Sharif Digital Repository

The portfolio selection problem is always one of the most important problems of finance and investments due to its great implication and vital role in financial institutions. Many of researches in this area are based on the mean-variance model, originally proposed by Markoitz. In the last two decades, however, researchers and investors have attracted to some new models that import some new factors other than mean and variance in the portfolio decision problem, such as different risk measures, etc. In this research we compare performances of mean-variance, Elton-Gruber-Padberg (EGP) and mean-variance-skewness based on genetic algorithm in Tehran Stock Exchange. Moreover, in order to find the... 

In this Thesis, a multi period portfolio optimization consisting stocks, gold and risk free asset is considered, in which periodical reinvestment and withdrawing is possible. Maximizing the net present value of investor’s cash flow is the objective. Due to the existence of uncertain parameters, two robust counterpart models are developed. In the first model, a conservative robust model is presented to generate feasible solution in all cases. In the second one, the conservative degree of investor is adjustable to control the risk of the model by investor appropriately. For evaluating the proposed models, the data of 5 well known stocks of Tehran market and gold prices are gathered. By using... 

In this thesis, we apply robust optimization to analyze the uncertainty of model parameters of a portfolio optimization which contains stock options. We also develop two robust counterpart models for single period and multiperiod problems. By assuming that the probability distribution of parameters is not known, their uncertainty is considered to lie within known linear intervals. Due to the existence of nonlinear relations (piecewise linear) between uncertain data (stock and option price), we present an over-conservative robust model to make the solution feasible for all parameters. However in the second model by adopting a different approach we develop a robust counterpart model with... 

In this study we are looking for an optimum way to select a multi-asset portfolio. Usually portfolios only contain stocks and bonds. Here we examine if adding other sort of assets like gold, currencies, bank deposit and gilt-edged securities are optimum or not. We are doing a comprehensive research of adding other kinds of assets to the portfolio. With modeling and solving the real situation, data and constraints of Iranian financial market we decide to whether add other assets to our portfolio or not. The results show in recent years low risk assets are optimum to add to the portfolio as it is was anticipated to the major economic of Iran  

Life insurance is a contract whereby the insurer becomes insured under certain conditions and for an amount known as insurance premium. Consequently, the insurer will pay the insured inheritors some given benefit as in the contract. There are different types of life insurance in the market. Other sectors of our proposed portfolio include risky and risk free investments. Household consumption is also considered as another variable in this package. This problem can be raised in both inflationary and non-inflationary states. You can also include a variable called loan. In the literature, this problem has been addressed by considering a sub-category of life insurance (Term Life Insurance)... 

Portfolio optimization is an most important field of study, affecting companies’ non-operating income, so companies try to achieve this beneficiary by incorporating it in market transactions. In this thesis, we propose some methods extending the Markowitz theory. We used the lagrangian method and other appropriate operations research techniques such as convex optimization in order to handle optimization process. Using these methods, we established portfolio optimization model for different corporation, i.e., banking industries and others, with different interest rate exposure for short and long conditions of risk free assets. We describe three risk criteria as possible alternatives to... 

In this thesis, we uses GARCH-EVT-copula and ARMA-GARCH-EVT-copula models to perform out-of-sample forecasts and simulate one-day-ahead returns for five stocks of Tehran Stock Exchange. We construct optimal portfolios based on the global minimum variance (GMV), minimum conditional value-at-risk (Min-CVaR) and certainty equivalence tangency (CET) criteria, and model the dependence structure between stock market returns by employing elliptical (Student-t and Gaussian) and Archimedean (Clayton) copulas. We analyze the performances of 42 risk modeling portfolio strategies using out-of-sample back-testing. Our main finding is that the Min-CVaR portfolio, based on ARMA-GARCH-EVT-Clayton forecasts,... 

I investigate methods of integrating prior hierarchical structure into hierarchical portfolio optimization methods. My contributions to the literature are forming a prior hierarchical structure based on investors' priorities and generating a unique representative distance matrix, which can be used as an input to other portfolio optimization methods too. In addition, I use SIC and GICs industry classifications as priory information for S&P500 companies and use them as a complementary input to the Hierarchical Risk Parity model and Hierarchical Equal Risk Contribution and compare the resultant portfolios' performance with (López de Prado, 2019)’s method of integrating prior information and... 

In the basic model of this thesis, which is called nested cluster optimization, only one distance function is used for clustering to form clusters with similar characteristics, while depending on whether the optimization model is long-only or long-short, different functions can be used. The aim of this thesis is to find the optimal distance function between assets in the simple nested cluster optimization so that during three different and separate strategies, based on three criteria of the lowest risk, the highest Sharpe ratio, and the highest return, the optimal distance function of assets is selected and clustering and finally weighting the portfolio to be done. The optimal distance... 

Portfolio optimization is a crucial concept in financial engineering, focusing on the efficient management of investment portfolios. In the realm of financial markets, a portfolio refers to a collection of investments held by individuals or companies, encompassing diverse assets. Specifically, a stock portfolio consists solely of stocks. The primary objective of portfolio optimization methods is to maximize returns while controlling risks. Within Tehran’s Stock Market, valuable data pertaining to the stock portfolios of big shareholders and their historical changes can be obtained. This dataset contains vital information that can be leveraged to optimize portfolios over time and formulate... 

Planning is a process to reach organizational goals, which many organizations can implement it to follow their desired outputs on the considered horizon with optimal costs under existing constraints and minimum control effort. Model Predictive Control (MPC) approach is considered as a planning approach based on forecasting model which contains three parts including objective function, constraints and forecasting model. The aim of using Model Predictive Control is to find specific numbers of current and future inputs on the determined horizon under existing constraints to reach the desired outputs of a dynamic system with minimum control effort. In order to deal with uncertain parameters of... 

The process of portfolio construction and optimization can be broken down into three main steps: selecting appropriate assets, allocating capital, and monitoring and adjusting the portfolio. This study focuses on evaluating the performance of the Hierarchical Risk Parity (HRP) method for capital allocation in investment portfolios, specifically in Iran’s capital market. The aim is to enhance the method's effectiveness by implementing alternative correlation calculation approaches, such as Wavelet and Chatterjee correlations. The study utilizes three different portfolios containing assets from the Tehran Stock Exchange, the US stock market, and the cryptocurrency market. The primary objective... 

The aim of this research is to develop and implement a deep reinforcement learning algorithm for portfolio management in the Tehran stock market, which is considered an emerging market with distinct patterns compared to the stock markets of developed countries. In this study, in addition to the market price data extensively used in previous research, we leverage fundamental ratio data extracted from company financial reports, which have received less attention. Furthermore, the research scope is limited to stocks in the mining and metal industries to enable the utilization of specific industry features, such as susceptibility to global prices of a key commodity. The portfolio management...