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Advances in Mathematical Finance and Applications under printed licence number 2538-5569 and online licence number 2645-4610, dated 26/5/12, portal of scientific publications of the Ministery of Science, Research and Technology to the address: https://journals.msrt.ir/Home/Detail/10245 has recieved the scientific grade (former Scientific Research) and in evaluation of dated 2022 the quarterly obtained the (A) grade. According to the executive instructions of the financial support of Islamic Azad University scientific journals(10/62815-2019), in case of initial approval by the editor, for sending each article for judging, the amount of 1/000/000 Rials and after the final approval, the amount of 3/000/000 Rials will be recieved from the esteemed authors for publication of the article. It should be noted that if the article be rejected by the esteemed judges, the initial payment cannot be returned to the author. For foreigners the article processing charges is 50$ for final approval step. |
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The Journal of Advances in Mathematical Finance and Applications has signed a memorandum of understanding with the Iranian Financial Engineering Association |
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Advances in Mathematical Finance and Applications (AMFA) provides and immediate open access to its content on the principle that making research freely available to the public supports a greater global exchange of knowledge. The journal is owned by Islamic Azad University, Arak Branch (IAU-AB). |
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COPE-The Committee of Publication Ethics (Registered) |
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All submitted manuscripts are checked for similarity through iThenticate software to ensure their authenticity and originality and then rigorously peer-reviewed by the expert reviewers. |
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© 2019 "Advances in Mathematical Finance and Applications" allows reuse and remixing of its content, in accordance with a CC-BY license and is distributed under a Creative Commons Attribution (CC-BY) 4.0 license |
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ISC Quartile 2022 = 1.031 (Q1) |
Recent Articles
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Open Access Article
1 - Developing Financial Distress Prediction Models Based on Imbalanced Dataset: Random Undersampling and Clustering Based Undersampling Approaches
Seyed behrooz Razavi ghomi Alireza Mehrazin Mohammad reza shourvarzi Abolghasem Masih AbadiIssue 3 , Vol. 9 , Summer 2024So far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overes MoreSo far, distress prediction models have been based on balanced, such sampling is not consistent with the reality of the statistical community of companies. If the data are balanced, the bias in sample selection may lead to an underestimation of typeI error and an overestimation of the typeII error of models. Although imbalanced data-based models are compatible with reality, they have a higher typeI error compared to balanced data-based models. The cost of typeI error is more important to Beneficiaries than the cost of typeII error. In this study, for reducing typeI error of imbalanced data-based models, random and clustering-based undersampling were used. Tested data included 760 companies since 2007-2007 with 4 different degrees and the results of the H1 to H3 test represented them. In all cases of the typeI error, typeII error of balanced data-based models were lower and more, respectively, compared to imbalanced data-based models; also, in most cases, the geometric mean of balanced data-based models was higher compared to imbalanced data-based models, respectively. The results of testing H4 to H6 show that in most cases, typeI error, typeII error and the geometric mean criterion of models based on modified imbalanced data were less, more, and more, respectiively compared to the models based on imbalanced data, in other words, applying Undersampling methods on imbalanced training data led to a decrease in typeI error and an increase in typeII error and geometric mean criteria. As a result using models based on modified imbalanced data is suggested to Beneficiaries Manuscript profile -
Open Access Article
2 - Investigating the Impact of Financial Development Indicators and Economic and International Trade Performance on the Stock and Financial Markets
Sara Maleki Mehrzad Minoie MirFeiz Falah ShamsIssue 3 , Vol. 9 , Summer 2024One of the goals of researchers and policymakers is to find measures to achieve economic growth. Financial development is one of the policies that many economists recommend in order to achieve economic growth and development. From this perspective, financial development MoreOne of the goals of researchers and policymakers is to find measures to achieve economic growth. Financial development is one of the policies that many economists recommend in order to achieve economic growth and development. From this perspective, financial development is an engine for economic growth, and policymakers should focus on creating and expanding financial institutions and markets. The present study examines the impact of financial development and economic performance indicators including economic growth and international trade in developing and developed countries in the long run from 2001 to 2018. Data collection has been done by two methods, library, and field, to complete the literature and research background, refer to libraries and researches, and for financial and economic data, including financial development indicators in two sections: Bank- Index and Capital Markets Stock-Index, as well as figures for Gross Domestic Product (GDP) and international trade from the World Development Index (WDI) databases, are used. Developed countries, due to their technology and power in production, can carry out their industrial production and export to developing countries. However, developing countries do not see long-term equilibrium relationships for economic growth and international trade. Manuscript profile -
Open Access Article
3 - Modelling Robust Optimization in DEA With Ratio Data: A Case Study of Commercial Banks
Javad GeramiIssue 3 , Vol. 9 , Summer 2024In many practical problems, we face situations where the data ratio is important for the decision-maker (DM). Data envelopment analysis ratio-based (DEA-R) and ratio analysis models are presented to deal with the above issue in data envelopment analysis (DEA). If the da MoreIn many practical problems, we face situations where the data ratio is important for the decision-maker (DM). Data envelopment analysis ratio-based (DEA-R) and ratio analysis models are presented to deal with the above issue in data envelopment analysis (DEA). If the data is uncertain, it is no longer possible to use the basic DEA-R and ratio analysis models to evaluate the efficiency of decision-making units (DMUs). In this paper, we will first discuss robust optimization modelling based on DEA-R models. In this regard, we consider a case where the inputs have an uncertain numerical value and the outputs have certain values. In the following, we present the ratio analysis model based on the set of common weights of all the ratios of input to output components and obtain this model for robust optimization. To show the validity of the proposed approach, we use it to evaluate the efficiency of 38 excellent banks that compete in the global market and compare the results of the proposed approach in this paper with the results of previous approaches. Manuscript profile -
Open Access Article
4 - Information Asymmetry with Emphasis on the Role of Financial and Managerial Criteria Based on Fuzzy Logic and Artificial Neural Networks
Mohammad Amir Golshani Mehrdad Ghanbari Babak Jamshidi Navid Forouzan Mohammadi YarijaniIssue 3 , Vol. 9 , Summer 2024This paper addresses the absence of a suitable criterion for measuring information asymmetry between managers forecasting earnings and analysts forecasting earnings through statistical methods. Besides, this paper aims to provide a model of information asymmetry, emphas MoreThis paper addresses the absence of a suitable criterion for measuring information asymmetry between managers forecasting earnings and analysts forecasting earnings through statistical methods. Besides, this paper aims to provide a model of information asymmetry, emphasizing the role of financial and managerial criteria. This is applied qualitative and quantitative research (mixed method). The library method is used to prepare and formulate theoretical bases. In addition, the field method is used for collecting data to measure and identify indices and modeling. Factor analysis was used to analyze the data, following identifying the dimensions and variables of financial and managerial criteria of information symmetry to eliminate extraneous factors and classify. The following five main dimensions were determined, including corporate profit forecast, corporate governance, capital market, capital return, and management characteristics of the company. Then, the modeling was done using fuzzy mathematics through triangular numbers, Mamdani implication, and center of gravity methods. The final results of the study of the company listed on the Tehran Stock Exchange show that the level of information symmetry in the range of zero to 100 equals 55.1, to predict the company's profit is 48.54; corporate governance is 56.95; the capital market is 1/59; capital return is 61.07, and managerial characteristics of the company are 67.84. Finally, we examined the factors affecting the information asymmetry obtained from fuzzy neural networks. The findings show a higher prediction accuracy of fuzzy neural network methods than other related prediction methods. Manuscript profile -
Open Access Article
5 - Computing the Efficiency of Bank Branches with Financial Indexes, an Application of Data Envelopment Analysis (DEA) and Big Data
Fahimeh Jabbari-Moghadam Farhad Hosseinzadeh Lotfi Mohsen Rostamy-Malkhalifeh Masoud Sanei Bijan Rahmani-ParchkolaeiIssue 3 , Vol. 9 , Summer 2024In traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increa MoreIn traditional Data Envelopment Analysis (DEA) techniques, in order to calculate the efficiency or performance score, for each decision-making unit (DMU), specific and individual DEA models are designed and resolved. When the number of DMUs are immense, due to an increase in complications, the skewed or outdated, calculating methods to compute efficiency, ranking and …. may not prove to be economical. The key objective of the proposed algorithm is to segregate the efficient units from that of the other units. In order to gain access to this objective, effectual indexes were created; and taken to assist, in regards the DEA concepts and the type of business (under study), to survey the indexes, which were relatively operative. Subsequently, with the help of one of the clustering techniques and the ‘concept of dominance’, the efficient units were absolved from the inefficient ones and a DEA model was developed from an aggregate of the efficient units. By eliminating the inefficient units, the number of units which played a role in the construction of a DEA model, diminished. As a result, the speed of the computational process of the scores related to the efficient units increased. The algorithm designed to measure the various branches of one of the mercantile banks of Iran with financial indexes was implemented; resulting in the fact that, the algorithm has the capacity of gaining expansion towards big data. Manuscript profile -
Open Access Article
6 - Designing a Model to Investigate the Process of Forming Cluster Fluctuations According to the Fractal Structure in Financial Markets
Amin Amini Bashirzadeh Shahrokh Bozorgmehrian Bahareh Banitalebi DehkordiIssue 3 , Vol. 9 , Summer 2024Cluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current researc MoreCluster fluctuations and fractal structures are important features of space-time correlation in complex financial systems. However, the microscopic mechanism of creation and expansion of these two features in financial markets remains challenging. In the current research, by using factor-based model design and considering a new interactive mechanism called multi-level clustering, the formation process of cluster fluctuations was investigated with regard to the fractal structure of financial markets. For this purpose, the daily information of the final price of 150 shares that were accepted in the Tehran Stock Exchange, after the final screening, was entered in 5 sections with 30 shares in each section, in the desired model, and they were aggregated in three stock levels., sector and market were measured. Due to the fact that some investors have a longer investment horizon in the stock market and due to the limitation of the investigated time period, the maximum investment horizon of 1000 days has been determined in the model.In addition, the data studied in the research model are from August 2012 to September 2018. The findings of the research showed that the intensity of the tendency of collec-tive behavior at the sector level is much stronger than at the market level. In addition, based on the findings of the research, it was determined that the distribution of simulation eigenvalues in three levels is significantly similar to the distribution of real data. Also, according to the investor's time horizon, the studied series always has a long-term memory for fluctuations. In addi-tion, it was found that long-term memory is directly related to fractal dimen-sions. The findings of this research, in addition to providing a new insight into the space-time correlations of financial markets, show that multi-level conglomeration is one of the mechanisms for creating the microscopic mi-crostructure of such markets. In other words, multi-level collective behavior is an important factor in the occurrence of cluster and fractal fluctuations in the market, and therefore, it should be considered from this point of view in the interpretation of the concept of risk and the definition of risk manage-ment strategies. Manuscript profile -
Open Access Article
7 - Designing a Model for Predicting Corporate Bankruptcy Using Ensemble Learning Techniques
Hossein Eghbali Alimohamad AhmadvandIssue 3 , Vol. 9 , Summer 2024The bankruptcy of corporations causes huge losses for investors, managers, creditors, employees, suppliers, and customers. If someone understands the reason for the corporate's bankruptcy, then he can save the corporate from certain death with the necessary planning. Th MoreThe bankruptcy of corporations causes huge losses for investors, managers, creditors, employees, suppliers, and customers. If someone understands the reason for the corporate's bankruptcy, then he can save the corporate from certain death with the necessary planning. Therefore, bankruptcy forecasting is the most important prerequisite for bankruptcy prevention. Due to this issue, the main aim of this article is the prediction of the economic bankrupt-cy of corporations in the Tehran Stock Exchange using group machine learn-ing algorithms. Financial ratios have been used as independent variables and healthy and bankrupt corporations as research dependent variables. The statistical population of the study is the information of financial statements of corporations on the Tehran Stock Exchange from the years 2004 to 2021. In this study, sampling is not used and corporations include two groups healthy and bankrupt. The bankrupt and non-bankrupt groups are selected based on the threshold of the Springate model. The research findings indicate that the accuracy of predicting the bankruptcy of corporations in the group learning model by stacking method is higher than other used models where the AUC and Accuracy Ratio were 0.9276 and 0.8247, respectively. Manuscript profile -
Open Access Article
8 - Early Warning Model for Solvency of Insurance Companies Using Machine Learning: Case Study of Iranian Insurance Companies
Saeed Naseri Khezerloo Atousa GoodarziIssue 3 , Vol. 9 , Summer 2024Stakeholders of an organization avoid undesirable outcomes caused by ig-noring the risks. Various models and tools can be used to predict future out-comes, aiming to avoid the undesirable ones. Early warning models are one of the approaches that could help them in doing MoreStakeholders of an organization avoid undesirable outcomes caused by ig-noring the risks. Various models and tools can be used to predict future out-comes, aiming to avoid the undesirable ones. Early warning models are one of the approaches that could help them in doing so. This study focuses on developing an early warning system using machine learning algorithms for predicting solvency in the insurance industry. This study analyses 23 finan-cial ratios from Iranian general insurance companies listed on the Tehran Stock Exchange between 2015 and 2020. The model uses Decision Tree, Random Forest, Artificial Neural Networks, Gradient Boosting Machine and XGBoost algorithms, with Boruta as a feature selection method. The depend-ent variable is the solvency margin ratio, and the other 22 ratios are the inde-pendent variables, which Boruta reduces to 7 variables. Firstly, the perfor-mance of the machine learning models on two datasets, one with 22 inde-pendent variables and one with 7, is compared based on RMSE values. The XGBoost algorithm performs the best on both data sets. Additionally, the study predicts the 2020 values for 19 insurance companies, performs stage classifications, and compares actual stages to predicted stages. In this analy-sis, Random Forest has the best estimate accuracy on both data sets, while Gradient Boosting Machine has the best estimate accuracy on the Boruta data set. Finally, the study compares the machine learning models' results in terms of capital adequacy classification, where Random Forest performs the best on both data sets, and Gradient Boosting Machine on the Boruta data set. Manuscript profile -
Open Access Article
9 - The Co-movement Between Bitcoin, Gold, USD and Oil: DCC-GARCH and Smooth Transition Regression (STR) Model
Yazdan Gudarzi Farahani Ehsan Aghari Ghara Mnasour HaghtalabIssue 3 , Vol. 9 , Summer 2024This study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARC MoreThis study investigates the relationships between Bitcoin (BTC) prices and fluctuations in relation to gold, USD, and Iran's oil prices from 2019 to 2022. We employed the dynamic conditional correlation generalized autoregressive conditional heteroscedasticity (DCC-GARCH) method to model the fluctua-tions of financial variables. Additionally, the smooth transition regression (STR) method was applied to explore the relationships between the variables. The results reveal significant positive correlations between BTC prices and gold, as well as oil, and a negative correlation with USD prices. We observed volatility persistence, causality, and phase differences between BTC and other financial instruments and indicators. Notably, a negative relationship was identified between Bitcoin and the USD in both linear and non-linear aspects, with a larger coefficient in the second regime. Furthermore, a posi-tive relationship was found between Bitcoin and the variables of gold and oil prices, with coefficients being larger in the second regime compared to the first. Manuscript profile -
Open Access Article
10 - Net Working Capital Investment Policies, The Value of Financial Flexibility and Financial Constraint, Evidence From The Tehran Stock Exchange
Maryam Karimi Mehdi Basirat Rasoul Karami Allah karam SalehiIssue 3 , Vol. 9 , Summer 2024Companies pay attention to the value level of financial flexibility in making decisions related to optimizing investments and applying their net working capital policies. This issue will make profitable investment opportunities for companies more efficient and enable co MoreCompanies pay attention to the value level of financial flexibility in making decisions related to optimizing investments and applying their net working capital policies. This issue will make profitable investment opportunities for companies more efficient and enable companies to gain more efficiency, as well as apply more optimal policies to keep cash. The purpose of this research is to investigate the effect of financial flexibility value and financial constraint on the speed of adjustment of net working capital, as well as the effect of financial constraint on the relationship between the value of financial flexibility and the speed of adjustment of net working capital in companies listed on the Tehran Stock Exchange. The appropriate pattern recognition test in combined data indicates the use of the regression model of the research using the panel data method with the fixed and random effects approach for the panel and pooled data patterns to estimate the regression model. The statistical sample includes 100 companies accepted to the Tehran Stock Exchange during the period from 2005 to 2020. The findings indicate that the value of financial flexibility has a positive and significant effect on the speed of adjustment of net working capital in the models of partial adjustments and error correction. Financial constraint has a positive and significant effect on the speed of net working capital adjustment, and it also has a positive and significant effect on the relationship between the value of financial flexibility and the speed of net working capital adjustment. Manuscript profile -
Open Access Article
11 - Examining Financial Performance and Corporate Governance in Tehran Stock Exchange: A Hybrid Machine Learning and Data Envelopment Analysis Approach
Morteza Bagheri Pooneh Noparvar Saravi Seyed Sadegh HadianIssue 3 , Vol. 9 , Summer 2024In the backdrop of an ever-evolving global business landscape and intense market competition, companies are faced with the imperative of strategically managing factors that influence their financial performance. This research delves into the intricate relationship betwe MoreIn the backdrop of an ever-evolving global business landscape and intense market competition, companies are faced with the imperative of strategically managing factors that influence their financial performance. This research delves into the intricate relationship between financial performance enhancement and corporate governance, with particular attention to the mediating role of human capital. The study centers its investigation on companies listed on the Tehran Stock Exchange and comprises a comprehensive sample of 140 top-level managers. A composite sampling approach, comprising a simple random sampling technique and Morgan's table, was employed to judiciously select a representative cohort of 103 participants. In the pursuit of rigorous academic analysis, the research leverages a goal-oriented, applied methodology, employing a descriptive survey design and a quantitative approach. The primary data for the study were methodically collected through rigorously designed and standardized questionnaires. Subsequent to data acquisition, a meticulous analytical process was undertaken using the Partial Least Squares (PLS) software, aligning with the latest developments in quantitative research techniques. The results stemming from hypothesis testing offer compelling insights into the dynamic relationship between corporate governance, human capital, and financial performance enhancement. Our findings convincingly demonstrate a significant positive impact of both corporate governance and human capital on the enhancement of financial performance in the context of Tehran Stock Exchange's listed companies. Furthermore, the empirical evidence strongly suggests that human capital plays a pivotal mediating role in the relationship between corporate governance practices and financial performance improvements. This study, in its pursuit of academic rigor, underscores the effectiveness of a novel hybrid approach, thoughtfully integrating machine learning and data envelopment analysis, to comprehensively examine the intricate interplay between financial performance enhancement and corporate governance within the context of the Tehran Stock Exchange's listed companies. The study contributes to the evolving body of literature in this domain and provides valuable insights for practitioners, policymakers, and researchers. Manuscript profile -
Open Access Article
12 - Financial Reporting Readability: A new Artificial Neural Network and Multi-Indicator Decision Making Approach
Ali Asghar Khazaei Harivand Arash Naderian Majid Ashrafi Ali KhozinIssue 3 , Vol. 9 , Summer 2024The desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the arti MoreThe desirability of the financial reporting can greatly help the users of finan-cial information in making investment decisions. The purpose of this re-search is to measure the readability of financial reporting using a multi-indicator decision-making model and the artificial neural network method and the role of information presentation time in its improvement. In this research, various indicators have been used to measure the readability of financial reporting, and the quality of reporting is obtained through the rank-ing of companies by the stock exchange. In this research, the number of 149 companies admitted to the Tehran Stock Exchange in the period of 2010-2020 was examined, and to measure the financial readability through struc-tural equations and Stata software, and to test the hypothesis of the research, the regression model and Eviews econometrics software were used. In this study, we have tried to Use machine learning techniques and optimization tools as a way to derive adaptive-robust nonlinear models that can reduce the risk of model error as much as possible. The findings of the research show that the time of providing information has an impact on the readability of financial reporting. The obtained outputs from the estimation of the artificial neural networks and results obtained from estimation, using of this method with evaluation scales concerning random amount and comparing it with adjusted R, we found that there is meaningful relation between the associated variables and return. However, such network has the least error than other networks. The results show an overall improvement in forecasting using the neural network as compared to linear regression method. In other words, our proposed system displays an extremely higher profitability potential. The obtained result can be argued that the more the company's information is provided by the managers to the company's shareholders and investors on time and at the right time, the more readable and understandable the financial reports will be. Manuscript profile -
Open Access Article
13 - The Modeling the Fixed Asset Investing with a Machine Learning Approach by Emphasizing the Role of Financial Criteria
Farzaneh SHamsdoost Omid Mahmoudi Khoshro Ataollah Mohammadi Malgharni Amir SheikhahmadiIssue 3 , Vol. 9 , Summer 2024The purpose of this research is to provide a growth model of fixed assets based on the financial criteria of companies admitted to the Tehran Stock Exchange. The current research is applied in terms of objective classification and descriptive-correlation in terms of met MoreThe purpose of this research is to provide a growth model of fixed assets based on the financial criteria of companies admitted to the Tehran Stock Exchange. The current research is applied in terms of objective classification and descriptive-correlation in terms of method. The research method is de-ductive-inductive. The statistical population of the current research is all the companies admitted to the Tehran Stock Exchange in the period from 2012-2021 and the financial information of 101 companies are use. Research hypotheses were tested using artificial intelligence algorithm. In this research, investment in fixed assets has been consider as a dependent variable, and financial criteria has been considered as primary independent variables. The results of research hypotheses testing using the methods of linear and non-linear algorithms of artificial intelligence PINSVR and KPLSR in predicting fixed asset investors of companies and by calculating the three errors criteria MAE, MSE and SMAPE in annual fixed assets. The asset forecasting in the next year of companies showed that the error difference between linear models and non-linear models is not so great that it can be claim that linear models are ineffective in predicting asset growth so that artificial intelligence algorithms are capable of predicting investment in company assets. Manuscript profile -
Open Access Article
14 - Comparing the Performance of Machine Learning Techniques in Detecting Financial Frauds
Jafar Nahari Aghdam Qala Jougha Nader Rezaei Yagoob Aghdam Mazraee, Rasol AbdiIssue 3 , Vol. 9 , Summer 2024Detecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in com MoreDetecting financial fraud is an important process in the activities of compa-nies. In the last decade, much attention has been paid to fraud detection techniques. Financial fraud is a problem with far-reaching implications for shareholders. Today, financial fraud in companies has become a big prob-lem. Companies and regulatory agencies must continuously develop their mechanisms to detect fraud. Machine learning and data mining techniques are currently commonly used to solve this problem. However, these tech-niques still need to be improved in terms of computational cost, memory cost, and dealing with big data that is becoming a feature of current financial transactions. In this research, machine learning techniques including logistic regression, neural network, and Bayesian linear regression were used to de-tect financial frauds in the Iranian stock market. According to the obtained results, the support vector machine model with radial kernel has the lowest RMSE and the highest accuracy criterion, and the support vector machine model with linear kernel and Bayesian linear regression has the highest RMSE and the lowest accuracy criterion for modeling the financial fraud of companies in they were Tehran stock market. Also, the models of artificial neural network model, Bayesian linear regression and support vector ma-chine model with linear kernel respectively had the lowest characteristic values and did not perform relatively well in detecting the existence of fi-nancial fraud in the companies present in the Tehran stock market. Manuscript profile -
Open Access Article
15 - Designing Prediction Model of Financial Restatements Using Neural-Genetic Simulation Algorithm
Sasan Mehrani Akbar Rahimi poorIssue 3 , Vol. 9 , Summer 2024The increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the r MoreThe increased number of restatements in recent years has increased the wor-ries about the quality of financial reporting among the beneficiary groups. The pres-ence of prior period adjustments and, subsequently, the financial restatements have a negative impact on the relatedness and reliability of the financial state-ments. The present study is aimed to present an appropriate criterion for predict-ing the financial restatements based on the Beneish model and its indices in companies admitted to the Tehran Stock & Exchange between 2009 and 2020. For this purpose, a total of 265 companies were selected considering the limitations. Also, the model estimation was per-formed using Beneish's primary model, a meta-heuristic neural network model, and optimization through genetic programming. As indicated by the obtained results based on the confusion matrix, the efficiency of the pro-posed model derived from the enhanced Beneish model with a genetic algo-rithm(S – 𝑆𝑐𝑜𝑟𝑒) had a total prediction accuracy of 73.21%, which was the highest prediction power compared to the Beneish Model . Manuscript profile
Most Viewed Articles
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Open Access Article
1 - Using MODEA and MODM with Different Risk Measures for Portfolio Optimization
Sarah Navidi Mohsen Rostamy-Malkhalifeh Shokoofeh BanihashemiIssue 1 , Vol. 5 , Winter 2020The purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for MoreThe purpose of this study is to develop portfolio optimization and assets allocation using our proposed models. The study is based on a non-parametric efficiency analysis tool, namely Data Envelopment Analysis (DEA). Conventional DEA models assume non-negative data for inputs and outputs. However, many of these data take the negative value, therefore we propose the MeanSharp-βRisk (MShβR) model and the Multi-Objective MeanSharp-βRisk (MOMShβR) model base on Range Directional Measure (RDM) that can take positive and negative values. We utilize different risk measures in these models consist of variance, semivariance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) to find the best one as input. After using our proposed models, the efficient stock companies will be selected for making the portfolio. Then, by using Multi-Objective Decision Making (MODM) model we specified the capital allocation to the stock companies that selected for the portfolio. Finally, a numerical example of the Iranian stock companies is presented to demonstrate the usefulness and effectiveness of our models, and compare different risk measures together in our models and allocate assets. Manuscript profile -
Open Access Article
2 - Overview of Portfolio Optimization Models
Majid ZanjirdarIssue 4 , Vol. 5 , Autumn 2020Finding the best way to optimize the portfolio after Markowitz's 1952 article has always been and will continue to be one of the concerns of activists in the investment management industry. Researchers have come up with different solutions to overcome this problem. The MoreFinding the best way to optimize the portfolio after Markowitz's 1952 article has always been and will continue to be one of the concerns of activists in the investment management industry. Researchers have come up with different solutions to overcome this problem. The introduction of mathematical models and meta-heuristic models is one of the activities that has influenced portfolio optimization in recent decades. Along with the growing use of portfolios and despite its rich literature, there are still many unanswered issues and questions in this area. Also, Iranian capital markets, as emerging markets, require native research to answer these questions and issues. The purpose of this study is to provide a useful and effective tool to assist professionals and researchers in portfolio selection theory. This study, while comprehensively reviewing the literature on the subject and the developments and expansions made in the area of portfolio selection and optimization, reviews the types of problems and optimization methods. Manuscript profile -
Open Access Article
3 - Improving Stock Return Forecasting by Deep Learning Algorithm
Zahra Farshadfar Marcel ProkopczukIssue 3 , Vol. 4 , Summer 2019Improving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the resu MoreImproving return forecasting is very important for both investors and researchers in financial markets. In this study we try to aim this object by two new methods. First, instead of using traditional variable, gold prices have been used as predictor and compare the results with Goyal's variables. Second, unlike previous researches new machine learning algorithm called Deep learning (DP) has been used to improve return forecasting and then compare the results with historical average methods as bench mark model and use Diebold and Mariano’s and West’s statistic (DMW) for statistical evaluation. Results indicate that the applied DP model has higher accuracy compared to historical average model. It also indicates that out of sample prediction improvement does not always depend on high input variables numbers. On the other hand when using gold price as input variables, it is possible to improve this forecasting capability. Result also indicate that gold price has better accuracy than Goyal's variable to predicting out of sample return. Manuscript profile -
Open Access Article
4 - DEA Approaches for Financial Evaluation - A Literature Review
Mohammad IzadikhahIssue 1 , Vol. 7 , Winter 2022Financial assessment has been of great interest to both academic and practitioners in the past decades. Among several performance assessment approaches, Data Envelopment Analysis (DEA) has become one of the crucial tools that have been commonly adopted to financially ev MoreFinancial assessment has been of great interest to both academic and practitioners in the past decades. Among several performance assessment approaches, Data Envelopment Analysis (DEA) has become one of the crucial tools that have been commonly adopted to financially evaluate firms in various fields. The main aim of this review article is to review of DEA models in regarding to evaluation of the financial performance. This paper presents the first comprehensive and structured literature review of the use of DEA models for financially assessment. To this end, this paper reviewed and summarized the different models of DEA models that have been applied around the world to development of financial assessment problems. Consequently, a review of 455 published scholarly papers appearing in 160 journals between 1994 and 2021 have been obtained to achieve a comprehensive review of DEA application in financial efficiency. Accordingly, the selected articles have been categorized based on year of publication, authors, nationalities, scope of study, time duration, application area, study purpose, results, outcomes, etc. The discussion and the findings of this paper can be used as a guideline to analysts to determine the best fit financial assessment method when DEA evaluation is applied to any dataset. Future perspectives and challenges are discussed. Manuscript profile -
Open Access Article
5 - Behavioral Finance Models and Behavioral Biases in Stock Price Forecasting
Nader Rezaei Zahra ElmiIssue 4 , Vol. 3 , Autumn 2018Stock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different MoreStock market is affected by news and information. If the stock market is not efficient, the reaction of stock price to news and information will place the stock market in overreaction and under-reaction states. Many models have been already presented by using different tools and techniques to forecast the stock market behavior. In this study, the reaction of stock price in the stock market was modeled by the behavioral finance approach. The population of this study included the companies listed on the Tehran Stock Exchange. In order to forecast the stock price, the final price data of the end December, March, June, and September 2006-2015 and the stock prices of 2014 and 2015 were analyzed as the sample. In this study, Bayes' rule was used to estimate the probability of the model change. Through this rule, the probability of an event can be calculated by conditioning the occurrence or lack of occurrence of another event. The results of model estimation showed that there is the probability of being placed in high-fluctuated regimes (overreaction) and low- fluctuated (under-reaction of stock price despite the shocks entered to the stock market. In modelling with the 4-month final prices, it was proved that the real stock price had no difference from the market price. Manuscript profile -
Open Access Article
6 - Presenting a New Bankruptcy Prediction Model Based on Adjusted Financial Ratios According to the General Price Index
Naimeh Jebelli Iman Dadashi Mohammad Javad Zare BahnamiriIssue 4 , Vol. 6 , Autumn 2021In 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 MoreIn 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%. Manuscript profile -
Open Access Article
7 - An Algorithmic Trading system Based on Machine Learning in Tehran Stock Exchange
Hamidreza Haddadian Morteza Baky Haskuee Gholamreza ZomorodianIssue 3 , Vol. 6 , Summer 2021Successful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these syst MoreSuccessful trades in financial markets have to be conducted close to the key recurrent points. Researchers have recently developed diverse systems to help the identification of these points. Technical analysis is one of the most valid and all-purpose kinds of these systems. With its numerous rules, the technical analysis endeavors to create well-timed and correct signals so that these points are identified. However, one of the drawbacks of this system is its overdependence on human analysis and knowledge in selecting and applying these rules. Employing the three tools of genetic algorithm, fuzzy logic, and neural network, this study attempts to develop an intelligent trading system based on the recognized rules of the technical analysis. Indeed, the genetic algorithm will assist with the optimization of technical rules owing to computing complexities. The fuzzy inference will also help the recognition of the total current condition in the market. It is because a set of rules will be selected based on the market kind (trending or non-trending). Finally, the signal developed by every rule will be translated into a single result (buy, sell, or hold). The obtained results reveal that there is a statistically meaningful difference between a stock's buy and hold and the trading system proposed by this research. In other words, our proposed system displays an extremely higher profitability potential. Manuscript profile -
Open Access Article
8 - A Combined Model for Prediction of Financial Software Learning Rate based on the Accounting Students’ Characteristics
Bahareh Banitalebi Dehkordi Hamed Samarghandi Sara Hosseinzadeh Kassani Hamidreza malekhossiniIssue 4 , Vol. 7 , Autumn 2022The 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 adequ MoreThe 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. Manuscript profile -
Open Access Article
9 - Application of Mathematics in Financial Management
Sanjay TripathiIssue 2 , Vol. 4 , Spring 2019The Time Value of Money is a important concept in Financial Management. The Time Value of Money includes the concepts of future value and discounted value or present value. In the present article, the basic notions and illustrate with their application in the field of i MoreThe Time Value of Money is a important concept in Financial Management. The Time Value of Money includes the concepts of future value and discounted value or present value. In the present article, the basic notions and illustrate with their application in the field of investment which is presented in the mathematical terms in form of theorems and we also presented the applications of some well known problems with the proof such as mortagage loan problem, investment in bond and an individual who plans to retire in certain years who plan for invest-ment for its future life. We also presented the application of calculus that is limit, derivative and integration in financial management. Manuscript profile -
Open Access Article
10 - The improved Semi-parametric Markov switching models for predicting Stocks Prices
Hossein Naderi Mehrdad Ghanbari Babak Jamshidi Navid Arash NademiIssue 2 , Vol. 9 , Spring 2024The modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov MoreThe modelling of strategies for buying and selling in Stock Market Investment have been the object of numerous advances and uses in economic studies, both theoretically and empirically. One of the popular models in economic studies is applying the Semi-parametric Markov Switching models for forecasting the time series observations based on stock prices. The Semi-parametric Markov Switching models for these models are a class of popular methods that have been used extensively by researchers to increase the accuracy of fitting processes. The main part of these models is based on kernel and core functions. Despite of existence of many kernel and core functions that are capable in applications for forecasting the stock prices, there is a widely use of Gaussian kernel and exponential core function in these models. But there is a question if other types of kernel and core functions can be used in these models. This paper tries to introduce the other kernel and core functions can be offered for good fitting of the financial data. We first test three popular kernel and four core functions to find the best one and then offer the new strategy of buying and selling stocks by the best selection on these functions for real data. Manuscript profile
