The purpose of this research is to present a model of portfolio management in investment funds based on behavioral financial variables. The method of this research was applied and descriptive-survey according to its purpose. The participants in this research included experts who had a doctorate degree in financial management or accounting and university professors. The method of data collection was made by a researcher's questionnaire and analyzed using structural and interpretive modeling. The results showed that in the first level, the most effective components included: control of the fear of surviving profit, normalization of conservative behaviors and control of regret-avoidance behaviors. In the second level, the components influencing the first level, i.e. paying attention to self-control behaviors, having a written investment strategy, paying attention to the principles of mental accounting, and in the third level, the components influencing the second level, which include behavioral optimism and pessimism, Paying attention to risk-averse and risk-taking behaviors and controlling mass behaviors is the most effective component at the fourth level, which includes the effect of inclination. In the Mik Mak model, most of the variables were included in the link variables, which have a strong influence and also a strong dependence force.

The relationship between the exchange rate, stock price, housing, and coin as the items considered by the investor for portfolio management has always been a complex discussion, and the relationship between them and determining the cause of the transfer of volatilities (receiver and transmitter of volatilities) may be different in each country and different periods. According to this, in the current study, the risk spillover between currency, housing, coin, and stock markets in the period of 1385:01- 1400:12 monthly using the vector autoregressive model with time-varying parameters of Diebold-Yilmaz (DY-TVP-VAR) have been investigated. The results show that currency and gold coins are the main drivers of transferring and receiving volatilities in the investigated network. The housing market has only received the risk and volatilities of other assets, and the most volatilities have been transferred from currency and stocks to housing. Also, the stock market has received the most volatilities from currency and then coins. Based on the results, housing can provide risk hedging for the investment portfolio, and in other words, it is a safe haven, but coins relationship with other assets has been different over time; its selection should be based on other assets in the portfolio as well as political and economic conditions, and it is not a safe haven under all conditions. Therefore, during the sanctions period and in conditions where the return on assets has a significant difference from the mean, using the DY-TVP-VAR model can bring better results for investors to manage their investment portfolios.

The aim of this research is to present a model for valuing new industrial projects in Iran's automotive industry. In this regard, a mixed-methods approach (qualitative and quantitative) was utilized. The qualitative part of the research employed grounded theory and involved interviews with 15 professors, experts, and specialists in the fields of valuation and automotive industry, using purposeful sampling to design the conceptual model of the research. In the next phase (the quantitative phase of the research), a fuzzy Delphi approach was used to screen and validate or reject these categories. The results showed that all indicators received a score higher than 0.7, thus confirming all 37 identified sub-categories by the experts. Subsequently, the fuzzy TOPSIS approach was applied to prioritize each of the sub-categories within the framework of the main categories. The results of this section also indicated that in the causal factors section (idea generation), the highest scores were related to organizational business policies and customer needs assessment. In the contextual factors section, the highest scores were related to market structure and product structure, respectively. In the intervening factors section, prioritization was given, in order, to the company's capabilities, market and stakeholders, and laws and regulations. The main categories were prioritized as including product portfolio management and quality management. The actions and interactions of the model also included process screening indicators, product value engineering, financial screening, economic screening, and after-sales services. Finally, the outcomes prioritized included initial prototyping, initial market testing, and pilot production.

This study raises concerns about the potential of Bitcoin as a new alternative investment. This article aims to analyze the properties of Bitcoin in terms of speculation, diversification, hedging, safe haven, and it is qualified as efficient in a well-diversified portfolio during the COVID-19 epidemic using the GARCH model, the DCC model -GARCH, and the BDS test for the period from March 1, 2019 to March 31, 2022. The results indicate that Bitcoin can play an important role in portfolio diversification and that it can serve as a speculative asset. However, the authors argue that Bitcoin is a safe haven, a hedge, and is efficient. The conclusions of our results have important implications for investors and market participants who are deeply concerned about investment strategies and risk management.

This study investigates the effect of diversification in the underwriting activities and insurance portfolio on the level of risk-taking in the investment activities of insurance companies. According to the coordinated risk management hypothesis, it is expected that there is a negative relationship between investment risk and underwriting risk of insurance companies. To examine the effect of insurance portfolio diversification as independent variable on the level of asset risk-taking as dependent variable, ordinary least squares and two-stage least squares regression models were used. Also, alternative measures of diversification and asset risk-taking, and an event study were used to do a robustness check and support the results. The research sample includes 29 direct insurance companies and covers the period from 2006 to 2023. The findings showed that consistent with coordinated risk management hypothesis, insurance companies that have a more diversified insurance portfolio take more asset risk than those with a less diversified insurance portfolio. The results were robust to corrections for alternative diversification and asset risk measures and potential endogeneity bias of variables.

Portfolio optimization is a widely studied problem in financial engineering literature. Its objective is to effectively distribute capital among different assets to maximize returns and minimize the risk of losing capital. Although portfolio optimization has been extensively investigated, there has been limited focus on optimizing portfolios consisting of cryptocurrencies, which are rapidly growing and emerging markets. The cryptocurrency market has demonstrated significant growth over the past two decades, offering potential profits but also presenting heightened risks compared to traditional financial markets. This situation creates challenges in constructing portfolios, necessitating the development of new and improved risk management models for cryptocurrency funds. This paper utilizes a new risk measurement approach called Conditional Drawdown at Risk (CDaR) in constructing portfolios within high-risk financial markets. Traditionally, portfolio optimization has been approached under certain conditions, considering risk and profit as decision criteria. However, recent approaches have addressed uncertainty in the decision-making process. To contribute to the advancement of scientific knowledge in this field, this paper proposes a new mathematical formulation of CDaR based on a chance-constrained programming (CCP) approach for portfolio optimization. To demonstrate the effectiveness of the proposed model, a practical empirical case study is conducted using real-world market data from 10 months focused on cryptocurrencies. The results obtained from this model can provide valuable guidance in making investment decisions in high-risk financial markets.

In May 2017, the International Accounting Standards Board (IASB) published International Financial Reporting Standard No. 17 (IFRS 17), titled Insurance Contracts. IFRS 17 establishes principles for the recognition, measurement, presentation, and disclosure of insurance contracts, reinsurance contracts, and investment contracts with discretionary participation features. The adoption of IFRS 17 can significantly impact financial management frameworks, profit, equity, reserves, financial reporting processes, and actuarial models. IFRS 17 introduces a novel approach to determining the discount rate for insurance liabilities, which is a critical component in calculating the present value of future cash flows associated with insurance contracts. The primary objective of this study is to develop a framework for estimating the discount rate of insurance contracts in accordance with IFRS 17 in Iran. This research is applied in nature and employs a descriptive, survey-based method for data collection. A questionnaire served as the data collection tool. The statistical population comprises financial managers, actuaries, internal auditors, and risk managers, with a total of 85 valid questionnaires received. Group mean comparison tests and analysis of variance (ANOVA) were used to evaluate the opinions of different respondent groups regarding the research objective.The results of the mean comparison indicate that with the exception of propositions related to selecting the rate of government debt bonds as the market rate and choosing a reference portfolio based on theoretical assets, other propositions such as incorporating liquidity features into insurance contracts, the availability of observable affordable price data in financial instruments, the use of market-based inputs to determine the discount rate, the application of top-down and bottom-up approaches in setting the discount rate, the annual determination of the discount rate for insurance contracts, the sensitivity of insurance liability assessment results, and the determination of the discount rate by the board of directors were found to be significant. Additionally, the results of the variance analysis and Tukey’s test revealed significant differences among the opinions of respondent groups concerning the liquidation claims of health insurance, property insurance, and liability insurance; the use of a customized portfolio as a reference portfolio; the alignment between the maturity of real assets in the reference portfolio and the liabilities of insurance contracts; and the use of the technical interest rate or accrual rate to calculate insurance premiums in life insurance.

Given the occurrence of international uncertainties and their direct impact on individuals' investment portfolios, investors, based on their preferences, tend to accept the minimum level of risk and diversify their risks while maximizing returns. In such circumstances, in the face of uncertainty, identifying safe assets becomes crucial. The present study examines the risk transfer among Bitcoin, gold, the dollar, and fixed-income funds in Iran, considering geopolitical risk. Daily data from August 2, 2021 to October 21, 2023 are used, employing the TVP-VAR approach. Initially, by investigating heterogeneous assets in the portfolio, the study identifies the relationships among the assets. Subsequently, by adding geopolitical risk to the model, the safest asset in the portfolio is re-determined. Safe assets are then declared in the face of geopolitical risk, and investments in these assets are recommended if necessary. The results indicate that gold has been a safe asset compared to other portfolio assets during times of uncertainty and has been able to impact the overall network. Additionally, Bitcoin is introduced as a safe asset in the face of geopolitical risk.

The goal of investors in forming a stock portfolio is to obtain the highest return for bearing the lowest risk and portfolio optimization is one of the most complicated problems in the field of finance and investment. It is an NP-hard problem, and in general there is no definite method in polynomial time to find an exact solution for it. In this research, to solve the problem of choosing the optimal stock portfolio, the multi-criteria decision making method has been used under conditions of uncertainty. In order to implement the algorithm and evaluate it, the monthly returns of the Tehran Stock Exchange indices were used between 2018 and 2013. The results can be examined from two different perspectives. From an analytical and technical point of view, the results can be discussed. From a technical point of view, presenting a new technique for doing things can give the capital market participants the confidence that they can choose a stock portfolio using a new tool. From an analytical point of view, the existence of decision making algorithms in providing the optimal portfolio is a new step that can be used in the combination of fundamental analysis and the use of dynamic stock portfolio.

Portfolio selection has been recognized as one of the most significant and challenging problems in financial engineering since Markowitz’s pioneering work on the mean-variance model. This problem centers on the optimal allocation of wealth across a set of assets to maximize returns while minimizing investment risk. While the basic Markowitz mean-variance framework is theoretically elegant and foundational, it has faced criticism from investment practitioners due to its reliance on unrealistic assumptions that limit its practical applicability. Specifically, the traditional model assumes perfect market conditions and neglects real-world constraints, such as the need to limit the number of assets in a portfolio (cardinality), which can significantly reduce its practical applicability. To address these limitations, this paper extends the mean-variance portfolio selection model by incorporating cardinality and floor-ceiling (quantity) constraints. The cardinality constraint ensures that the portfolio includes a specified number of assets, while the floor-ceiling constraint regulates the allocation to each asset, restricting it within predefined bounds. These added constraints transform the classical quadratic optimization problem into a mixed-integer quadratic problem, which necessitates the use of approximation algorithms such as metaheuristic algorithms for efficient and feasible solutions. Although numerous metaheuristic algorithms have been employed to tackle this problem, genetic algorithms have gained prominence due to their balance between solution quality and computational efficiency. However, the standard genetic algorithm is not without its shortcomings, particularly when handling the complexity of constrained portfolio optimization. To overcome these limitations, we propose a novel crossover operator designed to enhance the performance of the genetic algorithm.