عمران محمدی

The current research was conducted to find the optimal portfolio for investing in stock exchange stocks, and one of the methods that is currently very popular among analysts and researchers in this field is methods based on artificial intelligence, followed by methods aimed at reducing risk metrics. The aim of the current research is to form a portfolio using machine learning methods, risk measurement and its combination with fuzzy theory, which has a better return than the average return of the market. The output of each method is entered into the random forest algorithm and prediction is made by this algorithm, and in the last step, the prediction output is entered into the value-at-risk and value-at-risk optimization model with the fuzzy theory approach to form the capital portfolio. Shares information is daily and its time period is from the beginning of 2014 to the middle of 2018. At the end of each of these methods and steps, it was compared with the real return of the market. the CVAR risk measure has a better ability than the VAR risk measure, and the random forest algorithm among the used machine learning algorithms has achieved better results in choosing the investment portfolio.

In the realm of burgeoning financial landscapes, the cryptocurrency market has garnered substantial attention owing to its pronounced profit potential. Despite witnessing remarkable expansion in recent years, investing in this market is concurrently entwined with heightened risk levels. Consequently, the imperative of selecting a judicious strategy and criteria for investment and risk assessment within the cryptocurrency market cannot be overstated. Among diverse risk measures, Quantile Based Risk Measures emerge as pivotal tools due to their intrinsic capacity to precisely discern adverse risks. Consequently, both investors and scholars exhibit a keen inclination towards leveraging this category of risk measures. This research delves into the scrutiny and juxtaposition of portfolio efficiency comprising cryptocurrencies, employing Conditional value-at-risk and conditional dropdown value at risk as two paramount risk measures based on percentiles. Such an examination not only facilitates investors in adeptly steering their cryptocurrency investment portfolios through well-informed decision-making but also contributes to an enhanced comprehension of these risk measures and their potential application in diverse investment contexts. To bolster the practical utility of these models in authentic settings, operational constraints have been deliberately incorporated into their design. The research outcomes underscore that the conditional value-at-risk model yields superior performance. Adopting it as a preferred criterion empowers investors to make decisions imbued with greater discernment and evidence-based rationale in managing their investment portfolios. These findings not only deepen the understanding of these risk criteria and their applicability in investment decisions but also furnish investors with insights essential for making judicious and optimal

Investing in the stock market plays a fundamental role in economic growth and development by providing companies and governments with investment opportunities, funding and stimulating economic activity. Putting together a suitable investment portfolio for stock market activities is of utmost importance and requires skill and the ability to optimally combine different stocks to achieve desirable performance and better returns. Paying attention to market fluctuations when constructing an investment portfolio is crucial as they indicate changes and dynamics in the market. Investment decisions made based on these fluctuations help investors reduce their financial risks and identify investment opportunities. In addition, attention to positive volatility is important in portfolio construction as these fluctuations indicate the growth potential and profitability of stocks. The aim of this research is to construct an optimal investment portfolio to take advantage of the benefits and investment opportunities arising from positive volatility, while considering negative volatility and its reduction. To achieve this, a novel approach, the so-called two-stage semi-variance approach, is introduced. Using monthly stock data from the beginning of March 2018 to March 2023, the stock portfolio is constructed, and the efficiency of this model is evaluated by comparing it with the semi-variance model and the equally weighted portfolio. The results show that the two-stage semi-variance approach outperforms the semi-variance model and the equally weighted portfolio. This indicates that this approach can significantly improve the efficiency and performance of investment portfolios compared to traditional methods.

The capital market is one of the most influential institutions in the country when it comes to the economic improvement of society, and the optimal investment in this market has always been the main concern of investors and researchers in this field. Nowadays, a lot of research has been done to form an investment portfolio and various models have been introduced to optimize the investment portfolio. This study aims to investigate the performance of different investment portfolio optimization models under different scenarios in the insurance industry and pension funds as one of the main pillars of the capital market.

This study investigates the performance of different investment portfolio optimization models using variance, semivariance, standard deviation, semistandard deviation, conditional value at risk and conditional drawdown at risk under different scenarios. The optimized investment portfolio using the beta return-based mean-variance model and the CAPM model is also presented as an alternative consideration for investors with higher accuracy and as a basis for decision making. The data used for this research were collected from April to March 1401 on a daily basis from Tehran Stock Exchange.

Monthly stock returns indicate that the respective stocks were not stable and did not show a steady trend during the analyzed period. By forming an investment portfolio consisting of shares of insurance companies and pension funds, the return and risk of each investment model were determined, providing investors with comprehensive information. Depending on their risk appetite or risk aversion, investors can choose different investment strategies to maximize the benefits of their investment.

This research shows that the portfolio formed with the Conditional Drawdown at Risk measure has a return of 22%, the highest return among the analyzed models in the insurance industry in the Tehran Stock Exchange market.

The purpose of the current research is to create an optimal portfolio using machine learning algorithms and fuzzy theory, which has a better return than the average return of the market (total index of the stock exchange).

In this article, the stocks of the selected companies are classified in the first stage using the two introduced algorithms. In the next step, stocks that entered the positive class are predicted for the next trading day with the help of random forest algorithm. For each company, three predictions are made, which are the inputs of fuzzy method optimization. Optimization is done with the aim of minimizing the risk with risk measures of value at risk and value at conditional risk. Shares information is five years old (daily) and its time period is from the beginning of 2017 to the end of 2021.

In the end, each of the algorithms and the risk measure used were measured and compared with the actual market return. Based on the obtained results, the CVAR risk measure has a better capability and result than the VAR risk measure, and the support vector machine algorithm has also achieved a better performance in choosing the investment portfolio.

Originality/ value: 

This research is optimized in the form of a capital sample by integrating machine learning methods and risk measures. Adding VaR and CVaR risk metrics enhances the decision-making process regarding risk reduction. Forecasting with the help of random forest and using an approach based on fuzzy theory for risk and value analysis gives the research an innovative perspective in portfolio formation. The findings provide investors and researchers with valuable insights in their search for better investment strategies.

In today's world, optimizing investment portfolios has received increasing attention. While predicting the expected returns of investment options and incorporating them into the objective function for profit maximization is a common practice, the most significant innovation in current research is the minimization of prediction error as the objective function. This innovation advises investors to emphasize not only on profit and risk but also on the predictability of investment options when forming an investment portfolio. Integrating return prediction from traditional time series models into portfolio formation can enhance the performance of the primary portfolio optimization model. Since machine learning and deep learning models have demonstrated a significant superiority over time series models, this paper combines return prediction in portfolio formation with machine learning models, namely Random Forest, and deep learning model, Long Short-Term Memory (LSTM). To evaluate the performance of the proposed model, five years of historical data from 2017 to 2021 are used for five industry sectors: banking, automotive, pharmaceutical, metal, and petroleum. The experimental results demonstrate that the mean-variance optimization models perform better when return prediction is done using Random Forest

Forecasting has always been recognized as an important issue in financial markets and is considered a unique factor in estimating future unknown values. The aim of this research is to identify and forecast the conditions of the Tehran Stock Exchange(TSE) and the factors affecting them, focusing on the correlation between market prosperity and value at risk. To achieve this, in the first step of this study, the time series of the value at risk index on the capital market TSE was estimated using daily data and the first-order GARCH method from spring 2010 to June 2023. Then, the factors influencing prosperity in TSE were evaluated based on seasonal data from spring 2010 to June 2023 using the probit regression approach. In addition, value at risk index was calculated seasonally and the relationship between the probability of market prosperity and the value at risk index was examined using correlation coefficients.The research results show that the probability of market prosperity in the Iranian capital market has a significant negative relationship with the bank interest rate, liquidity growth and the occurrence of sanctions. There is also a significant positive relationship with the inflation rate and the growth of the exchange rate. Furthermore, the correlation analysis shows that market prosperity is directly related to equity value at risk. Assuming stable conditions, the research suggests that the probability of a prosperity market in the next three seasons is significantly higher than the occurrence of a recession.

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.

Uncertainty is the obvious attribute of any activity in the real world, and performance analysis of units in terms of uncertainty is one of the most important concerns of managers and planners of companies. Various approaches to data envelopment analysis have been presented so far to provide a good tool for evaluating the efficiency of units in uncertain situations, including random, fuzzy and robust approaches. In a situation where the probability distribution function of random variables is known, stochastic approach is a precise solution for problem solving. Nevertheless, previous research in this field has focused on the transformation of a random problem into a definite problem. In this research, a method for modeling data envelopment analysis is presented using computer programming. In this method, random processes are simulated, and finally, the performance distribution function is presented instead of a definite number as the unit's efficiency. In this model, a new concept is presented as the probability of efficiency, which represents the probability that the unit will be located on the efficient boundary. The proposed method is empirically applied in a real case of selecting research and development projects.

Economic factors have an impact on business structures and their regulations. Sole proprietorship, partnership, and company are the main methods of conducting a business. Due to special circumstances in different countries there are different patterns of business structures. Professional services are among these special circumstances that necessitate special structures with regard to partnerships. There is no proper structure in Iran legislations for trade in professional services. This started by undermining the differences between the Company and the Firm which eradicated the necessary elements for trade in professional services. Inconsistencies in subsequent legislations exacerbated this condition. Now in a new Commercial Bill there are provisions that pose more challenges on trade in professional services. This article discusses the key factors to improve trade in professional services using collective behavior theories, the history of business structures, and comparative law.

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.

Mutual funds and investment companies are very important institutions for investing in capital markets. Hence, measuring their performance with the aim of identifying efficient investment firms and providing a corrective remedy for inefficient firms is essential. The purpose of the present study is to propose a new approach to assessing the performance and classification of investment firms in terms of uncertainty. Considering the two-stage structure of investment firms, in this research, the approach of network data envelopment analysis (NDEA) is used to evaluate the performance of each stage and the entire investment company. Also, in order to take into account, the uncertainty of the data, the interval network data envelopment analysis (INDEA) model for a two-stage structure is presented. Using the data of 10 active investment companies in Tehran Stock Exchange, the proposed research approach has been implemented and all companies have been evaluated and classified. The results of INDEA model based on non-cooperative approach indicating on the efficacy and effectiveness of the proposed approach of research for performance measurement of investment firms under uncertain data.

Under no circumstances, the importance of health supply chain management can't be diminished. Locating health centers and hospitals in natural disaster scenarios can lead to greater control over life and financial losses. In this paper, three main levels, such as demand points (cities of Guilan province), health centers/hospitals and suppliers are defined to locate the mentioned facilities, which have been solved using linear and deterministic planning model. The optimization model approach and the final solution are solved and presented accurately considering Guilan province. The main purpose of this proposed model is to minimize construction and relocation costs due to disruption in hospitals, health care centers, especially links between demand points and hospital/medical centers, as well as between suppliers and hospital/medical centers.

Portfolio is a collection or combination of investments in financial and non-financial assets that may be carried out by an individual or organization. How to select and optimize of portfolio is very important. One of the most important points that should be considered in the proposed approach for portfolio selection, is uncertainty. Because, one of the most important features of financial markets is their uncertainty. Thus, the purpose of this study is to present a bi-objective model for portfolio selection that is capable to be used under uncertainty of financial data and for this purpose, a robust optimization approach has been used. It should be noted that return and conditional value at risk (CVaR) are considered as model objectives, and the constraints of the number of shares and the purchasing volume of each share have been added to the model. Also, due to the complexity of the proposed model, a NSGA-II meta-heuristic algorithm has been used to solve the suggested model of research. Finally, the presented model was solved by using the actual data of 200 stocks of Tehran stock market for the period of 2017 and the results were analyzed. The results indicate the efficiency of the proposed approach portfolio selection according to the investor's preferences and constraints under uncertainty of financial data.

Uncertainties in today's complex world are an integral part of common issues. Given these uncertainties in real issues, we can achieve a more reliable level of results. The special circumstances of our country's emergencies, which witness many of these natural and abnormal events, such as earthquakes, floods, accidents and human casualties every year, require special attention to the provision of emergency medical services to injured people at the scene or near the site in priority Specifically, developing mobile hospital systems will have a significant role in reducingmortality rates. In this article, a new look at the issue of therapeutic activities scheduling and sequencing in field hospitals has been uncertain. The proposed model for the assign and sequence therapeutic activities (jobs) in the operating rooms (machines) of a field hospital. In order to solve this model, Simulated Annealing algorithm has been used and the performance of this meta-heuristic algorithm is guaranteed by a methods of designing experiments, called the Response surface method. The results show significant changes after applying uncertainty in the model. The degree ofuncertainty impact in the model depends on the decision maker, and its degree of application determines the degree of variation in the program relative to the certain state

Startup accelerator program is a new incubation model to accelerate venture creation providing specialized mentorship, networking opportunities and financing growth driven businesses. Since emergence of the first accelerator in US in 2005, the number of seed accelerating programs has quickly grown worldwide. Performance evaluation of these emerging programs not only could clarify relative success of this new incubation approach but also help out Iranian emerging accelerators to benchmark from pioneers of the industry. The evaluation could also assist startups and entrepreneurs to find out which of the existing accelerators are well-performed and thus ease decision making to select them as their incubator. So in this paper we aim to measure the performance of US top accelerators using a DEA method combined with goal programing approach. The inputs of the evaluation consist of numbers of mentors and startups, investment on each startup in average and in general, and the share of accelerator in startup company. The outputs in turn are different and include number and value of exits, and fund raising. Comparison of the final result with Seed Accelerator Ranking Project (SARP), shows different sorting and it is due the distinct evaluation approach. SARP considers only outputs of accelerators but we take in to account both outputs and inputs in the evaluation which could better illustrate the efficiency of DMUs.

The purpose of the present study is to provide a fuzzy window data envelopment analysis (FWDEA) model in order to financial performance evaluation of stocks over different time periods under the uncertainty of the data. In other words, in this research, we will try to present a new approach to assess the stock's performance with the ability to be implemented in the presence of uncertain panel data. Because the use of information about several time periods rather than a time period, as well as taking into account the uncertainty in the data, can lead to more reliable results in the process of stock evaluation. It is necessary to explain that in modeling and presenting the mentioned approach, data envelopment analysis, window analysis and possibilistic programming have been used. Finally, the fuzzy window data envelopment analysis model was implemented on 5 stocks of the chemical industry in Tehran stock exchange for four periods from 2013 to 2016, and the results indicate that the FWDEA method is effective.

Risk management is a significant issue in supply chain management. Improving the ability to control and manage the risk, enables the companies to be more successful in competing with other companies and decrease the expected long-term loss. In this manuscript, a mixed integer linear programming model for designing the green supply chain is presented. This model aims to minimize the cost, greenhouse gas emissions, and risk. Risk of supplying the raw materials and transportation in all levels of supply chain are under uncertainty. Furthermore, cost of raw materials is suggested by suppliers to producers with an incremental discount. The initial modelling is turned into a deterministic one using Bertsimas and Sim budget of uncertainty approach and consequently solved by GAMS software to manage risk. Furthermore, the uncertain parameter is analyzed and using various amounts the obtained result has been assessed and evaluated. The results show that the risk function is the most important factor in objective function, because parameters of risk function are subject to uncertainty.

In the performance evaluation process, using the classic data envelopment analysis (DEA) models, decision making units (DMUs) are considered as black boxes. While in many cases and different applications such as investment funds, banks, insurance companies, etc., DMUs have a network structure. In addition, in many network structures, some of the indicators used to calculate the efficiency of DMUs have an undesirable nature. Therefore, it is necessary to provide an effective approach that takes into account the internal structure of the DMus and the inter-organizational relationships in the calculation of efficiency and also is capable to be used in the presence of undesirable data. Accordingly, the purpose of the present study is to provide a network data envelopment analysis (NDEA) model in order to performance assessment of DMUs with a two-stage structure in the presence of undesirable outputs. It should be noted that according to the two-stage structure of investment companies and the presence of undesirable output in their evaluation process, ten investment companies from Tehran Stock Exchange have been selected as a case study for the research. Finally, the results of their performance evaluation by applying the proposed approach of research have been analyzed.

One of the key issues for investors is the issue of creating an optimal stock portfolio. In the issue of choosing an portfolio, the decision maker faces different and sometimes conflicting goals such as rate of return, liquidity, dividend, and risk. In portfolio optimization, the main issue is the optimal choice of assets and securities that can be made with a certain amount of capital, but on the one hand, the uncertainties associated with each share, and, on the other hand, the multiplicity of the optimal portfolio selection model, on the complexity of the problem increases. In this paper, the portfolio optimization under uncertainty has been studied. A randomized approach to converting uncertainty into a state of definiteness and agreeing to plan for a single objective is used in combination. Information about 20 pharmaceutical companies from the Tehran Stock Exchange has been used and the validity of the model has been investigated. The results show that the stock portfolio offered has a high performance.

Today, the portfolio optimization and its management is one of the most important areas in financial decision-making. Therefore, picking a portfolio of stocks that could bring the highest rate of return and the lowest risk investment for its holder simultaneously has become one of the main concerns of the economic actors. But in choosing the optimum portfolio just these factors are not decisive and according to the economic environment, many factors can affect this process which should be identified and considered. Therefore, in order to cover these matter multi-criteria decision-making approaches should be used. On the other hand, when the real-world conditions and restrictions, including restrictions on investment in any of the stocks and cardinality constraint are considered in portfolio optimization, the problem is not easily solvable by means of usual mathematical methods. Specially when there are a large number of assets in the portfolio evaluation process. Regarding this fact, the main purpose of this paper is to solve portfolio optimization problem by using the Data Envelopment Analysis (DEA) and Symbiotic Organisms Search (SOS). Finally, the model used in this study has been solved with real data and the results have been analyzed. The results of this paper demonstrate that the proposed approach has been successful in portfolio optimization and has been able to properly interact with the actual limitations and effective variables of the market

The portfolio optimization has become one of the most important issues for the companies operating in the capital market. When the real-world conditions and restrictions, including cardinality constraint are considered in portfolio optimization, the problem is not easily solvable. Therefore using the meta-heuristic methods will be considered. Regarding this fact, the main purpose of this paper is to solve portfolio optimization problem by using an entirely new and emerging meta-heuristic algorithm that called symbiotic organisms search, while considering the limitations of the real world in the formation of portfolio. This algorithm is inspired by the symbiotic relationship in diverse ecosystems that exist in nature, and introduced in 2014. Finally, the model used in this study has been solved with real data and the results have been analyzed. The results of this paper demonstrate that the symbiotic organism search has been successful in portfolio optimization and has been able to properly interact with the actual limitations of the market.

Portfolio selection is one of the most important areas in financial decision-making; A portfolio of stocks that could bring the highest rate of return and the lowest risk investment for its owner simultaneously. However in choosing the most prefered portfolio just these factors are not decisive and according to the economic environment, many factors can affect this process which should be employed. Therefore, these diversity of factors, bring to the limelight the importance of multi-criteria decision-making approaches. Data Envelopment Analysis (DEA) is one of this approaches. The main purpose of this paper is comparing the traditional DEA approaches to a new proposed algorithm. In traditional approaches simply assumed that return to scale is constant or variable. This simplification may cause large errors. In the new algorithm by analyzing the behavior of return to scale, appropriate model will be used. As a case study, the models have been solved with real data belonging to Tehran stock exchange and the results have been analyzed.

Uncertainty is a prominent feature of real world problems and more especially financial markets; with this in mind, dealing with uncertainty becomes a necessary part of performance evaluation by means of data envelopment analysis. This paper presents three robust data envelopment analysis (DEA) models and their application for performance evaluation in Tehran Stock Exchange (TSE). Based on the results, the evaluated performance of stocks and the number of efficient stocks is decreased in all three models by increasing the level of uncertainty.