جستجوی مقالات مرتبط با کلیدواژه « portfolio selection » در نشریات گروه « علوم انسانی »

With the passage of four generations of theoretical and experimental development of the smart city, it was expected that by relying on technologies such as the Internet of Things, artificial intelligence, cloud computing, and the like, this theory would be the way to face the problem and solve the complexities of resilience planning. But countless recent researches show the opposite. The root of the problem is not in the application of technology, but in the theoretical integration of smart city management and urban resilience. In the meantime, the resilience approach is a new point of view that instead of focusing on reducing vulnerability and looking at confrontation, it emphasizes increasing the resilience and flexibility of cities against natural and human hazards. The purpose of this research is to evaluate the feasibility of smart urban management to improve the resilience of Urmia city. To achieve this goal, a descriptive-analytical research method was used based on the analysis of a questionnaire that was completed by 30 urban experts and specialists. The results of the statistical analysis show that Urmia city is relatively favorable in terms of social and infrastructural resilience, but it does not have a favorable situation in terms of institutional-management, economic and physical-environmental resilience. In general, the overall resilience of Urmia city is lower than the optimal level according to the experts, so that the calculated value of the overall resilience of Urmia city is 2.90, which is less than the average level (3).

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decisionmaking problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated. In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market. In this research, we have used of firms’ performance between 1396-1400 in civil engineering, construction, investment and construction materials and tools manufacturers in order to determine portfolio selection. The main advantage of proposed approach is helping an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge

This research aims to expand the Markowitz model in a way that aligns more closely with real-world conditions, considering various fundamental analysis factors and market constraints. Conducted on the Tehran Stock Exchange, this study utilizes two measures, semi-variance and mean absolute deviations, alongside the variance measure in the Markowitz model to better estimate risk levels. In addition, several constraints such as cardinality constraint, threshold constraint, and segmentation constraint are employed to bring the results of the Markowitz-based model closer to reality. To ensure that the stock return metric is not solely based on stock price changes, this research incorporates nine important fundamental analysis metrics in filtering company stocks and as a return metric in the Markowitz model. Due to the computational complexity of the mathematical programming model, sample problems were also solved using the Harmony Search algorithm. The results indicate that the mathematical model performs better in terms of the distance from the ideal point efficiency metric, while the harmony search algorithm excels in uniformity metrics, exploring diverse solutions, and solution time. Increasing the range of cardinality and threshold constraints results in selecting more stocks in the portfolio, and simultaneously, the risk and return objective functions will improve concurrently.

The purpose of this paper is to present a new technique to the portfolio selection using Genetic Algorithm and Fuzzy Synthetic Evaluation. Portfolio selection is a multi-objective/criteria decision-making problem in financial management. The proposed approach (Genetic Algorithm and Fuzzy Synthetic Evaluation) solves the problem in two stages. In the first stage، by using genetic algorithm and fuzzy synthetic evaluation، weight of criteria will be calculated.  In second stage، using Fuzzy Synthetic Evaluation، Portfolios will be prioritized. A multi objective genetic algorithm is used to determine return and risk in the efficient frontier in Tehran stock market.  In this research, we have used of firms’ performance between 1396-1400 in chemical industries in order to determine portfolio selection. The main advantage of proposed approach is help an investor to find a portfolio which have Best performance، portfolio selection doesn’t rely to expert knowledge.  

Agency theory mainly refers to the conflict of interests between management and ownership that managers may behave differently in investing despite contract incentives. Accordingly, in the present article, while examining the behavioral financial concepts, the behavioral portfolio theory is explained and then the effect of managers' motivation on the selection of investment portfolio is evaluated in the framework of prospect theory. In the present study, ten-year data on stock index yield and cash return Bahadar has been used from the beginning of 1389 to the end of 1398 and while separating test data into two parts of test and evaluation data based on quarterly returns of the first seven years, the proposed behavioral portfolio based on motivation and vision theory and calculation of returns and risk for 81 The proposed portfolio for the next three years is examined. The statistical test of the research hypotheses at an error level of 5% indicates that although the yield of the resulting portfolio was not significantly different between the classical and behavioral models, the risk of the behavioral financial portfolio was less than the classical portfolio

The purpose of portfolio optimization is to select an optimal combination of financial assets, which should be a guide for investors to achieve the highest returns against the lowest possible risk. On the other hand, one of the key factors in portfolio optimization decisions is related to predict the stock prices. To do this, classical nonlinear mathematical and intelligent models such as regression are commonly used. In the present study, a nonlinear model of support vector regression with multiple outputs is applied to reduce the prediction errors. To show the effectiveness of the proposed model, the data of S & P500 index companies in the period 12/09/2016 to 02/08/2021 is used. The results show that the selection of a portfolio based on prediction using multiple vector backup regression due to considering the relationships between outputs simultaneously in terms of Sharp criteria has a better performance than the selection of portfolio based on prediction using regression method.

Choosing the right portfolio is always one of the most important issues for investors. The price trend is predicted using technical analysis or basic analysis. Technical analysis focuses on market performance, while the focus of fundamental analysis is on the mechanism of supply and demand, and these changes prices. The existence of a solution to predict growth or decrease in stocks has been studied as a basic need in this study. In the present study, with the help of a monitoring dataset, a solution based on Raff collection algorithms and hierarchical analysis to reduce the feature and decision tree algorithms, backup vector machine, and business network have been used for prediction. This proposed solution has been implemented using language and compared with different solutions, and the research results have shown that the proposed method with 80% accuracy of prediction and 20 errors in prediction has the highest accuracy and the lowest error rate among the methods compared.

The financial perspective of behavior, which is a combination of psychology and finance, states that psychology plays a role in financial decision-making. Accordingly, the purpose of the present study is to present a portfolio selection model with a financial-behavioral perspective of company managers based on agency theory. The present study was conducted in two steps. In the first step, the dimensions of the components of behavioral financial theories based on the theory of representation, the relationships between these variables are explained in the form of a mathematical model. The calculations of this section have been done by Mathematica software. In the second step, using the price index data and cash return of Tehran Stock Exchange for quarterly periods over a period of 10 years from the beginning of 1389 to the end of 1398 to test the proposed behavioral financial model with the theory of representation in the portfolio Optimization and comparison with the classic model are discussed. The analysis of the second step was performed by means of comparison of means with SPSS software. The results of the analysis of two standard and behavioral models in the present study showed that the behavioral model led to the achievement of a portfolio with less risk and the returns of the portfolios are not significantly different.

Considering that in most specialized fields, decisions are made in groups, in this study, a method to select the desired option in conditions of uncertainty is presented, which also considers the group decisions to increase the effectiveness.

In this study, the ZlCWAA method and data envelopment analysis, which are responsible for the averaging of Z numbers and assigning weight to specialists, were used to create the Z number extension of the best-worst method. This method is innovative in decision-making and, in this study, we used the Z number extension of the best-worst group method.

For better decision-making, the information should be more valid, accurate, and reliable. So we compared the FBWM, ZBWM, and DEA-GZBWM methods with each other, based on the case study and data collection from the perspective of each expert on the most important indicators of optimal stock portfolio selection. This comparison showed that the proposed method of DEA-GZBWM has a lower rate of incompatibility than the others. Therefore, the information obtained in this method can be more reliable for us.

To prove the effectiveness of the DEA-GZBWM method, a case study was conducted to show how to use this method in optimal stock portfolio selection; In which the investor, with the help of financial experts (experts in financial markets), invests and selects the optimal stock portfolio from the companies in the stock exchange and securities organization. Then, based on weight, rank, and incompatibility rate, the results of the proposed method, FBWM, and ZBWM methods were compared. This comparison showed that the proposed method is more functional than the others due to its lower incompatibility rate (0.108).

Passive management is an investing strategy that tracks a market value-weighted index or portfolio. It seeks to minimize the cost of investment fees and to avoid undesirable repercussions of the unpredictability of future trends. Active portfolio management tries to beat the market while passive portfolio management pursues a similar risk-return pattern to that of the market index. Index tracking is a passive investment strategy in the stock market that aims to make a portfolio using constituents of an index. It seeks to mimic its behavior without purchasing all of its constituents. This study aimed to track Tehran Exchange Dividend & Price Index (TEDPIX).

In this study, portfolios were tracked and their performances were examined by applying a two-tail mixed conditional value-at-risk model (main model). Optimizing TMCVaR is a linear program that minimizes the upper deviation and the downside deviation from the benchmark index. The investigated sample included the weekly data gathered from 2011/3/21 to 2018/20/3. The data was divided into 26-time frames including 52 in-sample data and 12 out-of-sample data.

Statistical tests confirmed the portfolios resulting from the main model were successful in tracking the index. As a result, the investigated model was recognized as capable of tracking the index. However, due to the tracking error and information ratio, the two models were not statistically different. In the present study, the two models showed the same performance in tracking the index.

In this study, a linear mathematical programming model was proposed to form index tracking portfolios. The results showed that although the main model was successful in index-tracking it did not outperform the mean absolute deviation model in terms of reduction in tracking error and increasing information ratio.

In network theory, centrality is a measure to estimate importance and influence of a special node to the whole network structure. The aim of this research is to investigate the characteristics of stock centrality and its reliability in risk estimation and portfolio selection.

First in this paper, we analyzed the relationship between stock’s centrality & benchmark risk estimation measures like beta & standard deviation. Then, we analyzed the relationship between stock’s centrality & Markowitz framework’s weights; and finally, we introduced centrality-based portfolio selection strategy and compared it with other benchmarks, by different portfolio performance measures.

Our observations indicate that in Tehran stock exchange, centrality can have an effective role in stocks risk estimation and there is a meaningful relation between centrality and other measures. We also observed that out that low central stocks can raise the benefits of portfolio diversification, and centrality-based portfolio selection method can have a better performance than other benchmark portfolio selection methods and results in a better risk adjusted return.

Stock centrality, as a measure to estimate importance and influence of member of a network, is capable of describing stock risk characteristics like other accepted measures. We can take advantage of this capability for portfolio selection.

Since Markowitz's (1952) pioneering work on a single-period investment model, mean-variance portfolio optimization problem has become a cornerstone of investment management in both academic and industrial fields. Despite the presence of various theories and methods, the  model continues to be considered in the portfolio selection, because it doesn`t need to estimate other parameters in optimization and computes simply. The objective of this study was to investigate the performance of Model  in the portfolio.

In this paper, various models and methods have been used to select the optimal portfolios and to evaluate the performance of the portfolio. At the end of the paper, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. Portfolio selection models in this paper include  model, mean-variance model, minimum-variance model and composition of the minimum-variance model and  model. In this paper, various criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, Information ratio have been used to measure portfolio performance.

Relatively, the performance of the  model was better in terms of Sharpe ratio and Modigliani and Modigliani ratio, the performance of the mean-variance model in terms of Trainer ratio, and the performance of the composition of the minimum-variance model and  model in terms of Sortino ratio and Information ratio.

Finally, the ELECTRE multi-criteria decision-making method has been used to rank the portfolio selection models. The results indicate that  model and minimum-variance model is superior to other models.

Portfolio selection is one of the most concerns of any investor and the goal is to distribute the capital in different assets in such a way that it has the highest rate of return with considering the minimal risk from the investor's point of view. Saving in financial institute or buying bonds and investment in housing market, stock market, foreign currency market or precious metals such as gold and silver are one of the most important choices for any investor with different degrees of risk. Decision situations can be completely certainly, risky and completely uncertainly and solving techniques can be optimization or heuristics. So far during the past decade, different methods are presented depending on the conditions of the capital portfolio selection issue. In this research, a meta-heuristic algorithm based on genetic algorithm and based on the group life of lions is introduced to find a suitable capital portfolio for the investor in risky conditions. Using optimistic, most likely and pessimistic estimates is a strategy used in risky situations. The results of the research confirmed the efficiency of the proposed algorithm in distribution of capital in different sectors with the criterion of maximum return on capital. Also, the proposed algorithm performed better than the whale optimization algorithm in optimizing the portfolio of the top 50 listed companies in terms of stock portfolio return and risk criteria and the time to reach the answer.

In this paper, a multi objective multi stage stochastic model is proposed to portfolio selection. This model takes into account both the investment goal and risk control at each stage. A scenario generation method is proposed that acts as the basis of the portfolio management model. Scenarios for multistage portfolio management are proposed that use by consumption that rate of returns are not correlated during stages. One of the most important aspects of this model is using transaction cost in model and providing this ability that investors could add or withdrawal cash during time. In the end some numerical example are illustrated and model effectiveness proved. As is presented using stochastic programming with recourse and combination of this model with scenario generation model provides this possibility for investors to plan their medium and short term investing. As can be seen result of the model proved effectiveness of the model in financial markets. As result presented having such tool that investor could adjust his or her portfolio during time according to targets such as maximizing rate of return and minimizing risk of his or her decisions could bring powerful superiority in competitive financial markets.

In today's highly competitive world, the condition of survival and participation in the field of activity, functioning and efficient and effective, is not achieved except through continuous planning, monitoring, control and evaluation. In this regard, we tried to present a mathematical hybrid model for selecting and planning an optimal composition of the shares according to the goals and priorities, in order to obtain the highest compatibility between the final selection and the initial ranking of each share. The proposed model consists of three steps and several steps, the SBM method of data envelopment analysis (DEA) (for initial stock revisions, multi-factor decision-making (TOPSIS)) in uncertainty conditions, for the assessment and ranking of shares in two individual steps and categorized and integer linear programming (IP) for choosing the best stock portfolio with increased scores according to the organization's priorities and constraints. Collect information from reputable sites of five industries active automotive, pharmacy, petrochemical, cement and food industries to the best stock portfolio for investment, due to the impact of algorithms and methods.

An investor Constant relative risk aversion. pursues two goals of expected utility increases and reduces expected portfolio liquidity expectations. In the current study, the Constant relative risk aversion. utility using actual portfolio fluctuations, real asymmetry, real elongation of graphs and non-liquidity The portfolios were measured using the non-liquidity rate. Therefore, it is possible to directly select the options of the investor in the two-dimensional expected utility / liquidity space. This research was analyzed by using high frequency data on a set of 40 shares of Tehran Stock Exchange from 2011 to 2017 using MATLAB software and time retrieval methods were used for daily synchronization of transactions. Considering the expected returns of the portfolio and the expected liquidity over the minimum variance and the same weighted portfolio, the power coverage of this model is examined. The results show that in the different risk-incompatible levels, the expected liquidity of the expected portfolio is highly competitive and seems appropriate in terms of usefulness, liquidity, and expected utility, relative to the benchmark.

risk & return are two main factors that affect financial decisions. The trade off between risk & return create different investment strategies. In other words investment decisions are all based on risk & return. In this research we used multiperiod selection method in order to maximize investors utility. In this model we used not only variance but also higher order moment –skewness- for optimization.
For emprical test of the model we used return of first 50 companies stored by market capitalization in tehran stock exchange during 1386-1395. We used skewness
& transaction cost to introduce a moltipriod model in asset allocation to minimize variance of investors utility. Comparing the result of this model with markowitz model & simpel model considering investor preferences shows that based on performance evaluation criteria, the suggested model perform much better than the two other.

The issue of portfolio selection has always been considered as one of the key issues in the field of investment. To select optimal portfolios, various models and methods have been represented since the initial presentation of the Markowitz approach. However, finding the most efficient model in portfolio selection has always been the subject of concern. Introducing a new model, called “the composition model of minimum-variance and N/1”, this paper aims to examine the efficiency of three different models of portfolio optimization. For this purpose, the performance of the composition model is compared with the sole minimum-variance model and the sole N/1 model. To evaluate the performance of the portfolios, some criteria such as Sharpe ratio, Trainer ratio, Modigliani and Modigliani ratio, Sortino ratio, and Information ratio have been applied. Finally, the TOPSIS multi-criteria decision-making method for ranking the research models has been used. The results indicate the superiority of the composition model over the two models applied solely.