Advances in Mathematical Finance and Application (AMFA)

Nowadays, marketing researchers are constantly striving to identify consumer behavior and therefore to find appropriate solutions for better and more effective sales and increase market share. In this regard, the purpose of the present study is the role of customer clustering in designing a targeted marketing model. The research method is applied and exploratory. The statistical population of the study was in the qualitative part of sales and marketing managers of IoT companies who were selected by non-random sampling method and 15 people were interviewed. The quantitative part also included all the customers of the companies surveyed. Due to the unlimited population of Morgan Table 384 persons were selected as the sample size. Data gathering tool was interview and questionnaire, which were used to assess the validity of the questionnaire by the opinions of marketing experts and Cronbach's alpha reliability. Content analysis approach was used to analyze the data in the qualitative part and PLS2 software in the structural part. The results showed that the dimensions of the model in the four main clusters were communication factors, behavioral factors, individual factors, and economic factors. Model performance is very high performance.

The present study aimed to rank banks in terms of board of directors report and notifying the users of reports. In addition, we evaluated factors affecting risk disclosure from the perspective of corporate governance. Moreover, we assessed risk disclosure based on linguistic analysis of board report text and capital adequacy ratio. Words were applied as measurement units to measure risk disclosure. The advantage of this technique is the unique analysis of words. According to the theoretical foundations presented in the present study, we first identified risk disclosure words in reports provided to financial information users and divided them into two categories of positive and negative risk disclosure words. Another variable selected for risk disclosure was capital adequacy ratio. Effective variables in corporate governance system in banks included the board independence, duality of CEO duties, and major shareholders as input variables in data envelopment analysis (DEA) model. On the other hand, the BCC model of DEA was selected as output-based nature. The statistical population included all banks listed in Tehran Stock Exchange. In total, 20 year-bank units listed during 2016-2017 were assessed. In the end, seven year-bank units were considered efficient while the rest were inefficient. Moreover, we estimated the amount of shortage in outputs to reach inefficient banks to the desired level of efficiency.

In a volatile economic environment, financial decision making is always associated with risk. Bankruptcy, as one of the most important risks, has a significant impact on the interests of the firm's stakeholders, so presenting appropriate bankruptcy forecasting patterns is of the utmost importance. In this study, after reviewing the theoretical literature and selecting the financial ratios used in previous bankruptcy prediction models as the variable input of the initial model, the financial ratios were adjusted based on the price index and then, using the LARS algorithm, the ratios that have the highest ability to differentiate between bankrupt and non-bankrupt firms were identified, and finally, using the SVM and Naive Bayesian algorithms, the final bankruptcy prediction model was developed. For this purpose, the data of 50 companies listed in Tehran Stock Exchange who had experienced bankruptcy for at least one year from 2008 to 2018 under Article 141 of the Commercial Code were used. The results show that the adjusted financial ratios based on the price index in the model presented by SVM algorithm can be a very good predictor for bankruptcy of companies with an accuracy of 99.4%.

Studying stock options is still a pristine area of research in the Iranian capital market. This is due to the lack of data as well as the complexity of valuation methodologies. In the present paper, using the Monte-Carlo simulation, we have estimated the value of stock options traded on Tehran Stock Exchange and examined whether the use of a control variate or antithetic variate augmented methods can lower the standard error of estimates. Furthermore, the estimated values of the three models under consideration, including crude Monte-Carlo, control variates augmented Monte-Carlo, and antithetic variates augmented Monte-Carlo are compared with each other and with options market prices. The results show that the standard error of the antithetic variate method is less than the crude method and control variate method. However, the control variate augmented Monte-Carlo model is more powerful than the crude Monte-Carlo and antithetic variate augmented Monte-Carlo method. Therefore, we can conclude that the control variate augmented Monte-Carlo model has a better performance in estimating the value of trading stock options and its estimated values are closer to the market prices.

In this article, at first standard linear Black-Scholes model and then some nonlinear Black-Scholes models will be considered and thereupon alternating direction explicit (ADE) method is applied firstly for solving the standard Black-Scholes model and then for Barles and Soner model which is one of the most complete and comprehensive nonlinear Black-Scholes models. Furthermore, the stability of this method has been considered and its accuracy will be compared with other numerical methods such as finite difference methods. Since in solving nonlinear Black-Scholes models by the ADE methods, we need to solve only some scalar nonlinear equations instead of a full nonlinear system of equations that we should solve in implicit methods, so this method can be a suitable choice for solving such models.

The Malmquist Index (MI) is a tool for analyzing the productivity. Considering its importance, different suggestions and studies have been offered on the Cost Malmquist Productivity Index (CMPI) according to existing conditions of decision making units (DMUs) and the available data. The present research aimed to provide a CMPI in a non-competitive environment in which the price data changes from one evaluation unit to another. Given the deficiency of Farrel’s cost efficiency (1957) and also the cost efficiency model presented by Tone (2002), and by taking advantage of the idea of changing the productivity of DMUs at different time periods, we presented cost Malmquist Productivity Index in the presence of non-identical prices for various DMUs, Then, we evaluate the data of the Iranian Stock Exchange by aforementioned Cost Malmquist Productivity index

The most comprehensive criteria for evaluating management performance is economic value added, accounting added value, and over-valuation, which can best reflect how managers operate because of the information content that they provide. Therefore, considering the importance of earnings, this study investigates The Role of Earnings Management in Economic Growth and the Corporate’s Growth Illusion in Tehran Stock Exchange during the period 2012-2018 using systematic elimination method of information of 150 selected companies. The study data and theoretical foundations were collected through library studies. Hypotheses were tested using correlation method and multivariate regression. The results showed that with increasing in real earnings management, economic value added and accounting added value also increased. Also, with the increase in revenue earnings management, economic value added and accounting added value also increase. But there was no significant relationship between accrual earnings management and income with accounting added value. There is a positive significant relationship between actual earnings management resulting from abnormal and overvaluation operating cash flows and, there is a positive and significant relationship between accrual earnings management and overvaluation.

In this paper, we present an improved version of the Ramanathan [R. Ramanathan, ABC inventory classification with multiple-criteria using weight linear optimization, Computer and Operations Research, 2006, 33, 695-700] and Zhou and Fan models [P. Zhou and L. Fan, A note on multi-criteria ABC inventory classification (MCABCIC) using weighted linear optimization, European Journal of Operation Research, 2007, 182, 1488-1491]. The model that Ramanathan [1] offered, hereafter the R-model, in spite of its advantages may be led to a situation in which the weights of some criteria regarding an item would not play any role in determining its overall score. On the other hand, for the R inventory items, the Zhou and Fan [2] approach, hereafter the ZF-model, may be resulted to a situation where an item with the high value for an unimportant criterion is inappropriately classified as class A. Moreover, none of the above studies take into account the ranking order (RO) of criteria. Hence, in order to remove drawbacks of both approaches, an integrated model based on criteria importance through inter-criteria correlation (CRITIC) is applied. At last, the proposed method is implemented in an illustrative example and the results are compared with the others.

The collapse of large corporations such as Enron and WorldCom caused much concern about the ethical behavior of accountants among users of accounting service. Ethical behavior in accounting is one of the distinguishing features of accountants, which enable them to make the best decisions for stakeholders. Professional ethics learning plays a major role in the development of ethical behavior. Also, TOPSIS method is a well-known methodology that is applied in a various decision making problems. On the other hand, in order to determine the importance of criteria, the Entropy method is known as a powerful tools. Therefore, the purpose of this study was to investigate the effect of ethics teaching methods on the development of ethical behavior in accounting. This is done by means of an integrated Entropy/TOPSIS based Model for Financial prioritization of professional ethics teaching methods in accounting. The findings of the study showed that the variables of ethics education, style of teaching ethics and ethics textbook content respectively had a 1, 24 and 55 percent impact on the development of ethical behavior in accounting. The results of this study indicate that an ethical training program in accounting is needed to provide accountants with guidance on ethical issues and the importance of ethical standards.

In this paper, we present an efficient and accurate method for calculating the Black-Scholes differential equations and solve the Black-Scholes equations using Jacoby and Airfoil orthogonal bases, with the collocation method. The Black-Scholes equation is a partial differential equation, which describes the price of choice in terms of time and the collocation method is a method of deter-mining coefficients. Then we show the computational results and examine the performance of the method for the two options, the price of basic assets and its issues. These results show that the Jacoby method is more efficient in solving the Black Scholes equation, and the method error is less and the convergence rate is higher. In this paper, by applying numerical methods to the desired equation, nonlinear devices can be solved by nonlinear solution methods, such as Newton's iterative method. The existence, uniqueness of the solution, and convergence of the methods are examined, and we will show in an example that by repeating then |u n+1-u n |/|u n | <ε can be reached and this indicates the accuracy of the response to these methods.

The present study investigates effective governance and quality of regula-tions on financial development in Iran's current economic conditions. For this purpose, the model is estimated based on the annual data of 1996-2018 using Smooth Transition Autoregressive (STAR). The results of estimating the linear part of the model (first regime) show that the variables of GDP, role or the rule of law, quality of regulations, and government size have a significant and positive impact on Iran's financial development at 95% confi-dence level. Also, the variables of devaluation of the national currency and financial crises have a negative impact on financial development in the Irani-an economy. Besides, the results of the non-linear part of the model (second regime) show the existence of a positive relationship between the variables of role or the rule of law and GDP with financial development. The sign of the variables of quality of regulations, government budget deficit, govern-ment effectiveness, devaluation of the national currency, nuclear sanctions, and financial crises are negative that is expected because Iran is developing and growing. The positive sign of the lag of the dependent variable of the financial development index shows the country's attention to the issue of financial development and the use of solutions and attention to infrastructure to increase financial development over time, which needs more attention from government officials.

This research was conducted with the aim of explain and prioritize information disclosure factors related to sustainable development accounting with fuzzy approach" in 2019 of companies active on the Iranian Stock Exchange. Qualitative data were obtained through the study of research and credible sources in the field of sustainable development accounting and using coding through content analysis, the initial variables of the model were identified; 61 indicators were extracted from the initial codes in 4 dimensions including: environment, social factors. Economic and governance factors were categorized. In the quantitative section, the statistical population of the study included knowledgeable and professional and academic experts in the field of ac-counting. Using the targeted sampling method, 25 experts were selected as statistical samples. In the quantitative part, using fuzzy Delphi technique in one step, the indicators were screened. In the next step, prioritization of criteria and sub-criteria was done by hierarchical analysis method with fuzzy approach; among the main criteria, environmental dimension with weight 0.405 in rank 1 The social dimension with a weight of 0.296 was ranked 2nd, the economic dimension with a weight of 0.186 was ranked 3rd and the leadership dimension with a weight of 0.113 was ranked 4th. Finally, based on the calculated final weight, the strategic approach to environmental impacts with a weight of 0.955 in the first place, management and efficiency in consumption in the second place, social development and humanity in the third place and management Waste and waste came in fourth.

Financial Mathematics is the application of mathematical methods to financial problems. It is shown that p-valent functions play important roles in Financial Mathematics. In this paper, we define a new subclass of meromorphically p-valent functions by using q-derivative operator and fractional q-calculus operator. We obtain some geometric properties of coefficient estimates, extreme points, convex linear combination, radii of starlikeness and convexity. Finally, ε-neighborhood property will be investigated. Financial Mathematics is the application of mathematical methods to financial problems. It is shown that p-valent functions play important roles in Financial Mathematics. In this paper, we define a new subclass of meromorphically p-valent functions by using q-derivative operator and fractional q-calculus operator. We obtain some geometric properties of coefficient estimates, extreme points, convex linear combination, radii of starlikeness and convexity. Finally, ε-neighborhood property will be investigated.

Stock market forecasting is a challenging task for investors and researchers in the financial market due to highly noisy, nonparametric, volatile, complex, non-linear, dynamic and chaotic nature of stock price time series. With the development of computationally intelligent method, it is possible to predict stock price time series more accurately. Artificial neural networks (ANNs) are one of the most promising biologically inspired techniques. ANNs have been widely used to make predictions in various research. The performance of ANNs is very dependent on the learning technique utilized to train the weight and bias vectors. The proposed study aims to predict daily Tehran Exchange Dividend Price Index (TEDPIX) via the hybrid multilayer perceptron (MLP) neural networks and metaheuristic algorithms which consist of genetic algorithm (GA), particle swarm optimization (PSO), black hole (BH), grasshopper optimization algorithm (GOA) and grey wolf optimization (GWO). We have extracted 18 technical indicators based on the daily TEDPIX as input parameters. Therefore, the experimental result shows that grey wolf optimization has superior performance to train MLPs for predicting the stock market in metaheuristic-based.