Optimization and Performance Analysis Standard portfolio optimization Static optimization 3.1 Mean-variance analysis 3.1.1 ... 318 XII Portfolio Optimization and Performance Analysis 10 Optimal dynamic portfolio with risk limits 10.1 Optimal insured portfolio: discrete-time case 10.1.1 Optimal insured portfolio...

Ngày tải lên: 04/12/2013, 23:54

... converges to one of the solutions With an optimization study, you can specify the goal function in addition to the parameters for a feasibility study In an optimization study, you define the following: ... maintains a value of 0.25 in or greater Select Analysis pull-down menu Then choose Feasibility /Optimization The Optimization/ Feasibility dialog box appears 17 Under Study Type/Name choose Feasibility ... to FRONT view, and notice that point of CG is now align to the axis of rotation 21 OPTIMIZATION STUDIES The optimization study is setup very much like the feasibility study The main difference...

Ngày tải lên: 12/12/2013, 12:15

... combinatorial optimization problems subject to highly complex constraints, which are very difficult to solve by conventional optimization techniques This has led to the recent interest in using genetic algorithms ... Genetic Algorithms Genetic algorithms have proved to be a versatile and effective approach for solving optimization problems Nevertheless, there are many situations where the simple genetic algorithms ... Uniform crossover in genetic algorithms, in Proc 3rd ICGA, Ed J Schaffer, pp 2–9, Morgan Kaufmann, San Mateo, CA Syswerda, G., 1991 Scheduling optimization using genetic algorithms, in Handbook of...

Ngày tải lên: 25/12/2013, 19:15

... global optimization Amsterdam: North Holland, 1975 Dixon L.C.W., Szeg6, G.P.: Towards global optimization Amsterdam: North Holland, 1977 Hansen, Eldon: On solving systems of equations using interval ... shall again shorten notation and denote J(x, y, tl) by J(t/) and J(x, X , X) by J(X) Global Optimization Using Interval Analysis 251 Note that the elements of H(X) on and below the diagonal have ... stationary point if it occurs on the boundary We discuss this point further in Sect 10 Global Optimization Using Interval Analysis 253 If we were to use this Newton method only, we would in general...

Ngày tải lên: 12/01/2014, 22:02

... global optimization Amsterdam: North Holland, 1975 Dixon L.C.W., Szeg6, G.P.: Towards global optimization Amsterdam: North Holland, 1977 Hansen, Eldon: On solving systems of equations using interval ... shall again shorten notation and denote J(x, y, tl) by J(t/) and J(x, X , X) by J(X) Global Optimization Using Interval Analysis 251 Note that the elements of H(X) on and below the diagonal have ... stationary point if it occurs on the boundary We discuss this point further in Sect 10 Global Optimization Using Interval Analysis 253 If we were to use this Newton method only, we would in general...

Ngày tải lên: 12/01/2014, 22:17

... researchers proposed using genetic and evolutionary algorithms (GEAs) Using these nature-inspired search methods it is possible to overcome some limitations of traditional optimization methods, ... of genetic and evolutionary algorithms 2.2.2 Functionality Genetic and evolutionary algorithms imitate the principles of life outlined in the previous subsection and use it for optimization purposes ... Representations for Genetic and Evolutionary Algorithms Franz Rothlauf Representations for Genetic and Evolutionary Algorithms ABC Dr Franz Rothlauf Universität Mannheim...

Ngày tải lên: 31/03/2014, 15:58

... SYNTHESIS AND OPTIMIZATION OF DSP ALGORITHMS This page intentionally left blank Synthesis and Optimization of DSP Algorithms by George A Constantinides Imperial ... then covers the discrete-time description of signals using the z-transform Finally, Section 2.3 presents the representation of DSP algorithms using computation graphs 2.1 Digital Design for DSP ... FPGAs used in our implementation, explained the desciption of signals using the z-transform, and the representation of algorithms using computation graphs It has also provided an overview of the multiple...

Ngày tải lên: 09/04/2014, 16:39

... vascular cells using small interfering RNAs J Am Coll Surg 2007, 204:399-408 doi:10.1186/1479-5876-9-48 Cite this article as: Nabzdyk et al.: High throughput RNAi assay optimization using adherent ... 1) Gene knockdown was calculated using the ΔΔ-Ct method Statistical Analysis At least three independent experiments were performed and results were analyzed using Graph Pad Prism Version 5.0 ... obtain exact cell counts using the Celigo®’s brightfield cell segmentation function (Figure 2A & 2B) In order to address this issue, AoSMCs were trypsinized and manually counted using a hemocytometer...

Ngày tải lên: 18/06/2014, 19:20

... vascular cells using small interfering RNAs J Am Coll Surg 2007, 204:399-408 doi:10.1186/1479-5876-9-48 Cite this article as: Nabzdyk et al.: High throughput RNAi assay optimization using adherent ... 1) Gene knockdown was calculated using the ΔΔ-Ct method Statistical Analysis At least three independent experiments were performed and results were analyzed using Graph Pad Prism Version 5.0 ... obtain exact cell counts using the Celigo®’s brightfield cell segmentation function (Figure 2A & 2B) In order to address this issue, AoSMCs were trypsinized and manually counted using a hemocytometer...

Ngày tải lên: 20/06/2014, 03:20

... Heterologous expression and optimization using experimental designs allowed highly efficient production of the PHY US417 phytase in ... strategy herein developed combining heterologous expression using a cloning vector carrying the pAMβ1 replication origin and experimental designs optimization can be generalized for recombinant proteins ... Identification of critical culture variables using Plackett–Burman design For a screening purpose, various medium components and culture parameters were evaluated Using a Plackett–Burman (PB) factorial...

Ngày tải lên: 20/06/2014, 20:20

... Heterologous expression and optimization using experimental designs allowed highly efficient production of the PHY US417 phytase in ... strategy herein developed combining heterologous expression using a cloning vector carrying the pAMβ1 replication origin and experimental designs optimization can be generalized for recombinant proteins ... Identification of critical culture variables using Plackett–Burman design For a screening purpose, various medium components and culture parameters were evaluated Using a Plackett–Burman (PB) factorial...

Ngày tải lên: 21/06/2014, 19:20

... LEARNING CLASSIFIER PARAMETERS BY MEANS OF EVOLUTIONARY STRATEGIES Evolutionary algorithms combine characteristics of both classifications of classical optimization techniques, volumeoriented and ... appropriated than genetic algorithm) in real-values optimization problems Evolutionary computation algorithms offer practical advantages facing difficult optimization problems [10] These advantages are ... an evolutionary strategy (ES) The reason for using this technique is the big size of the space of solutions and the correlations among the parameters of the classifier, requiring an automatic optimization...

Ngày tải lên: 23/06/2014, 01:20

... Therefore, Ant colony optimization and Particle swarm optimization are usually classified into the swarm intelligence algorithms Evolutionary algorithms are successively applied to wide optimization problems ... the problem specific knowledge into evolutionary algorithms Fig Hybridization of Evolutionary Algorithms In Fig some possibilities to hybridize evolutionary algorithms are illustrated At first, ... self-adaptive evolutionary algorithms Evolutionary algorithms are a generic tool that can be used for solving many hard optimization problems However, the solving of that problems showed that evolutionary...

Ngày tải lên: 29/06/2014, 12:20

... published using dynamic variables for intraoperative fluid management Lopes and colleagues [11] demonstrated a significant morbidity reduction using solely pulse pressure variation in the optimization ... study, optimally using the novel software generation should be performed to confirm results of our study Key messages • In this study, intraoperative hemodynamic optimization using SVV in high-risk ... easy-to-use device Using a new software generation (version 3.0 or higher) would probably enhance the monitor performance, but it was not available at the beginning of our study Using dynamic variables...

Ngày tải lên: 13/08/2014, 20:22

... portfolio and the tangency portfolio The minimum variance portfolio is simply the portfolio that has the minimum variance among all other portfolios, not considering the returns, and is the portfolio ... Sharpe ratio using Particle Swarm Optimization, but for only a very limited number of assets It was not found in the literature articles applying GA or PSO to portfolio optimization using VaR, ... riskless asset (dashed) Portfolio A is the portfolio with minimum variance and portfolio B is the tangency portfolio with maximum Sharpe ratio where Sp is the Sharpe ratio of the portfolio, Rp is the...

Ngày tải lên: 28/03/2015, 20:10