Advances in Mathematical Finance and Application (AMFA)

In this paper, using Evolutionary Game Theory, we study the evolution of currency as an important and influential problem in the world economy by presenting three new models. In examining the evolution of two different currencies, we prove that if the parties chose either of the two currencies with a fixed probability, it will not have an impact on the evolutionary process of these currencies. Considering a new action based on either the possibility of using any two currencies or a preference between the two currencies, we illustrate their impact on the evolution-ary process of currencies. In fact, we examine the impact of different actions and strategies on the evolutionary process between the two types of currencies.

Forming a portfolio of different stocks instead of buying a particular type of stock can reduce the potential loss of investing in the stock market. Although forming a portfolio based solely on past data is the main theme of various researches in this field, considering a portfolio of different stocks regardless of their future return can reduce the profits of investment. The aim of this paper is to introduce a new two-phase approach to forming an optimal portfolio using the predicted stock trend pat-tern. In the first phase, we use the Hurst exponent as a filter to identify stable stocks and then, we use a meta-heuristic algorithm such as the support vector regression algorithm to predict stable stock price trends. In the next phase, according to the predicted price trend of each stock having a positive return, we start arranging the portfolio based on the type of stock and the percentage of allocated capacity of the total portfolio to that stock. To this end, we use the multi-objective particle swarm optimization algorithm to determine the optimal portfolios as well as the optimal weights corresponding to each stock. The sample, which was selected using the systematic removal method, consists of active firms listed on the Tehran Stock Ex-change from 2018 to 2020. Experimental results, obtained from a portfolio based on the prediction of stock price trends, indicate that our suggested approach outperforms the retrospective approaches in approximating the actual efficient frontier of the problem, in terms of both diversity and convergence.

Today, health tourism is a growing phenomenon, especially in developing countries, which has been greatly affected by the Covid 19 crisis. After overcoming this crisis and re-competing in this industry, our country also needs to develop capabilities. Take your marketing step and move towards being financially agile. Agility, or the ability to adapt quickly and in a timely manner to changing international markets at low cost and high added value, is therefore cost-effective. The present study was conducted with the exploratory mixed research method and the purpose of designing and validating the agile financial marketing capability model in the health tourism industry in Iran. The statistical population includes health tourism industry experts and prominent professors in the field of finance who have worked in medical centers that provide health tourism services. The research findings led to the identification of 14 main concepts that were presented in the form of a paradigm model and the central category of agile financial marketing capabilities (specialized and structural capabilities) was empirically examined with real data and confirmed.

Markets such as Money Market, Labor market, Commodity market along with Capital market are committed to optimal allocation of capital and financial resources. One of the most important sources of information is financial reports. Investors base their decisions on the balance between risk and return and are interested in estimating their expected future returns and investments using information reported by the company and other evidence. The purpose of this study is to investigate the relationship between accounting conservatism and cost of equity capital and evaluate the economic results of accounting conservatism through information perspective. Therefore, in order to achieve the main objective of the research, first the reports and documentation of past performance of the sample member companies have been studied and the required data have been collected. The research hypotheses were then evaluated which consisted of one main hypothesis and two sub-hypotheses, indicating that there is a negative and relatively strong relationship between conserv-atism and capital cost. Differentiating between several aspects of accounting con-servatism and examining the relationship among each aspect and cost of equity capital are new innovation of the present study. .

One of the most critical indicators of one-factor productivity is the labor productivity index. This index has various and broad applications in the dimensions of economic policy. On the one hand, the labor productivity index determines per capita income levels and living standards. On the other hand, efficiency combined with other factors such as capital stock can make technical changes. The issue of how monetary and exchange rate variables affect labor productivity has been of particular importance in recent international studies. Due to the importance of the issue, this study investigates the asymmetric effect of exchange rate and bank facility rates on labor productivity in Iran in 1971-2018. The results of model estimation by self-explanatory method with nonlinear autoregressive distributed lag (NARDL) indicate that in the short and long run, the effect of free-market exchange rates and bank lending rates on labor productivity is asymmetric so that reductions in exchange rates have a significant direct effect on labor productivity and increases are not significant. In addition, increases in bank lending rates have a direct effect and reductions have an adverse effect on labor productivity.

In order to evaluate the companies of the cement industry active in the Tehran Stock Exchange (Iran), it is first necessary to identify the indicators of the industry. Note that some of the indicator values provided by the Exchange Organization to the users may have been lost for any reason. All of the existing models for calculating the efficiency of such units calculate the weight of the indicators related to each unit under evaluation independent of the weight of other units. So, in this study, researchers decided to develop a common set of weight models and propose a model that evaluates efficiency in the presence of heterogeneity decision-making units (DMUs) based on the common set of weight models. Finally, the proposed model evaluates 25 cement industry companies in the presence of heterogeneity DMUs of indicators and the results are being analyzed.

In this research, we have tried to investigate the effect of successive news of dis-tributed profits, negative adjustment and late announcement of an adjustment on the market reaction process in Iran. Following the design of the market response evaluation indicators, the transaction information was collected from the Stock Exchange in the five-year period of 2011-2015. The statistical sample consists of 125 companies selected by systematic elimination method and a total of 625 years-firms. To investigate the research hypotheses, linear regression and correlation have been used and for analyzing the data and testing the hypotheses, Eviews software has been used. What is summarized in the overall conclusion of the test of research hypotheses is that the successive news of distributed profits has an effect on the market reaction process;moreover, negative adjustment has a negative impact in the forecast of earnings per share and late announcement of the adjustment of the earnings per share on the market reaction process.Finally, the results indicated that the market's negative reaction to the late negative adjustment of the earnings per share forecasting is not lower than timely negative adjustment of earnings per share forecasting, and the positive reaction of the market to adjust the timely positive or zero forecast of earning per share, compared to adjusting the late positive or zero forecast of earnings per share is also no greater. the results obtained in this study are partly consistent with the documentation referenced in the theoretical framework of the research and financial literature

Corporate managers can share information about the financial status and corporate future perspective with stockholders in different ways. In recent years, the corporate program for maximizing the appropriate reaction of Securities Exchange to corporate position performance and minimizing the inappropriate reaction to their negative performance has been considered by analysts and accounting researchers. In this research we use the multivariate regression model to test the hypotheses and our statistical population is Tehran stock Exchange corporate. After sampling, 64 corporates have been selected during 2014to 2018. And to test hypotheses Multivariate regression has been used According to the results obtained Managers change timing to hide The bad news is that they are using the stock market after hours after the stock market Managers while have made less use of the weekend and busy days to hide bad news The results also show that companies have used time changes to highlight good news rather than hide bad news So to investors and other users It is advisable to pay attention to the time changes of the forecast profit, to make a more correct decision.

The accounting software is considered to be of the most critical components of accounting information system, with particular significance as of accounting and financial systems. the most important problems with accounting education systems is that students do not adequately learn the financial software required by the accounting profession, which, in turn, reduces the credibility and position of the accounting profession. That the main objective of accounting software education is to educate skilled and expert accountants to enter the accounting profession, which is considered as of the success factors of country’s economy. In this study, employ data mining techniques to investigate the accuracy, precision, and recall performance measures and to predict the rate of financial software learning based on accounting students’ emotional intelligence (EI), gender and education level. Accordingly, a machine-learning-based multivariate statistical analysis is performed on 100 Iranian accounting students. The results show that emotional intelligence has the most impact on the rate of financial software learning among the variables. Gender and education level were influential. Also, among the five algorithms, the highest precision and recall are achieved by both Decision Tree and XGBoost and are presented as the most appropriate models for the prediction rate of financial software learning.

Pension funds are major concerns and future threats to the economy of de-veloping countries. In general, pension funds are among the most sensitive and complicated modern financial institutions in today’s world, as well as an important component of the national economy to achieve the goals of the social security system. The present study was only focused on the dimensions and prioritization of the influential factors in evaluating the impact of pension funds. This could be an important step toward accurate policymaking in this regard within a shorter period and with decreased costs by recognizing the important factors. This was an applied research in terms of the objectives and involved a mixed method in terms of the design. Data were collected in two consecutive stages using the descriptive survey method. The first stage involved a qualitative assessment using the content analysis method, and the second stage was carried out using a decision-making mathematical model known as the fuzzy DEMATEL method. The two stages facilitated the identification of the influ-ence and interaction of the factors and prioritized the identified indexes. The fuzzy DEMATEL technique is a simple and efficient model applied to rec-ognize and rank the most important factors. The factors were calculated based on the opinions of experts using the fuzzy DEMATEL method. Ac-cording to the results, the three most important factors in this regard were upstream rules and regulations, inflation, and investment strategies.

In the research, pattern explanation of micro and macro variables on return on stock trading strategies has been dealt with. Based on data collected, existence of momentum and contrarian strategies in Tehran Stock Exchange market has been studied. To collect data and make statistical analysis, Excel Spread Sheet software, and statistical SPSS and R software packages have been used. Through usage made of various statistical models, the relationship between variable of return on stock and other variables added has been studied so that based on which stock trading strategy would be predicted, for the next 12 months. To do so, three statistical models of autoregressive time series (with no auxiliary variable), linear regression, and Markov-switching have been applied. Using the model’s fit criteria, these three models have been compared and best of them has been selected. Based on selected model, stock trading strategy for the next 12 months has been predicted. Markov model showed that within next 12 months, using contrarian strategy i.e. selling previous winners and purchasing previous losers can be profitable. According to the research findings, from among micro variables (base volume, trade volume, institutional investment, and free float) and from among macro variables (currency and inflation rates), only three variables of the first (base volume, institutional investment, and free float) are effective on stock trading strategy; and, they can be used as auxiliary variables to predict return on stock and to specify stock trading strategy in future as a result.

In this paper a combination of two financial derivatives in financial markets modelled of future interest rates is presented and evaluated. In fact European option pricing is driven when zero-coupon bond is considered as underlying asset in a market under Hull-White model . For this purpose, the exact solutions of the valuation of this bond option are driven, using Lie group symmetries method. Then in the next part, the finite difference method is applied to find numerical solutions for assumed bond option pricing. Then the significance and usefulness of this approximated method is comparing with the exact solutions by some plotted graphs.

Since calculating the amount of fractal in the ARFIMA time series and increasing its accuracy and bring it closer to reality is very important, this article intends to investigate the possibility of modifying this computational formula by changing the focus criterion and using simulation. In the present paper, by analysing and simulating the fractal parameter for time series ARFIMA model and redefining and reviewing the Fractal mathematical, a fractal calculus and dimension in comparison with Euclidean norms introduced. In this regard, first, a new criterion about fractal or Hausdorff component for measuring the forms of fractal time series introduced, then the effects and functional inquiries using simulation data searched, and some mathematical proofs through simulation of data achieved. The findings showed that, the deviation of the new estimator from the simulated initial value is less, and closer to reality as this new criterion introduced by changing the focus criterion and replacing the mean with the median due to less sensitivity to out-dated data. The new criterion is better for determining the fractal parameter and identifying its degree of effectiveness. Finally, the findings empirically indicated that the proposed criterion is more efficient and better than the others for calculating fractal dimensions.

stock market is considered as the most profitable and valuable areas of investment in any country. In this regard, high return depends on the correct choice of stock portfolio.That’s why today different methods of mathematical planning and decision-making have been proposed to solve such problems. Aiming to present a new method, the study designates 10 criteria for selecting the best stock portfolio options among the 21 most viewed options in the stock market. The method is a combination of fuzzy SAW and experimental design (2k factorial design). Analysis of variance results for the response variable is calculated . The value of R2 obtained from the response variable of 70% value, shows that this model has selected suitable options by removing ineffective criteria and analysing the results and discovering the relationships between criteria and ranking the criteria and presenting simpler solutions in addition to high accuracy. As a result, by considering and comparing the real values of the stock market in one-month and quarterly intervals, the model presents more capabilities for providing accurate ranking and higher portfolio returns than fuzzy TOPSIS in the capital and stock markets. The response surface method and the regression equation obtained in the proposed method are used to rank the options. In addition, Pareto method, which ranks the criteria based on the effectiveness of the criteria in the final result and regard to the surfaces of experiments and weights of capital market and stock market experts, is used for ranking the factors (criteria).

In customs management, the main problem is balancing the needs of trade facilita-tion as a process of simplifying and accelerating foreign business on the one hand and countering illegal trade, reducing government revenue, capital sleep and the level of controls and interventions on the other. Also, due to the financial crisis in recent years, risk management has been reconsidered, although this attention is related to various financial branches. Since risk analysis and identification is the main component of risk management, developing a suitable model for data analysis is of particular importance. The purpose of this study was to use data data analysis techniques to develop an intelligent model to timely predict the risk of import declarations in customs and thus prevent irreparable losses. In this study, data analysis techniques have been used according to the statistical population which is data-driven. Statistical data were extracted from www.eplonline.ir with 575006 import declarations of all Iranian customs during 2019-2020. having pre-processed and prepared the data using PCA, LDA and FastICA methods, attribute reduction and effective attribute extraction were performed using 14 data analysis algorithms. Using Python software, algorithms were trained and modeled with 80% of the final data. Then, 14 obtained models were tested and validated with 20% of the data. Finally, the results of these models were compared with each other and the model obtained from the random forest algorithm was selected as a comprehensive model for predicting and determining the level of risk of import declarations at customs.

The emergence of a new theory of"macroaccounting" with a new wave of accounting research over the last decade tries to explain and use the Aggregate information of interim accounting statements in economic forecasts. Macroaccounting theory suggests that economists and macroeconomic forecasters use Aggregate accounting information at the macroeconomic level. For example, accounting earning is used to predict GDP, cost stickiness is used to predict unemployment, and the ratio of book value to market value is used to predict inflation. Earnings growth dispersion contains information about trends in labor reallocation, unemployment change, and, ultimately, aggregate output. initial macroeconomic estimates released by the Central bank of Islamic Republic of Iran and Planning and Budget Organization and Statistical Center of Iran do not fully incorporate this information. Accordingly, the present study, based on macroaccounting theory, has examined the Predicting Restatements in Macroeconomic Indicators using Accounting Information. The population of this study includes all companies listed in the TSE. Due to the seasonality of the data and the fit of the models in a time series, the observations reach 40 times (2008:1to2018:4). The research method is based on time series data, VARtechnique. the The results suggest that earnings growth dispersion provides related data about final GDP growth. The results suggest that after considering the effect of other influential factors, specifically real initial GDP, earnings growth dispersion is useful in forecasting future GDP changes. The findings are important for economists and policymakers to have more accurate economic estimation and prediction by applying for accounting Information.