Sharif Digital Repository

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... 

Since investing in the stock market is always known as one of the ways to increase capital, many researches have been done in the field of portfolio selection in order to provide methods to earn more profit and control investment risk. One of the influential factors in increasing the profit from the portfolio is the proper prediction of future of shares. Therefore, in many research, various methods and tools have been used to predict the future of shares more accurately. One of these tools is forecasting the market regime. In this research, harmonic patterns have been used to predict the market regime. Also, based on the harmonic patterns, the scope of entry into the transaction, the... 

Price prediction in financial markets is an exciting problem for a vast majority of groups and people; however, investment portfolio managers and owners are always looking for holistic predic-tion approaches and tools having high functional accurate metrics. Strictly speaking, players in fi-nancial markets are always in search of methods and toolboxes since they need to overcome the un-certainty of their buy, sell, or hold decisions in order to reduce the investment risk. In this research, we have tried to deal with the stock price prediction problem as an asset pricing problem and find a novel approach to push forward the state-of-the-art of the problem based on the fundamental pric-ing... 

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... 

Portfolio generating functions are positive twice continuously differentiable functions of the common or ranked market weights. The return on such portfolios related to the market with a stochastic differential equation that has two components: the logarithmic change in the value of the generating function, and a drift process that is of bounded variation. The goal of this study is to generalized portfolio generating functions theorem for ’ functions, in order to cover some important functions in economic such as Gini coefficients  

In financial portfolio problem, optimization models with uncertainty has been considered. The multi-stage stochastic programming methodology has been used as a solution method for these problems. In this study, we use multi-stage programming with uncertainity to evaluate the asset allocation problem. Some general models have been introduced for problem solving. Non-anticipativity and parent-child models are implemented and solved by both simplex and interior-point methods and the results are compared. The results show the parent-child model to have a better performance. Also, the interior-point method appears to need less computing time than the simplex method  

In this thesis, we study a continuous time financial market of some risky assets and a risk-free asset for investment in a finite time period. We use mean-variance approach for investment in this market. In the model considered here, the mean returns of individual assets are explicitly affected by underlying Gaussian economic factors. Using past and present information of the asset prices, a partial-information stochastic optimal control problem with random coefficients is formulated. Here, the partial information is due to the fact that the economic factors can not be directly observed. In first step, by filtering and in secound step by solving the stochastic control problem, we show that... 

In each organization, decision-making about products and their impact on the profitability of the organization is vital. Today, with the constant changes and resource constraints, as well as the growing importance of environmental issues for organizations, creating a decision model that considers all of the aformentioned aspects is vital. This study considers ten effective factors related to product portfolio and focuses on maintaining business sustainability in multi-product organizations. First, by assessing the relationship between criteria and products, the priority and weight of each of the criteria by the network analysis process (ANP) method are determined. In the next step, using the... 

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... 

This research focuses on the topic of international portfolio diversification and its core attention is entitled to Middle East stock exchanges, as emergent markets. These markets are not analyzed yet as investment destinations for developed countries investors. Although all methodologies in this research are applied on ME markets data, but can be augmented and generalized for other markets. In this quest, we tried to investigate portfolio diversification opportunity for European (generally Frankfurt & Paris stock exchanges) or American investors with ME stock exchanges including Tehran, Istanbul, Saudi Arabia Tadawul, Abu Dhabi & Cairo stock exchanges, in order to reduce risk and increase... 

In this essay, by putting in the framework of linear-quadratic optimal control (LQ),we study and solve the mean-variance portfolio selection problem. Two models will be studied in our work; in one we assume that the price process satisfies a diffusion stochastic differential equation, while in the second model, we assume it to satisfy a jump-diffusion stochastic differential equation. In both models, a formula for the efficient frontier is obtained. This essay is mainly obtained from the works of the following articles and books:
1)X.Y. Zhou and D. Li, Continuous-Time Mean-Variance Portfolio Selection:A Stochastic LQ Framework. Applied Mathematics and Optimization. 42(2000),... 

Today’s organizations always encounter limitations on the way of achieving their goals. Budget constraint, inventory constraints, capital constraints are issues which complicates management decision making.
Global market has enabled manufacturers compete globally. Even position their factories in other countries and use of competitive advantages and other markets demand. This is why decision making in production and manufacturing domain has been complicated. Decision making in production and manufacturing should become faster and more precise in order to keep market competitiveness. Organization should make long term or strategic decisions and short term decisions about keeping the... 

In the thesis, a multi-period portfolio selection problem is presented in which transaction costs and diversity constraints are considered. The objective is to maximize the value of this portfolio at the end of the time horizon. Furthermore, data uncertainty, which is inherent in stock returns, is considered as well. To model data uncertainty, we apply robust optimization technique. At first, a robust counterpart model is proposed by assuming that uncertain parameters are independent. Then, we assume uncertain data are correlated. Both interval and budgeted uncertainty technique are applied in robust counterpart modeling. In the next step, we assume lending and borrowing risk-free interest... 

During the last years, a more volatile and dynamic financial environment has caused an increasing concern about the stability of banking systems. In this sense, it is widely agreed that credit risk is one of the variables that are more directly related to financial stability. One of the most important purposes of modeling credit risk, is estimating credit loss distribution and forecasting expected loss. In this research, we estimate and analysis credit loss by considering macroeconomic variables and latent factors. We express loans losses in terms of four stochastic components: default frequencies, the size of the loans portfolio, the exposures at default and the losses given default. ... 

Asset and liability management is a method for optimizing cash flows of input and output of a bank . The model for optimizing the balance between liability and assets of a bank is presented in this research. Bank asset liability management and planning can be considered at the same time. In tjis model , all assets and liabilities in the bank balance sheet are discussed under different objectives and requirements , such as legal and administrate requirements, market condition, risk rate etc  

In this thesis, a multi-stage project portfolio selection and scheduling problem in an uncertain environment is modeled, analyzed and solved. Moreover, we investigate the situations in which a project manager is confronted to a large number of projects to be selected and scheduled while the required resources of the projects are limited and renewable. The aim is to maximize objective function which is the net present value of the profit of the projects. To solve the mentioned problem, we introduce a multi-stage stochastic programming. For the reason that, in real problems, optimization problems that included all restraints are large scale and have computational complexity, these problems are... 

Markowitz’s asset allocation model with mean-variance approach has well-known issues, amongst them are too much sensitivity to input parameters (especially asset returns) and portfolios that are not diversified well. In order to cope with these issues, risk parity approach has been suggested, which by omitting return parameter from the model, tries to balance portfolio’s risk among different assets. However, risk parity has not been appreciated by researchers due to ambiguity in the investor’s utility function, lack of a solid mathematical framework and its heuristic nature. This study, first tries to develop the mathematical theory of risk parity at the asset level and at the risk factors... 

By expanding the use of IT and public access to financial markets, the number of players in this area has increased and the nonlinearity of the market has become more complex. Hence, investors need specific strategies that can make profitable investment by determining the time of purchase and sale of stocks. The purpose of this research is to provide a stock trading framework for strategic portfolio management. This framework uses daily values of 18 indicators of technical analysis as features and daily trading signals as data labels for training various machine learning models, such as support vector regression, k nearest neighbors, decision tree, artificial neural network and random...