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

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.

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.

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 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.