Advances in Mathematical Finance and Application (AMFA)

Despite the growth of financial technologies (FinTechs) and their impact on transforming financial services, the implementation of fintech for gaining a competitive advantage in banks appears essential. The aim of this article is to model and determine the relationships and relative importance of the dimensions and components of implementing fintech in the financial-banking system. To this end, the research was conducted with a qualitative and quantitative approach in 2023. The research community included 12 experts, comprising managers and senior specialists from state banks, who were selected purposefully and theoretically. The data collection tools were semi-structured interviews for the qualitative section and a pairwise comparison questionnaire for the quantitative section. The results of the qualitative section, analysed through content analysis, indicated that the model includes dimensions such as "infrastructure and technical", "organizational policies", "products and services", "market", "marketing and sales", "economic", "human resources" and "ecosystem." The quantitative results, analysed using fuzzy DEMATEL, showed that "products and services" is the most influential and critical dimension in the model, suggesting that the success of implementing the model depends on improving this dimension. "Ecosystem" is the most impactful dimension with the strongest relationship in implementing the model, leading to the model's improvement. The findings of the network analysis process also indicated that the components of "product and service diversity", "security and privacy protection", "product and service adaptability" and "creating a platform and sharing it" are the most important. Based on the results, it is recommended that banks invest in those financial technologies (fintech) that can quickly respond to the desires and needs of their customers for successful fintech implementation.

Tax fraud is the legal minimization of tax liability through appropriate financial planning techniques, which often involve techniques, accounting methods, and fraudulent transactions, and pursue little or no purpose other than creating a tax advantage. There is no doubt that tax fraud is a legal way to reduce the amount of tax, and individuals with different personality traits can take dissimilar procedures to reduce the amount of tax. In this study, the impact of personality traits on tax fraud concerning gender (male and female) and type of firm (listed and non-listed) is investigated. The statistical population of the study includes all financial managers of listed and non-listed firms in 2020 who are not exempt from taxes. Information about personality traits was collected through a questionnaire and SPSS software version 21 was used to test the hypotheses. The results suggest that the personality traits of neuroticism, extraversion, flexibility (openness to experience), and agreement have a positive and significant effect on tax fraud. In contrast, the personality trait of conscientiousness has a negative effect on tax fraud. This effect was also observed in both male and female financial managers. The results also indicate that the effect of personality traits of neuroticism, extraversion, and agreement on tax fraud in the managers of listed firms is positive and the effect of personality traits of flexibility (openness to experience) and conscientious is negative on tax fraud, but the effect of personality trait of neuroticism is negative and the effect of flexibility (openness to experience) on tax fraud in listed firms is not statistically significant. In non-stock firms, personality traits of neuroticism, extraversion, flexibility (openness to experience), and agreement have a positive and significant effect on tax fraud, and the personality trait of conscientiousness has a negative effect on tax fraud.

In this paper, the European option pricing is done using neural networks in the Black-Scholes-Vasicek market. The general purpose of this research is to compare the accuracy of neural network and Black-Scholes-Vasicek models for the pricing of call options. In the sequel, the finite difference method is applied to find approximate solutions of partial differential equation related to option pricing in the considered market. In the design of the artificial neural network required for this research, the parameters of the Black-Scholes-Vasicek model have been used as network inputs, as well as 720 data from the daily price of stock options available in the Tehran Stock Exchange market (in 1400) as the network output. The approximate solutions obtained in this article, which were carried out by two methods of neural networks and finite differences on the Tehran stock exchange based on the daily price of stock options, are shown that neural networks are more accurate method comparing with finite difference. The comparison of pricing results using neural networks with real prices in the assumed market is presented and shown via diagram, as well. In this paper, the European option pricing is done using neural networks in the Black-Scholes-Vasicek market. The general purpose of this research is to compare the accuracy of neural network and Black-Scholes-Vasicek models for the pricing of call options. In the sequel, the finite difference method is applied to find approximate solutions of partial differential equation related to option pricing in the considered market. In the design of the artificial neural network required for this research, the parameters of the Black-Scholes-Vasicek model have been used as network inputs, as well as 720 data from the daily price of stock options available in the Tehran Stock Exchange market (in 1400) as the network output. The approximate solutions obtained in this article, which were carried out by two methods of neural networks and finite differences on the Tehran stock exchange based on the daily price of stock options, are shown that neural networks are more accurate method comparing with finite difference. The comparison of pricing results using neural networks with real prices in the assumed market is presented and shown via diagram, as well.

Financial development has been the main factor for economic development in different countries, and the causal relationship between financial development and economic development is part of the macroeconomic relations that have been examined many times. Yet studies on fishermen's poverty have rarely been done. Many residents of coastal villages are engaged in fishing activities. Small-scale fishing on various coasts in the north of Iran is an important source of employment, income and nutrition for coastal villages. This characteristics and effects have not been well examined. This study has investigated the poverty of Iranian fishermen in Gilan, Mazandaran and Golestan provinces due to changes in financial development.. The study was based on the Threshold Vector Auto Regression (TVAR), between 2000 to 2020. The research results confirm the existence of a nonlinear relationship between financial development and poverty. A significant relationship between financial development and income distribution was also confirmed on the poverty of northern Iranian fishermen.

Momentum strategies, due to their strong performance, are common investment methods designed based on the continuation of past asset performance. However, these strategies face sharp declines in high volatility conditions and market reversals. In this research, the impact of Constant Volatility Scaled Momentum (cMOM) and Semi-Constant Volatility Scaled Momentum (sMOM) strategies is examined using data from 100 stocks that constitute a significant portion of the Tehran Stock Exchange market value during the years 2013 to 2024. These strategies aim to reduce risk and improve risk-adjusted returns by adjusting for recent volatility. The results show that sMOM outperforms cMOM in factor-spanning tests and acts as a complement to traditional momentum. Moreover, its strong correlation with traditional momentum and its relative independence from market risk were confirmed in this study. These findings indicate that volatility adjustment does not always lead to performance improvement, and market conditions play a crucial role in the efficiency of these strategies. The results demonstrate that neither the constant volatility nor the semi-constant volatility scaled momentum strategies consistently outperform one another.

Taxes serve as a vital source of funding for governments and significantly influence economic growth and income distribution, which varies based on a country's level of development and economic framework. Recently, researchers have introduced methods to identify effective factors in preventing tax evasion. Most of these methods rely on basic techniques such as regression, structural equations, and non-intelligent methods that cannot effectively analyze the relationship between the values of the variables involved in this field. Therefore, in this study, we introduce a method that leverages artificial intelligence techniques, specifically a meta-heuristic optimization approach and a perceptron neural network. This method effectively analyzes the nonlinear relationships within the data while addressing both the intrinsic and extrinsic aspects of the data dimensions simultaneously. The research sample consists of 25 experts from the Tax Affairs Organization, and the study was conducted in the year 2022. The results indicate a high prediction accuracy of approximately 98%. Additionally, it highlights the significant role of various factors contributing to tax evasion, including income concealment, money laundering, economic crises, political trust, the weaknesses, and complexities of tax laws and regulations, inadequate clarification of tax laws, contradictions in legal tax articles, administrative bureaucracy, and an inefficient tax structure.

The excess volatility puzzle refers to the observation of returns that cannot be explained only by fundamentals, and this research attempts to explain such volatilities using the concept of endogenous jumps and modelling them based on the generalized Langevin equation. Based on stylized facts, price behaviour in financial markets is not simply a continuous process, but rather jumps are observed in asset prices that may be exogenous or endogenous. It is claimed that the source of exogenous jumps is news, and the source of endogenous jumps is internal interactions between the agents. The goal is to extract these endogenous jumps as a function of the state variable and time. For this purpose, the generalized Langevin equation is introduced and it is shown that the parameters of this model can be extracted based on the Kramers-Moyal coefficients. The results of self-consistency tests to evaluate the accuracy of the Kramers-Moyal method in extracting the generalized Langevin equation show that this method has good accuracy. In a practical application of the aforementioned method, Ethereum cryptocurrency price data was used between October 2017 and February 2024 with a sampling rate of one minute. By simulating the extracted dynamics, the probability distribution of the first time passage of this cryptocurrency from a specific level was calculated, and an examination of the price behavior of this asset shows that the aforementioned distribution was extracted with good accuracy. The potential function, which is calculated from the first KM coefficient, will be a quadratic parabola for the studied process, and as a result, we have a stable equilibrium at the zero point. Also using the extracted dynamics we show that this model has good out-of-sample prediction ability.