مطالب مرتبط با کلیدواژه

Portfolio optimization


۱.

Using Genetic Algorithm in Solving Stochastic Programming for Multi-Objective Portfolio Selection in Tehran Stock Exchange(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۲, Issue ۴, Autumn ۲۰۱۷ 107 - 120

کلیدواژه‌ها: Portfolio optimization Multi criteria decision making Stochastic Programming Chance constrained compromise Genetic Algorithm

حوزه های تخصصی:
تعداد بازدید : ۵۸۶ تعداد دانلود : ۳۳۰
Investor decision making has always been affected by two factors: risk and returns. Considering risk, the investor expects an acceptable return on the investment decision horizon. Accordingly, defining goals and constraints for each investor can have unique prioritization. This paper develops several approaches to multi criteria portfolio optimization. The maximization of stock returns, the power of liquidity of selected stocks and the acceptance of risk to market risk are set as objectives of the problem. In order to solve the problem of information in the Tehran Stock Exchange in 2017, 45 sample stocks have been identified and, with the assumption of normalization of goals, a genetic algorithm has been used. The results show that the selected model provides a good performance for selecting the optimal portfolio for investors with specific goals and constraints. 
۲.

Application of Clayton Copula in Portfolio Optimization and its Comparison with Markowitz Mean-Variance Analysis(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۳, Issue ۱, Winter ۲۰۱۸ 33 - 51

کلیدواژه‌ها: Portfolio optimization Copula Function Copula Clayton utility function

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تعداد بازدید : ۵۵۱ تعداد دانلود : ۳۵۰
With the aim of portfolio optimization and management, this article utilizes the Clayton-copula along with copula theory measures. Portfolio-Optimization is one of the activities in investment funds. Thus, it is essential to select an appropriate optimization method. In modern financial analyses, there is growing evidence indicating the distribution of proceeds of financial properties is not customary. However, in common risk management methods the main assumption is that the distribution of assets returns is normal. When the distribution of earnings isn’t normal, the linear correlation coefficient isn’t considered to be an appropriate measure to express the dependency structure. The investors are required to make use of methods that concentrate on the aggregated risks, considering the whole positions and the links between risk factors and assets. Therefore, we use copula as an alternative measure to model the dependency structure in this research. In this regard, given the weekly data pertaining to the early 2002 until the late 2013, we use Clayton-copula to generate an optimized portfolio for both copper and gold. Finally, the Sharpe ratio obtained through this method is compared with the one obtained through Markowitz mean-variance analysis to ascertain that Clayton-copula is more efficient in portfolio-optimization.
۳.

Portfolio Optimization by Means of Meta Heuristic Algorithms(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۴, Issue ۴, Autumn ۲۰۱۹ 83 - 97

کلیدواژه‌ها: Artificial Bee Colony Portfolio optimization Genetic Algorithm Ant Colony Algorithm

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تعداد بازدید : ۵۸۱ تعداد دانلود : ۵۲۵
Investment decision making is one of the key issues in financial management. Selecting the appropriate tools and techniques that can make optimal portfolio is one of the main objectives of the investment world. This study tries to optimize the decision making in stock selection or the optimization of the portfolio by means of the artificial colony of honey bee algorithm. To determine the effectiveness of the algorithm, its sharp criteria was calculated and compared with the portfolio made up of genes and ant colony algorithms. The sample consisted of active firms listed on the Tehran Stock Exchange from 2005 to 2015. The sample selected by the systematic removal method. The findings show that artificial bee colony algorithm functions better than the genetic and ant colony algorithms in terms of portfolio formation
۴.

Using MODEA and MODM with Different Risk Measures for Portfolio Optimization(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۵, Issue ۱, Winter ۲۰۲۰ 29 - 51

کلیدواژه‌ها: Portfolio optimization Data Envelopment Analysis Multi-Objective Decision Making Negative data Conditional Value at Risk

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تعداد بازدید : ۳۷۵ تعداد دانلود : ۲۵۶
The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model and the Multi-Objective MeanSharp-βRisk (MOMShβR) model base on Range Directional Measure (RDM) that can take positive and negative values. We utilize different risk measures in these models consist of variance, semivariance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) to find the best one as input. After using our proposed models, the efficient stock companies will be selected for making the portfolio. Then, by using Multi-Objective Decision Making (MODM) model we specified the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the Iranian stock companies is presented to demonstrate the usefulness and effectiveness of our models, and compare different risk measures together in our models and allocate assets.
۵.

Portfolio optimization with robust possibilistic programming(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۲, Issue ۴, Autumn ۲۰۱۸ 41 - 63

کلیدواژه‌ها: Portfolio optimization Sharpe ratio robust possibilistic programming

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تعداد بازدید : ۳۱۸ تعداد دانلود : ۲۲۲
Portfolio selection is one of the most important financial and investment issues. Portfolio selection seeks to allocate a predetermined capital (wealth) over one or multiple time periods between assets and stocks in a such way that the wealth of investor (portfolio owner) is maximized the risks are minimized. In the paper, we first propose a mathematical programming model for Portfolio selection to maximize the minimum amount Sharpe ratios of portfolio in all periods (max-min problem). Then, due to the uncertain property of the input parameters of such a problem, a robust possibilistic programming model (based on necessity theory) has been developed, which is capable of adjusting the robust degree of output decisions to the uncertainty of the parameters. The proposed model has been tested on 27 companies active in the Tehran stock market. At the end, the results of the model demonestrate the good performance of the robust possibilistic programming model.
۶.

Portfolio optimization with robust possibilistic programming(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۳, Issue ۲, Spring ۲۰۱۹ 44 - 65

کلیدواژه‌ها: Portfolio optimization Sharpe ratio robust possibilistic programming

حوزه های تخصصی:
تعداد بازدید : ۳۷۷ تعداد دانلود : ۲۲۸
one of the most important financial and investment issues is Portfolio selection, that seeks to allocate a predetermined capital (wealth) over one or multiple periods between assets and stocks in such a way that the wealth of investor (portfolio owner) is maximized and, Simultaneously, its risk minimized. In the paper, we first propose a mathematical programming model for Portfolio selection to maximize the minimum amount of Sharpe ratios of the portfolio in all periods (max-min problem). Then, due to the uncertain property of the input parameters of such a problem, a robust possibilistic programming model (based on necessity theory) has been developed, which is capable of adjusting the robust degree of output decisions to the uncertainty of the parameters. The proposed model was tested on 27 companies active in the Tehran stock market. In the end, the results of the model demonstrated the good performance of the robust possibilistic programming model. 
۷.

Measuring value at risk using short-term and long-term memory of GARCH models based on switching approach to form an optimal stock portfolio(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۵, Issue ۱, Winter ۲۰۲۱ 61 - 90

کلیدواژه‌ها: Portfolio optimization Value at Risk (VaR) Markov Switching Model ARFIMA- GARCH Family MSR-FIEGARCH

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تعداد بازدید : ۳۱۱ تعداد دانلود : ۱۹۶
Value at Risk model based on a switching regime approach was used in this study to optimize portfolios consisting of industry index (petroleum products, investment, chemical products, and metal products). For this purpose, the VaR of returns on index should first be extracted through parametric models of the (GARCH) family in each of the above industries by using regime transitions. After the risk of return on index is obtained for each industry, the optimal portfolio is created in the next step based on VaR minimization, and the optimal value of each industry is determined in the portfolio. According to the results, (MRS-FIEGARCH) model had no superiority in VaR estimation over the other parametric models of the GARCH family. In fact (MS-EGARCH-t) was introduced as the optimal model. Among the designated industries, returns on indices followed regime transitions only in chemical products and investment by showing asymmetric reactions to external shocks. Moreover, the optimal weights were on the rise in the industries where VaR decreased over time, whereas the optimal weight of the portfolio decreased in the industries where VaR increased over time. The higher share of an optimal portfolio belonged to the industries where stock returns had lower rates of VaR. The risk-return-ratio was employed to show that the optimal portfolio with a risk rate was measured by considering the switching regime was superior over the optimal portfolio with a risk rate extracted without considering the switching effects. To create an optimal portfolio, it is then recommended to make investments in the industries characterized by higher stability in prices and lower fluctuations in stock returns in the long run. This approach can be employed to obtain the best results from optimal portfolio preparation in the worst-case scenario of the market fluctuations.
۸.

The Quantitative Diversity Index in Multi-Objective Portfolio Model(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۵, Issue ۱, Winter ۲۰۲۱ 122 - 146

کلیدواژه‌ها: Systematic/Unsystematic risks VAR Diversity Index Portfolio optimization

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تعداد بازدید : ۴۲۲ تعداد دانلود : ۲۰۲
The primary purpose of investors is maximizing the utility that is characterized by two essential criteria include risk and return. Regarding investors' uncertainty about the future, one of the main ways to reduce risk is to diversify the investment portfolio. In this research, we proposed an index conducted by Euclidean distance for assessing portfolio diversity. Besides, we designed a multi-objective model to select optimal stock portfolios with considering value at risk (VaR), which is one of the critical indicators of unacceptable risk, portfolio Beta as systematic risk, and portfolio variance as unsystematic risk simultaneously. The model presented in this paper aims to maximize diversification while minimizing value at risk and stock risks. Furthermore, maximizing returns are considered as a limitation of this model. Since the proposed model is nonlinear and concerning computational complexity, it is NP-hard; therefore, we utilized the PSO and the GE metaheuristic algorithms that are improved for solving multi-objective problems to solve the model. The results of the model implementation in multiple iterations showed that the average yield of selected portfolios by the model is higher than the desirable condition. The evaluation of stock performance indicators also shows the satisfactory performance of the multi-objective model.
۹.

Modeling the selection of the optimal stock portfolio based on the combined approach of clustered value at risk and Mental Accounting(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۵, Issue ۲, Spring ۲۰۲۱ 70 - 94

کلیدواژه‌ها: Portfolio optimization mental accounting Value at Risk

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تعداد بازدید : ۳۴۰ تعداد دانلود : ۲۹۸
This paper concentrates on the modelling of optimal stock portfolio selection based on Risk Assessment and Behavioral Financial Approach Mental Accounting and 28 expert’s opinion. In this approach developing the model approved by the opinion of academic and practical experts using quantitative and qualitative methods.  Using quarterly return data of industrial indices for ten years in form of eight training and two test years indicates that the performance of DMSS and MVO based portfolios is equal however by regarding the value at risk and liquidity constraints in modeling, DMSS based portfolios perform higher than MVO portfolios.
۱۰.

Optimal Portfolio Selection for Tehran Stock Exchange Using Conditional, Partitioned and Worst-case Value at Risk Measures(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of Money and Economy, Vol. ۹, No. ۱, Winter ۲۰۱۴ 1-30

کلیدواژه‌ها: Portfolio optimization Value at Risk CVaR WVaR PVAR HGAPSO

حوزه های تخصصی:
تعداد بازدید : ۲۷۹ تعداد دانلود : ۱۵۴
This paper presents an optimal portfolio selection approach based on value at risk (VaR), conditional value at risk (CVaR), worst-case value at risk (WVaR) and partitioned value at risk (PVaR) measures as well as calculating these risk measures. Mathematical solution methods for solving these optimization problems are inadequate and very complex for a portfolio with high number of assets. For these reasons, a combination of particle swarm optimization (PSO) and genetic algorithm (GA) is used to determine optimized weights of assets. Stocks’ Optimized weight results show that proposed algorithm gives more accurate outcomes in comparison with GA algorithm. According to back-testing analysis, PVaR and WVaR overestimate risk value while VaR and CVaR give a rather accurate estimation. A set of companies in Tehran Stock Exchange are considered as a case study for empirical analysis. JEL Classification: G10, G11, G19
۱۱.

Hierarchical Risk Parity as an Alternative to Conventional Methods of Portfolio Optimization: (A Study of Tehran Stock Exchange)(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۵, Issue ۴, Autumn ۲۰۲۱ 1 - 24

کلیدواژه‌ها: Hierarchical Risk Parity Machine Learning Portfolio optimization Minimum variance

حوزه های تخصصی:
تعداد بازدید : ۳۲۶ تعداد دانلود : ۱۳۰
One of the most critical investment issues faced by different investors is choosing an optimal investment portfolio and balancing risk and return in a way that, maximizes investment returns and minimize the investment risk. So far, many methods have been introduced to form a portfolio, the most famous of the Markowitz approach. The Markowitz mean-variance approach is widely known in the world of finance and, it marks the foundation of every portfolio theory. The mean-variance theory has many practical drawbacks due to the difficulty in estimating the expected return and covariance for different asset classes. In this study, we use the Hierarchical Risk Parity (HRP) machine learning technique and compare the results with the three methods of Minimum Variance (MVP), Uniform Distribution (UNIF), and Risk Parity (RP). To conduct this research, the adjusted price of 50 listed companies of the Tehran Stock Exchange for 2018-07-01 to 2020-09-29 has been used. 70% of the data are considered as in-sample and the remaining 30% as out-of-sample. We evaluate the results using four criteria: Sharp, Maximum Drawdown, Calmer, Sortino. The results show that the MVP and, UNIF approach within the in-sample and, the UNIF and HRP approach out-of-sample have the best performance in sharp measure.
۱۲.

A Hybrid Artificial Intelligence Approach to Portfolio Management(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۶, Issue ۱, Winter ۲۰۲۲ 1 - 27

کلیدواژه‌ها: Portfolio optimization Artificial Intelligence Algorithmic Trading trading systems Genetic Algorithm Technical Analysis Neural Network

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تعداد بازدید : ۳۴۲ تعداد دانلود : ۱۷۶
The tremendous advances in artificial intelligence over the past decade have led to their increasing use in financial markets. In recent years a large number of investment companies and hedge funds have been implementing algorithmic and automated trading on their trading. The speed of decision-making and execution is the most important factor in the success of institutional and individual investors in capital markets. Algorithmic trading using machine learning methods has been able to improve the performance of investors by finding investment opportunities as well as time entry and exit of trading. The purpose of this study is to achieve a better portfolio performance by designing an intelligent and fully automated trading system that investors with the support of this system, in addition to finding the best opportunities in the market, can allocate resources optimally. The present study consists of four separate steps. Respectively, tuning the parameters of technical indicators, detecting the current market regime (trending or non-trending), issuing a definite signal (buy, sell or hold) from the indicators’ signals and finally portfolio rebalancing. These 4 steps respectively are performed using genetic algorithm, fuzzy logic, artificial neural network and conventional portfolio optimization model. The results show the complete superiority of the proposed model in achieving higher returns and less risk compared to the performance of the TEDPIX and other mutual funds in the same period.
۱۳.

Portfolio Optimization based on the Risk Minimization by the Weight-Modified CVaR vs. CVaR Method(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۶, Issue ۲, Spring ۲۰۲۲ 70 - 94

کلیدواژه‌ها: Portfolio optimization conditional value at risk (CVAR) Smoothly-clipped absolute deviation (SCAD) penalty function

حوزه های تخصصی:
تعداد بازدید : ۲۷۳ تعداد دانلود : ۱۴۶
Given the lack of a specific approach to the explanation of values of optimal portfolio weights in the portfolio optimization, the present study aimed to examine large-scale portfolio optimization according to both stock weighting and utilization of SCAD function to minimize the portfolio risk based on the "weight-modified conditional value at risk (CVaR)" and its comparison with the "conditional value at risk (CVaR)" method in the Tehran Stock Exchange. Therefore, the price information of companies listed in the Tehran Stock Exchange and Over-the-counter (OTC) from 2012 to the end of September 2020 was collected, screened, and analyzed daily, and then the risk and return of the portfolios were examined by forming optimal portfolios. The results indicated that the efficiency limit of the stock portfolio and also the ranks of different companies were different according to the types of the optimization method. Based on the behavior of the TEDPIX, the investors' degrees of risk-taking, and the risk management, diversification, and computational complexity of each method, the weight-modified CVaR had a better performance due to better diversification and risk management. Furthermore, the SCAD function added computational complexity to this method .
۱۴.

Forming Efficient Frontier in Stock Portfolios by Utility Function, Risk Aversion, and Target Return(مقاله علمی وزارت علوم)

نویسنده:
منبع: Iranian Journal of Finance, Volume ۶, Issue ۲, Spring ۲۰۲۲ 95 - 119

کلیدواژه‌ها: Risk Aversion Generalized Co-Lower Partial Moment Target Rate of Return Portfolio optimization Reference Dependent Utility Function

حوزه های تخصصی:
تعداد بازدید : ۶۲۲ تعداد دانلود : ۱۸۰
Asset allocation has always been a challenging issue / for individuals and businesses to survive in our competitive world. One of the famous businesses, which has an enormous impact on people's lives worldwide, is the pension industry. Pension funds- as Defined Benefit, Defined Contribution, or others- accept reserves from contributors and try to invest them in a way to keep up with their obligations in the future or even pay more than that. The equity market has been one of the good choices for investment as pension funds try to reach a particular rate of return to maximize their wealth while considering not crossing red lines in taking risks. This paper will detail the new mathematical model for finding optimal stock portfolios using Generalized Co-Lower Partial Moment as a risk measure to minimize portfolio optimization. On the other hand, it introduces new tailored Expected Utility as a performance metric to maximize in this model. The proposed model's issue against previous studies is considering risk aversion and target rate of investment return as two significant investor characteristics. This is based on price returns' simulation of candidate stocks in TSE while using accurate and nonparametric Probability Density Function in historical data analysis.
۱۵.

Introduction of New Risk Metric using Kernel Density Estimation Via Linear Diffusion(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۷, Issue ۲, Spring ۲۰۲۲ 467-476

کلیدواژه‌ها: Risk measurement Generalized Co-Lower Partial Moment Portfolio optimization Nonparametric estimation Stock Market

حوزه های تخصصی:
تعداد بازدید : ۳۰۸ تعداد دانلود : ۱۸۳
Any investor in stock markets around the world has a deep concern about the shortfalls of allocation wealth to any stock without accurate estimation of related risks. As we review the literature of risk management methods, one of the main pillars for the risk management framework in defining risk measurement approach using historical data is the estimation of the probability distribution function. In this paper, we propose a new measure by using kernel density estimation via diffusion as a nonparametric approach in probability distribution estimation to enhance the accuracy of estimation and consider some distribution characteristics, investor risk aversion and target return which will make it more accurate, compre-hensive and consistent with stock historical performance and investor concerns.
۱۶.

Higher moments portfolio Optimization with unequal weights based on Generalized Capital Asset pricing model with independent and identically asymmetric Power Distribution(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۶, Issue ۲, Spring ۲۰۲۱ 263-283

کلیدواژه‌ها: Capital Asset Pricing Model Independent and Identically Asymmetric Power Distribution Capital Asset Pricing Model Asymmetric Independent Exponential Power Distribution with two tail parameters Portfolio optimization Higher moments Adjusted-Sharp ratio

حوزه های تخصصی:
تعداد بازدید : ۸۱۹ تعداد دانلود : ۱۲۶۶
The main criterion in investment decisions is to maximize the investors utility. Traditional capital asset pricing models cannot be used when asset returns do not follow a normal distribution. For this reason, we use capital asset pricing model with independent and identically asymmetric power distributed (CAPM-IIAPD) and capital asset pricing model with asymmetric independent and identically asymmetric exponential power distributed with two tail parameters(CAPM-AIEPD) to estimate return and risk. When the assumption of normality is violated, the first and second moments lose their efficiency in optimization and we need to use the third and fourth moments. For the first time, we propose independent and identically asymmetric exponential power distributed with two tail parameters. Then, we use higher moments optimization with unequal weights to optimize portfolios. The results indicate that capital asset pricing model with independent and identically asymmetric power distributed (CAPM-IIAPD) is better than asymmetric independent and identically asymmetric exponential power distributed with two tail parameters(CAPM-AIEPD) to estimate return and risk. Adjusted Sharp ratio in portfolio optimization in second moments are higher than others. Adjusted returns to risk in third and fourth moments in the CAPM-IIAPD model significantly differ from the CAPM-AIEPD model and have a better performance.
۱۷.

Portfolio Optimization and the Momentum- Contrarian Strategy (MCS)- Based Performance: Evidence from Tehran Stock Exchange(مقاله علمی وزارت علوم)

نویسنده:
منبع: Journal of System Management, Volume ۹, Issue ۳, Summer ۲۰۲۳ 1 - 26

کلیدواژه‌ها: Optimal Portfolio Momentum Optimal Portfolio Contrarian Mean-variance model Portfolio optimization

حوزه های تخصصی:
تعداد بازدید : ۱۱۷ تعداد دانلود : ۱۰۲
This study was conducted to determine the stock portfolio with the best return and low-risk investments using momentum-contrarian strategies (MCSs). The momentum-Contrarian strategy is one of the well-known models to construct the portfolio which suggests buying the stocks with the best performance (the winner stocks) and selling the stocks with the worst performance (the loser stocks). The optimal values of the portfolio's objective function and the weight of all assets in the portfolio that are not necessarily the same are calculated by defining a nonlinear multivariate optimization model combined with momentum-contrarian strategies (MCSs). The return information of companies listed on the Tehran Stock Exchange from 2014 to 2019 was used to select the best optimal portfolio. The results confirmed the stability in the profitability of the contrarian optimal portfolio with minimum risk compared to other optimal portfolios. Furthermore, through MATLAB software the optimal weight of assets in the optimal portfolio is calculated based on statistical data.
۱۸.

Portfolio optimization using gray wolf algorithm and modified Markowitz model based on CO-GARCH modeling(مقاله علمی وزارت علوم)

نویسنده:
منبع: Advances in Mathematical Finance and Application, Volume ۹, Issue ۱, Winter ۲۰۲۴ 305 - 319

کلیدواژه‌ها: Portfolio optimization gray wolf algorithm Markowitz Model CO-GARCH

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تعداد بازدید : ۳۸۷ تعداد دانلود : ۳۸۹
Portfolio optimization which means choosing the right stocks based on the highest return and lowest risk, is one of the most effective steps in making optimal investment decisions. Deciding which stock is in a better position compared to other stocks and deserves to be selected and placed in one's investment portfolio and how to allocate capital between these stocks, are complex issues. Theoretically, the issue of choosing a portfolio in the case of minimizing risk in the case of fixed returns can be solved by using mathematical formulas and through a quadratic equation; but in practice and in the real world, due to the large number of choices in capital markets, the mathematical approach used to solve this model, requires extensive calculations and planning. Considering that the behavior of the stock market does not follow a linear pattern, the common linear methods cannot be used and useful in describing this behavior. In this research, portfolio optimization using the gray wolf algorithm and the Markowitz model based on CO-GARCH modeling has been investigated. The statistical population of the current research included the information of 698 companies from the companies admitted to the Tehran Stock Exchange for the period of 2011 to 2020. First, the optimal investment model is presented based on the gray wolf algorithm, and After extracting the optimal model, the efficiency of the gray wolf algorithm is compared with the Markowitz model based on CO-GARCH modeling.
۱۹.

Developing a Mathematical Programming Model to Determine the Optimal Portfolio of Capital Projects in Oil and Gas Companies to Achieve the Strategic goals(مقاله علمی وزارت علوم)

نویسنده:
منبع: Petroleum Business Review (PBR), Volume ۷, Issue ۲ - Serial Number ۲۳, Spring ۲۰۲۳ 15 - 30

کلیدواژه‌ها: Portfolio optimization Linear Planning of Integers Zero and One Portfolio management Optimal portfolio Oil and gas industry

حوزه های تخصصی:
تعداد بازدید : ۴۸ تعداد دانلود : ۳۲
Project portfolio management is a comprehensive framework for decision making and selecting the portfolio of projects to achieve the goals of the organization by considering resource constraints. The importance of this issue in Iran's oil and gas industry is even more remarkable than ever due to its unique position in the country's economy, capital-intensive and capital budget constraints that have been intensified in recent years. Identifying and defining different scenarios for each oil and gas field, determining the parameters of the mathematical model, the required data to calculate the parameters of the model and the process and methods of identifying this data, indicate the distinction and necessity of this research. This study is an applied research in terms of objective, using mathematical modeling approach, has provided a pattern to determine the optimal portfolio of capital plans of oil and gas companies. The research method is case study which has studied one of the most important oil and gas producing companies in the country and the only offshore company. In this study, a framework for selecting the optimal portfolio of capital projects is determined and after gathering required data, the zero-one integer linear mathematical programming model with the objective function of maximizing the net present value from fields (as the strategic goal of company) by considering investment constraints was designed and solved by GAMS software. Finally, according to the defined constraint, the best investment mode for each field was identified and the optimal portfolio was defined.
۲۰.

Cryptocurrencies and Risk-based Strategies Portfolio Diversification(مقاله علمی وزارت علوم)

نویسنده:
منبع: International Journal of Web Research, Volume ۷, Issue ۱,Winter-Spring ۲۰۲۴ 33 - 47
تعداد بازدید : ۲۵ تعداد دانلود : ۱۹
Recently, many investors have become interested in investing in cryptocurrency market. Investing in an asset carries a lot of risk and may bankrupt the investor. The main way to control this risk is portfolio diversification. In this paper, we will investigate the effect of portfolio diversification by adding cryptocurrencies to the portfolio. We evaluate the performance of seven risk-based portfolio optimization strategies. these strategies are the minimum variance, inverse volatility, L2-norm constrained minimum variance, L2-norm constrained maximum decorrelation, risk parity portfolio and maximum diversification. Our portfolios consist of three markets stocks including, Tehran Stock Exchange, Commodities and Cryptocurrencies. Also, due to the fact that the cryptocurrency market has gained a significant attraction among investors, we will examine the positive and negative effects of adding the five selected currencies, simultaneously and separately to the base portfolio, which is Tehran Stock Exchange-Commodities portfolio. We investigate that whether adding cryptocurrencies to a stock portfolio can be considered as a tool to improve a risk-based portfolio. After analyzing portfolios, the best portfolio in each strategy and the best strategy in each portfolio are introduced from the aspects of risk, return and Sharpe ratio, and finally we have concluded that entering the cryptocurrency market in most of the strategies lead to an overall increase in the return, while the approach is to minimize the risk of the portfolio. So, it can be concluded that if the main goal is to build a more diversified portfolio, better outcome can be obtained for the investor considering the return gained.