جستجوی مقالات مرتبط با کلیدواژه « portfolio » در نشریات گروه « مالی »

The interplay between various industrial groups plays a crucial role in determining the optimal investment portfolio for investors. Identifying which group carries or accepts risk within a specific time period and performance range allows for necessary adjustments in the investor's portfolio to achieve maximum returns. In this regard, the present study examines the impact of banking, automotive, cement, basic metals, and petroleum products groups on a symmetric, positive, and negative performance basis from January 5, 2015, to February 17, 2023. The results of the study indicate that in recent years, the overall index of these mentioned groups has shown more negative performance than positive performance. Moreover, banks and basic metals have acted as guiding and risk-transferring entities to other groups. On the other hand, the automotive and petroleum products groups have been risk-accepting, and their performance can be explained by the two groups of banks and basic metals.

Finding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of digital currencies using the LSTM neural network model and then form an optimal portfolio by calculating the rate of return, risk and the Sharpe ratio. The data used is from the archives of the Tehran Stock Exchange website, the website of the gold, coin and currency information network, as well as the website of buying and selling digital currencies. The time series of the prices of the investigated assets is between 2017 and 2020. Also, we used Python programming language and Gephi software to build the model and analyze the data. In the end, it was found that the LSTM neural network model is capable of predicting the price of financial assets with a very low error rate in each asset, and according to the Sharpe ratio obtained for each financial asset and the correlation matrix, Vebank stock, Khbahman 1 stock, and Digital currencies TRON, Tether and Bitcoin allocate more shares in the proposed portfolio.

Ever since Harry Markowitz's groundbreaking paper on the mean-variance model was published in 1952, numerous efforts have been dedicated to exploring the applications and advancements of classical models. Following the development of financial markets, active portfolio optimization has become one of the most important topics in finance. This study aimed to examine active portfolio management, a critical and delicate choice for investors, particularly concerning overall portfolio risk. The determination of an optimal stock portfolio that offers both a substantial return rate and controlled risk is consistently a subject of keen interest for analysts, investors, and even portfolio managers.
 

Many methods have been developed to measure investment risk, and the price of risky assets changes rapidly and randomly due to the complexity of the financial market. A random interval is a suitable tool for describing uncertainty with randomness and imprecision. Given the uncertainty in financial markets, this study used stochastic intervals to describe the returns of risky assets and the tail sequence risk, called the interval-valued conditional value at risk (ICVaR). The interval value in this model is an extension of the classic portfolio model, which can comprehensively reflect the complexity of the financial market and the risk-taking behavior of investors.
 

Following the findings from the real data of 10 out of 30 large corporates listed on the Tehran Stock Exchange, the ICVaR model is interpretable and compatible with the practical scenario and can be used to choose the optimal portfolio at different levels of risk and depending on the risk-taking degree of the investor. The present study used the portfolio optimization approach under a new criterion of ICVaR through the closing price, the highest price, and the lowest price on each trading day. In this model, the return range of the risky asset is taken as a random variable with an interval value. Besides, CVaR with an interval value is used to describe the risk instead of the variance at a certain level of return.
 

Uncertainties induced by asset transactions affect the predictions of investment plans. To address such challenging uncertainties in this study, a stable stochastic optimization approach was presented based on the range of optimal solutions produced by the proposed model to determine different operational options. Finally, the model developed in this study showed that investors’ subjective risk preference or aversion can be described by observing the principle of portfolio diversification, which reflects an innovation different from the classic portfolio model. Furthermore, the worst possible case was optimized in all scenarios in the model by robust optimization. The findings indicated that a narrower range corresponds to a higher level of risk aversion among investors.

The purpose of this research is to evaluate the investment methods and compare between the behavioral model and conventional rational models in choosing the optimal portfolio using cumulative perspective theory because the behavior of the stock market does not follow a linear pattern, for this reason, the methods A common line cannot be used to describe this behavior and be useful. Considering that choosing a portfolio is a difficult task according to different criteria and goals, in this research, an attempt was made to provide a solution for this problem by using cumulative perspective theory and the usefulness of the mentioned technique in choosing the best portfolio according to several Different criteria should be tested. The analyzed statistical sample is for 31 active companies in the period from the beginning of April 2015 to March 2014. The results of using cumulative perspective theory based on elliptic two-point distribution show that using this method over time and using more training data leads to the creation of a portfolio with better performance and a high standard. Also, the total portfolio, which is obtained by using the monthly average of the total stocks, has a favorable trainer criterion, which indicates better portfolio performance and creation of an optimal portfolio.

Today, beyond classical financial / behavioral theories, biorhythm science as a set of emotional causes: Perceptual and even physical, can be considered the basis for financial and investment decisions. In fact, this science is based on a sinusoidal diagram of a person's state according to the person's position in one of the positive points; Negative and critical shows that can be considered effective on individual decisions. The purpose of this research is evaluating the difference between investors biorhythmic internal rotations with the formation of portfolio based on value stocks. This study was conducted in a period of 6 months separately and 2 months in the form of 4 periods of 15 days to investigate the differences in biorhythmic internal rotations of institutional investors. In this study, 60 institutional investors in the form of 4 groups of 15 people in order to be in three cycles of physical biorhythm; Emotional and subjective were selected and analyzed through multivariate MANOVA tests, ANOVA with repeated measure and repeated measures analysis of variance and post hoc tests. The research tool was to measure the formation of value stock portfolio, the ratio of book value to market value and the separation of companies' stocks into two parts: growth and value, and to measure the biorhythmic internal rotations of investors from Biorhythm software (Biorhythm-365) used in Android and IOS operating systems. The results showed that investors who are in the positive zone in the mental and emotional periodicity tend to use value stocks to form more portfolios than investors who are in the negative and critical zone.

The purpose of the current study was the optimal allocation of assets using the cyclical behavior of commodities. For this purpose, the three commodities of copper, aluminum, and steel, along with gold and foreign exchange rate, were studied and their price data, along with the Tehran Stock Exchange (TSE) index during the period of January 2, 2009, to September 26, 2020, were analyzed. To examine the data, cluster analysis was used based on regime change behavior and Markovian state transition models were fitted to the data. Homogeneous and heterogeneous portfolios were constructed in terms of regime change behavior in both ascending and descending market regimes and the performance evaluation criteria of the portfolios were calculated. The results showed that portfolio diversification through the assets in the homogeneous cluster led to the best portfolio performance in the descending trend, while diversification by using commodity metals in the heterogeneous cluster might become the best performance in the ascending trend. In general, the results demonstrated that the use of commodities to diversify the portfolio led to better results in portfolio performance.

With the advent of cryptocurrencies in international financial markets, the study of new issues around the portfolio of individuals and the risk relationship of different segments of the financial market with each other has become prominent among investors and researchers. In this study, with the help of monthly data from 2013-to 2021 and to investigate the interaction effects of bitcoin fluctuations (risk) and exchange rate on fluctuations in the Iranian stock market index in the portfolio of the M SBVAR approach, two behavioral regimes (consistent with before and After the corona epidemic) has been identified for bitcoin price fluctuations and then the effects of bitcoin innovation on the stock market index during the two behavioral regimes. The results of the study, while confirming the adaptation of bitcoin behavioral regimes to the prevalence of the corona epidemic, show that during the corona epidemic, the stock market index reacts more strongly to bitcoin shocks and exchange rates, and the overall risk Increases the portfolio.

Investors are looking to choose the optimal combination of assets and allocate their wealth among them in such a way that they can achieve the goal of investing (increasing the revenue that can be seized in future periods). The main issue in this study, considering the conditions of high liquidity or low liquidity of companies' stocks and portfolio selection models, is the use of a new tool to select investment portfolio. The statistical sample for 27 companies active in the time domain from the beginning of April 2014 to March 2017 has been considered. The use of asset liquidity index to optimize portfolios using and benchmarking process has made a significant difference in portfolio weights, yields and risk compared to the Markowitz model. Also, the results of calculating the trainer criterion showed that the optimization model obtained from the benchmarking process of the value function has a higher performance than the portfolios obtained from the Markowitz model. .

The novel theory of the portfolio optimization has developed based on the fundamental Markowitz model. The Markowitz model is unique in terms of theory, but its weaknesses prevent the use of this model in practice. In this model, the return rate is extracted based on past data, but in this research, future scenarios related to return rates has been used in the investment process. The Markowitz model and its subsequent generalizations are multi-objective. In this study, from the development approach of Zimmermann's multi-objective optimization approach, that has fuzzy like structure, has been used. Also, for confront with the uncertainties from a robust optimization approach based on minimization of regret, has been used. In other words, first describe Zimmermann's model by considering the scenarios and specific weighting combinations, for the two purposes of minimizing risk and maximizing return, and then the minimax regret solution has been calculated with respect to the considered scenarios and weighted combinations. In the end of this paper, in order to evaluate the performance and validation of the proposed model, it has been implemented in Tehran Stock Exchange.

Providing facilities in the form of bank contracts increases the bank's income and survival, but this should not reduce the bank's ability to respond to depositors, nor should it increase the risk of overdue receivables, as these are factors that can lead to the bankruptcy of the bank or credit institution. Banks are more successful in this space, having a better strategy for combining their resources and consumption. In this research, a linear programming model is presented to identify the optimal combination of resources and expenditures of the bank with simultaneous attention to increasing revenues and reducing risks. Therefore, this research is quantitative and applied. The necessary data have been gained from the information sources of a sample bank in 2018. Model variables are facility values that are paid in the form of various contracts and the method of data analysis, modeling and programming is linear. The results of the research determined the optimal values of each facility and these results were compared with real figures. Also, the level of risk for each loan was determined and finally the parameters of the model were analyzed sensitively. The results of solving the linear programming model showed that the optimal values ​​are significantly different from the real values in qarz al-hasan, mudaraba and ju’alah. Also, the interest rates of conditional lease facilities, predecessor facilities and debt purchase facilities have the largest role in the bank's income. Applying this model can reduce unemployment resources, increase income and reduce the risk of overdue receivables in the bank. Even in cases where the bank is making a loss, this model can minimize the loss and at the same time reduce the likelihood of the bank going bankrupt.

Portfolio optimization and selection is one of the most important issues in the financial world, so investors are trying to make decisions that are most in line with the real world. But the uncertainty in data and parameters, and the contradiction in the investor's goals, adds to the complexity of the stock portfolio optimization problem, and the other hand because of the efficient market, it is necessary to use multi-period models that, unlike single-period models, allow the investor to review their wealth at the beginning of each period. This paper introduces a new approach to optimizing a multi-period portfolio optimization based on fuzzy general theory and using scenario tree to deal with uncertainties. In addition to considering all of the above constraints, It has made it possible for the investor to be able to apply his manner by changing the parameter to optimistic-pessimistic, and there is no need to model in credibility, necessity or possibility mode. Then the proposed model is solved by the Epsilon constraint method. Finally, using the data of 17 companies from different industries operating in the Tehran Stock Exchange Market in 1398, we examine the validity of the model and its efficiency.

A digital currency is a complex form of electronic money.The process of transferring this system is quite direct, and compared to the traditional method, there is less cost and time for transactions to take place in different parts of the world. Digital currencies use a system called the Block chain,Anyone who understands the benefits of the Chinese block,It's empty in our country .Advantages such as the impossibility of manipulating information, intelligence and decentralizing processes, along with high transparency, are issues that optimistic about the future of this technology. In this research, we select a number of digital currencies with the highest transaction volume and liquidity to create portfolios. and calculated using the Value at Risk (VaR) approach and the return on the portfolio Finally, an optimal investment portfolio is offered.

The need to achieve optimal portfolio optimization criteria, which, in addition to the accuracy of investment decisions quickly, goes beyond academic needs, as investors, investment companies and investment managers need to reduce their investment losses, increase Risk-adjusted returns have always been discussed. However, it is very difficult to achieve a portfolio optimization method that fills the gap between applied requirements and theoretical models. Considering that one of the most important factors in achieving optimal returns is diversification. This research, with the theme of "Formation of optimal investment portfolio in Tehran Stock Exchange using hierarchical and K-means clustering methods", attempts to present a suitable method for portfolio optimization using market data and clustering. The result of this comparison will clarify the success rate of cluster optimization compared to the index portfolio.

 Based on Markowitz theory of portfolio optimization, capital market is not predictable by any methods and the risk can only be diversified through portfolio formation and optimization. Recent works made huge developments in the basic model from modeling and risk measures perspectives. Spectral risk measures such as expected shortfall and value at risk are being used frequently as risk measures. In addition, researchers tend to consider uncertainty in risk and return evaluation via fuzzy, stochastic and robust modeling. However, a matter that has been neglected in many researches is portfolio management under uncertainty conditions. This paper propose a method for robust modeling of portfolio optimization and management using expected shortfall as risk measure and Bertsimas modeling as robust programming. The proposed model solved with artificial bee colony algorithm and results show a better performance of proposed model compared to classic methods in both the optimal portfolio formation and its management phase.

Portfolio optimization is one of the most important issues in financial sciences. Various strategies have been used to manage stock portfolios, which can be categorized into types: active and passive strategies. One of the most important passive portfolio management approaches is to form an index tracking portfolio. The purpose of index tracking portfolio is to its performance replicate market index as benchmark as closely as possible with a limited stocks in portfolio which will result in lower transaction costs for investor. In this research, a model for index tracking problem is proposed which aims to minimize undesirable deviations and maximize desirable deviations. Finally, a genetic algorithm is used to solve the leading model. To evaluate the performance of the model, data from four major industries of Tehran Stock Exchange has been used. The results show that the proposed model has a suitable performance in tracking the relevant index and achieving excess return over the benchmark.

Risk management is part of investment decision making. Diversification as one of the risk management techniques can increase the utility by increasing return for every unit of risk. In this paper, we review the effect of gold on diversification of stock portfolios in Tehran Stock Exchange. For this purpose, we used the spot price of gold coin as a standard price for gold and we used different indexed portfolios such as Tehran Stock Exchange Price Index, Industry Index, index of 50 companies from 2008 to 2018.We used Stochastic Dominance to rank portfolios as one of the criteria for rating and evaluating performance is to select a portfolio that maximizes the expected utility. By implementing stochastic dominance at three levels we compared the portfolio’s performance. The portfolios were made of an indexed portfolios and a share of gold which could be from 0 to 50 percent of the value of the portfolio. The results showed that adding gold to the portfolios of almost all of the indexes can improve their performances at 95% confidence level. The diversification benefit of gold will be maximized when at least 20% of the portfolios consist of gold coin.

Creating a balance between risk and return has always been the major criterion in investment decisions in stock exchange. All investors look for an optimized compromise for their investments so that the utility function of their investment is maximized. This study aims at arriving at a more efficient model for optimizing investment portfolio. This model is to consider uncertainty and investment risk on the way to create a bigger return for the investors. In this way, Extreme value theory for assessing investment risk, as one of the newest assessors of value at risk (VaR) was utilized. The time of this study covers the period from 2013 to 2018. The sample includes top 50 companies in Tehran Stock Exchange. Using GARCH method and maximizing likelihood function, the type of return distribution of top companies of Tehran Stock Exchange was determined at the first step. Next, efficiency frontier for investment risk in Tehran Exchange was compared to Markovitz model`s efficient frontier, using a quadric planning model through a Extreme value theory approach. The results of this study signify that forming an optimized investment portfolio through a Extreme value model would not make any significant difference with Morkovitz Variance-Mean model.

One of the main concerns of investors in financial market is reduction of risk and achieving desired return. One of the ways that reduce risk, is creating portfolio; but due to market changes, the optimal portfolio will not be stable and it is necessary to be control and rebalancing. Generally, investors in financial market are divided into two categories: real investors and legal investors. The volume of real investors' capital is little and is called small investors, too. Portfolio selection and rebalancing for small investors need attention to their criteria and limitations that are not considered in the classic financial models such as Markowitz’s model. These criteria are transaction cost, dividend, systematic risk and transaction units. In this study, a multi-objective and comprehensive model are provided that addresses the goals and limitations of small investors. For this purpose, the lexicographic goal programming is used and a mixed integer programming model is provided. Finally, the model is solved with actual data and the results are analyzed.