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

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

Theory and Applications of Robust Optimization

In this paper we survey the primary research, both theoretical and applied, in the area of Robust Optimization (RO). Our focus is on the computational attractiveness of RO approaches, as well as the modeling power and broad applicability of the methodology. In addition to surveying prominent theoretical results of RO, we also present some recent results linking RO to adaptable models for multi-stage decision-making problems. Finally, we highlight applications of RO across a wide spectrum of domains, including finance, statistics, learning, and various areas of engineering.Comment: 50 page

Solving the Optimal Trading Trajectory Problem Using a Quantum Annealer

We solve a multi-period portfolio optimization problem using D-Wave Systems' quantum annealer. We derive a formulation of the problem, discuss several possible integer encoding schemes, and present numerical examples that show high success rates. The formulation incorporates transaction costs (including permanent and temporary market impact), and, significantly, the solution does not require the inversion of a covariance matrix. The discrete multi-period portfolio optimization problem we solve is significantly harder than the continuous variable problem. We present insight into how results may be improved using suitable software enhancements, and why current quantum annealing technology limits the size of problem that can be successfully solved today. The formulation presented is specifically designed to be scalable, with the expectation that as quantum annealing technology improves, larger problems will be solvable using the same techniques.Comment: 7 pages; expanded and update

Modeling Portfolio Based on Linear Programming for Bank Business Development Project Plan

The bank’s business processes target business plans for the next year. Existing conditions, the business plan is based on the growth asset portfolio every year, so that the purchase of productive assets awaits issuers’ offers. This condition will cause a portfolio not to be measured and the inaccuracy of portfolio selection. Asset Liability Management (ALM) is the management of the structure of assets and liabilities to achieve profit. Banking books and trading books are bank portfolios to earn income. In selecting each portfolio, it contains liquidity risk, market risk and, credit risk. The level of profit is reflected in returns, while returns and risks are a trade-off so that calculations require mathematical and simulation models. Each bank needs an overview of the composition of productive assets, as short-term, medium-term and, long-term assets must be measured risk and target achievement. Linear programming method will allocate productive assets as the bank’s leading source of income, to achieve optimization of profit on the risks received. The problem with this research is that there are 830 variables as banking assets and 19 constraints as indicators of risk. In the seventh iteration of mathematical models, return 1,803 Trillyun from 11 banking book assets

A survey on financial applications of metaheuristics

Modern heuristics or metaheuristics are optimization algorithms that have been increasingly used during the last decades to support complex decision-making in a number of fields, such as logistics and transportation, telecommunication networks, bioinformatics, finance, and the like. The continuous increase in computing power, together with advancements in metaheuristics frameworks and parallelization strategies, are empowering these types of algorithms as one of the best alternatives to solve rich and real-life combinatorial optimization problems that arise in a number of financial and banking activities. This article reviews some of the works related to the use of metaheuristics in solving both classical and emergent problems in the finance arena. A non-exhaustive list of examples includes rich portfolio optimization, index tracking, enhanced indexation, credit risk, stock investments, financial project scheduling, option pricing, feature selection, bankruptcy and financial distress prediction, and credit risk assessment. This article also discusses some open opportunities for researchers in the field, and forecast the evolution of metaheuristics to include real-life uncertainty conditions into the optimization problems being considered.This work has been partially supported by the Spanish Ministry of Economy and Competitiveness (TRA2013-48180-C3-P, TRA2015-71883-REDT), FEDER, and the Universitat Jaume I mobility program (E-2015-36)

A survey on portfolio optimisation with metaheuristics.

A portfolio optimisation problem involves allocation of investment to a number of different assets to maximize return and minimize risk in a given investment period. The selected assets in a portfolio not only collectively contribute to its return but also interactively define its risk as usually measured by a portfolio variance. This presents a combinatorial optimisation problem that involves selection of both a number of assets as well as its quantity (weight or proportion or units). The problem is extremely complex due to a large number of selectable assets. Furthermore, the problem is dynamic and stochastic in nature with a number of constraints presenting a complex model which is difficult to solve for exact solution. In the last decade research publications have reported the applications of metaheuristic-based optimisation methods with some success., This paper presents a review of these reported models, optimisation problem formulations and metaheuristic approaches for portfolio optimisation

Methods to Support the Project Selection Problem With Non-Linear Portfolio Objectives, Time Sensitive Objectives, Time Sensitive Resource Constraints, and Modeling Inadequacies

The United States Air Force relies upon information production activities to gain insight regarding uncertainties affecting important system configuration and in-mission task execution decisions. Constrained resources that prevent the fulfillment of every information production request, multiple information requestors holding different temporal-sensitive objectives, non-constant marginal value preferences, and information-product aging factors that affect the value-of-information complicate the management of these activities. This dissertation reviews project selection research related to these issues and presents novel methods to address these complications. Quantitative experimentation results demonstrate these methods’ significance

Automatic Data Processing System of Constructing an Optimal Mean/Value-at-Risk Portfolio

We propose a computer-based automatic system of share prices processing for constructing an optimal mean/Valueat-Risk portfolio with mixed integer linear programming algorithm based on Benati - Rizzi method. We investigate the impact of so-called Value at risk measure on the size of total capital and shares in the optimal risky portfolio is necessary for revising the classical approach of Markovitz and for adapting it to the modern requirements in the banking and financial sectors. In a classical way it is impossible to construct a portfolio when structural changes in the stock market are happened, or as the same, when a long fall in prices is replaced by a steady growth. Our work is devoted to the study of the construction of the risky portfolio using the Value at risk measure. Within this investigation, two portfolios are constructed according to the classical Markowitz algorithm and the Benati - Rizzi algorithm. The sample alpha and beta-coefficients are estimated, the riskiness and profitability of passive portfolio investments are calculated. The comparison of returns and values of such portfolios of shares included in Moscow index MICEX-10 was carried out