‎‎The PORTFOLIO optimization problem, including PORTFOLIO selection, typically aims to maximize return and minimize risk. In this paper, we discuss about increasing use of stochastic PORTFOLIOs in investments and aim to create optimal PORTFOLIOs. It follows the relative wealth process of these PORTFOLIOs, outperforms the market PORTFOLIO over sufficiently long time-horizons. In this regard, initially, a model of the market is presented by the stochastic PORTFOLIO theory (SPT) and features like Growth rate, Excess growth rate are mentioned. Then, functionally-generated PORTFOLIOs are defined by using diversity weighted PORTFOLIOs with parameters p ∈ (0, 1), p < 0 and combination of them. Finally, by obtaining the daily closing price of 10 stocks in Tehran Stock Exchange (TSE) ,the performance of diversity weighted PORTFOLIOs is investigated.

This study investigates the application of deep reinforcement learning in optimizing investment PORTFOLIOs and integrates it with modern PORTFOLIO theory, illustrating significant advancements in financial management strategies. While modern PORTFOLIO theory is recognized as a mathematical framework aimed at maximizing expected returns while considering risk, its limitations—such as assumptions regarding the normal distribution of returns and the neglect of transaction costs—clearly highlight the need for adaptable solutions in complex and dynamic financial markets. This research demonstrates that through the implementation of deep reinforcement learning, investors can leverage real-time data and dynamic decision-making capabilities to develop more efficient and robust investment strategies. Moreover, challenges such as data quality, computational complexity, and the interpretability of deep reinforcement learning models are thoroughly examined. In this study, a Levenberg–Marquardt neural network algorithm for deep reinforcement learning is proposed for PORTFOLIO optimization based on historical data. For this purpose, data from 10 highly liquid companies listed on the Tehran Stock Exchange during the period from 2011 to 2021 were utilized. The findings of this study indicate that reinforcement learning algorithms can lead to a 15% increase in cumulative return compared to traditional methods in PORTFOLIO selection. Furthermore, the article recommends that analysts and investors employ advanced techniques to enhance corporate performance stability, enabling better investment decisions. Finally, this research offers practical pathways for future investigations in this domain.

One of the most important instruments used in PORTFOLIO analysis is the business PORTFOLIO analysis matrix and the industry attractiveness-business strength matrix is the variation of the business PORTFOLIO analysis matrix that we have selected for the purposes of this research. In this paper, we consider the limitations and problems of the classic PORTFOLIO analysis approach and resolve these problems by using fuzzy set theory. In the proposed methodology, both internal and external factors are evaluated in linguistic terms and in terms of fuzzy triangular numbers with fuzzy screening approach. Triangular fuzzy numbers were displayed in industry attractiveness-business strength matrix and specified a region on the matrix for each business. Thus the presented methodology in this article, according to the percentage of the rectangle in each zone, extracts and prioritizes the more significant strategies for businesses. Finally, an application in one of active Companies in Sugar industry is presented to illustrate the proposed methodology.

This research aims to design the PORTFOLIO Entrepreneurship Model and to analyze the effect of social capital, prior experience and knowledge, the entrepreneur’ s financial resources, as well as human capital on PORTFOLIO entrepreneurship and also to investigate the effect of PORTFOLIO entrepreneurship on recognition of new opportunities, as well as business growth and development. The research population includes all the PORTFOLIO entrepreneurs in Isfahan which their number is approximately 280. Simple random sampling method was used for sampling. The tool for data gathering was a questionnaire, compiled by the researchers, and its validity and reliability was confirmed. The results show that social capital, prior knowledge and experience, and the entrepreneur’ s available resources have positive and significant impacts on PORTFOLIO entrepreneurship and PORTFOLIO entrepreneurship has a positive and significant impact on business growth and development, and opportunity recognition.

In this study, using a basket of 5 most traded currencies as the base to measure currencies return (a basket with minimum variance in value), and applying variables affecting the exchange rate, we design a model for predicting and determining the best foreign exchange PORTFOLIO (in the sense of risk adjusted return), and then the output PORTFOLIOs of the model, will compete the momentum based PORTFOLIO which is commonly used in forex and other financial markets. If there is a significant difference between the two models, the model presented in this study will be introduced as a model with the more ability than momentum investing strategy to predict the currency risk adjusted return. For research, quarterly data from 15 currencies (which includes the 15 most traded currencies) has been used since 1999 to 2018, and the Dynamic Panel method is used to process related data. The research findings indicate the power of the proposed model for predicting risk adjusted return of the currencies Also, the finding shows than the fundamental variables (Interest Rate and Real Exchange Rate) have a positive relationship with the currencies return and the previous lags of currency return has a negative relation with the current return.

This essay is going to optimize the PORTFOLIO of stocks similar to the Markowitz approach. Nonetheless, the way in which the risk is measured is Foster-Hart risk. This measure was proposed by Foster and Hart in 2009. It takes into account the extreme events of losses. The theoretical definition could be as a minimum wealth that an investor should have in order not to face with bankruptcy. Our sample consists of adjusted daily data from thirty-four companies chosen from Tehran Stock Exchange’ s Top 50 Index in the period between 1391/07/01 and 1396/06/31. Data has been collected from Rahavard Novin software which is widely used in finance studies in Iran. Different optimal PORTFOLIOs has been achieved in this essay. Each of which uses a different method of risk like Cvar and Semi-Variance besides Foster-Hart. Results of this essay show that Foster-Hart optimal PORTFOLIO could have higher sharp ratio in comparison with the others.