نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 58 (بهار 1403)

This research has been done in order to predict stock returns at the company level: an application of linking asset pricing models and economic factors, in 130 selected companies admitted to the Tehran Stock Exchange between 2011 and 2018. In this research, an attempt has been made to propose a model for predicting future performance by combining a multi-factor model and a research model. The results showed that 1) anomalies in stock returns were evaluated and according to the results obtained based on the Gibbons test, it is the only model based on the proposed research model that is able to explain anomalies in stock returns unlike the multi-factor models (single, three, four and five agent) 2) Due to the complexity and conflict in the relationship between explanatory variables and future performance, the power to explain the proposed research model in predicting anomalies in accruals and research and development costs to the value of the company, is weaker than multifactorial models that use of The integrated model of research (using profitability, returns based on the lowest and highest price) as well as the proposed model have shown a better explanatory power in this field.

In recent years, data mining, particularly the support vector machine, has gained considerable interest among investors, managers, and researchers as an effective means of bankruptcy prediction. However, studies indicate that it is highly sensitive to the selection of parameters and input variables. Hence, the aim of this research is to improve bankruptcy prediction accuracy by combining an advanced support vector machine model with the k-nearest neighbor approach to eliminate erroneous entries. To achieve this, first, by using five financial ratios: current ratio, net profit margin, debt ratio, return on assets, and return of investment from 150 companies listed on the Tehran Stock Exchange during the 10-year period (2010-2019), and k-nearest neighbor algorithm, the training data will be refined. Then, relying on a support vector machine based on classification penalty, a prediction model will be constructed. The parameters will be estimated, and its validity will be assessed using test data. Finally, a comparison will be made between the outcomes of the proposed model and traditional models.The findings demonstrate that the combination of the k-nearest neighbor models and support vector machine reduces the overall prediction error, and the penalty coefficients of the support vector machine exhibit a high level of statistical significance.

The productive credit certificate (GAM) is one of the new innovations of the country's monetary and financial system for financing production units, which can improve the access of production units to working capital, and cause the development and prosperity of production of these units. By benefiting from the capacity of the country's private sector, which are in the form of production supply chains, and with the guarantee of banks and credit institutions, these bonds will provide the necessary sources of working capital.In this article, based on the analytical-descriptive method and using library sources, the risks of GAM bonds have been identified for each of the elements involved in the structure of these bonds and divided into two general categories: primary market and secondary market, and then using the FUZZY TOPSIS method. The identified risks were prioritized.The findings of the research show that among the different types of risk, in the primary market, the risk of validation committed by the bank, and in the secondary market, the risk of inflation, the risk of exchange rate fluctuations, and the risk of interest rate are the important risks of GAM bonds, respectively.

The purpose of this study is to investigate the ability of mutual fund investors to predict the performance of mutual funds and select a fund with higher returns. To predict whether investors and the cash they enter into investment funds will be able to select a more efficient fund, first, the information of 34 funds in the period 2012 to 2020 was Collected to examine the intelligence of cash. The economic prosperity periods of the market were then examined separately to determine whether investors had acted differently in different market periods. Also, real and legal investors are separated from each other to examine the performance of each separately. Using Carhart's four-factor model, the adjusted return on risk of the funds was calculated to assess the intelligence of investors, which showed that group of real and legal investors, at the time of entry during market blooming and at the time of exit money during market stagnation act wisely in mutual funds.

Identifying the distributional behavior of returns of risky assets is one of the necessities that has attracted the attention of many researchers. Because a more accurate knowledge and understanding of the distribution behavior of returns in them allows for more accurate predictions of the future state of the market, especially in determining the risk-exposed value of these assets, which has a direct relationship with the distribution form of returns. The aim of the current research is to investigate the distributional changes of financial asset returns in the periods before and after covid-19 based on power law, stretched exponential function and Gaussian q-functions.In this regard, 3 variables: stock market index, gold price and exchange rate were investigated and their related Information was collected in each of the trading days during the period of 2016-03-26 to 2023-01-19 .In order to test the hypotheses, by using the Kolmogorov-Smirnov test, the empirical distribution of returns was compared with each of the mentioned distributions. The results showed that the logarithmic distributions of these assets do not follow any of the probability distributions obtained from the power law, stretched exponential and q-Gaussian.

The purpose of this research is to choose the composition of the bank's foreign exchange portfolio to reduce the risk of managing net open positions (NOP). This realization aims to answer the question of whether it is possible to optimize the currency portfolio based on different value-at-risk models, including Var, Copula VaR, and Copula CVaR. In this research, on a quantitative scale and observations in the form of time series, the daily logarithmic return percentage of the two main and common currencies in the country's business, including the US Dollar and the EURO, in Sepah Bank from 2013 April 6 to 2021 September 22.The results of the selected portfolio based on these three methods show that, according to the obtained results, the Copula GARCH VaR model has a higher Sharpe value than the other two methods. On the other hand, the results of the average Sharpe obtained from the portfolios on the efficient border between the three methods are presented, according to the obtained values, we find that there is a significant difference at the 95% statistical confidence level between the average of the three methods presented, during which the Copula model GARCH VaR is ranked higher.

The interaction between financial derivatives and their underlying physical markets has been the subject of extensive research in finance. This article examines the effect of dynamic contagion between the gold futures market and the physical gold market in financial markets and Tehran Stock Exchange, period of 08/29/2009 and 09/05/2018. It specifically focuses on the oscillating cycle that occurs between these two interconnected domains. Data is collected daily. Using a comprehensive data set covering a significant period, advanced econometric techniques from GARCH-BEKK, Markov-Switching and Structural VAR models to analyze volatility dynamics and contagion between gold futures and We use the physical gold market. Our findings show the existence of a two-way relationship, in which there is a contagion effect of turbulence from the physical gold market to the coin futures market. Also, another result of the research is that the contagion effect of turbulence from the physical gold market to the coin futures market is different in different regimes.

Exchange-traded funds (ETFs) are an important innovation in the global financial markets, where there is still the possibility of incorrect pricing and creating arbitrage opportunities in these funds. Since the investment units in ETFs are traded in the same way as stocks, the transactions of these funds at a price other than their intrinsic value may be considered as their disadvantages. Considering the short history of these funds in Iran and the importance of their pricing efficiency, this research has taken a step to investigate this issue in the capital market of Tehran. In order to achieve the above goal, six funds (equity, index and fixed income) were selected. Daily data of a 6-year period (2016 to 2021) has been used for analysis. The results show that the performance of the selected funds was better than the performance of the whole market index. Also, the results showed that the factors affecting the Tracking Error and Premium (discount) in different funds provide different results and price inefficiency is also evident in these funds.

Central Bank Cryptocurrency (CBDC) is a semi-decentralized solution based on DLT. This money is issued electronically by central banks to facilitate payments without intermediaries. this research aims to model CBDC and is conducted with a mixed approach (qualitative and quantitative methods). In the qualitative part, a conceptual model was designed by asking the opinions of 20 experts through an open questionnaire and a coding approach was tested by a sample of 102, banking and payment experts. In the quantitative part, the reliability and validity features of the questionnaires were investigated, and in the structural part, the model coefficients were evaluated to check the research hypotheses. The results of this research, while determining many factors affecting the circulation of the central bank's currency, confirm that the circulation of money is the basis for the social acceptance of this phenomenon and emphasizes the necessity of its precise policy. Some background and intervening factors are also identified in the model. On the other hand, the consequences of publishing cryptocurrency in this model are influencing monetary policy, other businesses, banks, and some other micro and macro factors.

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.

For long-term savings purposes, cryptocurrency is considered a controversial asset because the value of the cryptocurrency changes significantly several times during the week. Despite the fact that the value of the cryptocurrency has changed since the beginning of the year, the cryptocurrency may lose a lot of its value. Cryptocurrency is an innovation that has created many questions even for experts in various industries. In some countries, cryptocurrency has not only become a means of payment, but also an investment tool that is included in the investment portfolio of cryptocurrency fans. In this article, focusing on cryptocurrency as a source of value, cryptocurrency is examined from the perspective of the role of money. Cryptocurrency can be useful as a form of money and has the characteristics of money such as divisibility, durability, dynamism and other features. The function of cryptocurrency is not based on physical assets such as gold and silver, but on mathematical principles. The actual price of a cryptocurrency is determined by the demand from people who want the coins and

The main purpose of this experimental research was to investigating the relationship between the policy of marketing analysis capability of goods and services and creating a Sustainable competitive advantage based on the of artificial intelligence adoption in manufacturing companies. The research method is of the correlation studies type, and in terms of the goal, it is considered an applied research. The statistical population of this study was managers and experts of production (industrial) companies in Tehran province. The questionnaire of Afnan Hossein et al. (2022) was used to collect and compile information, and for the purpose of its validity analysis, expert opinions and convergent validity were used. Also, the reliability of the data collection tool of this research was analyzed through Cronbach's alpha criteria and composite reliability and it was confirmed. In addition, SPSS and Smart PLS statistical software have been used in this research to examine the conceptual-experimental framework of the research. In general, the results of the research showed that there is a positive and significant effect of marketing analysis capability policy on sustainable competitive advantage.