Advances in Mathematical Finance and Applications
Volume:7 Issue: 1, Winter 2022

Financial 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.

This research investigates the capital market's efficiency relative to the decreasing and increasing information the components of cash and accrual of the accounting earnings. In the accrual accounting system, accounting earnings includes two components of cash and accrual. Information about decreasing and increasing values of the normal and abnormal portion of the changes in financial assets as a cash component is compared with the information on the decreasing and increasing amounts of discretionary and non-discretionary accruals. The required data was extracted from the financial statements of the listed companies in Tehran Stock Exchange during the years 2003 to 2017. In order to estimate the research models from the regression with the combined data as well as the equations system, the simultaneous equations system with the seemingly unrelated regression (SUR) approach are utilized and then some experiments are implemented to evaluate the research hypotheses by using Mishkin’s test. The results reveal that the capital market is inefficient in terms of the increasing information (positive portion) of accrual and discretionary accruals and information on abnormal changes in financial assets (increasing and decreasing), but rather on the information (negative) of discretionary and non-discretionary accruals and abnormal changes in the financial assets (increasing and decreasing).

The secret to achieving your organization's goals in complex and challenging environments is to make the right managerial and rational decisions. In this regard, the accounting information system as one of the sources of information for the decision of managers is of particular importance. Therefore, in order to achieve these goals, it is necessary to have an accounting information system with dynamic capabilities. The dynamic capability of the accounting information system (hidden variable) was measured by the observed variables of flexibility, continuous evaluation, continuous investment and system variability. Therefore, based on this argument, the aim of the present study is to provide a dynamic capability model of the accounting system based on the financial effectiveness of the management accounting system. Data envelopment analysis is a well-known methodology that is applied to evaluate the selected firms based on the most important features. The results of analysis of the proposed method on 86 companies listed on the Tehran Stock Exchange and analysis and analysis of data by structural equation modeling show that the dynamic capability of the accounting information system consists of (flexibility, continuous evaluation, continuous investment and system variability). The result indicate that the management accounting system is effective.

Chief executive officers (CEO), as the central pillar directing companies at the capital market level, plays a critical role in making investment decisions to de-crease agency costs and maximize shareholders’ wealth; however, their psycho-logical attitudes and perceptions lead to different investment functions of these companies in a competitive market. Accordingly, this study aimed to detect the propositional arrays of the investment functions using Q analysis in order to identify the CEOs’ mental typology in distinguishing investment functions. The present study was carried out with the participation of 13 CEOs from Tehran Stock Exchange companies during a one-year period (2018-2019). The study encompassed two different phases. In the first phase, content analysis was used to identify the phrase Q of the CEOs’ investment functions. In the second phase, Q analysis was adopted to typify the CEOs’ investment functions, which was based on the subjective cognition of the target population and contributed to the development of the approaches in line with the research objectives. The results confirmed the existence of three mental patterns in the CEOs regarding the in-vestment functions in the capital market. These mental patterns were ‘investment function in stock market indices’ as the first mental pattern, ‘investment function in risk control’ as the second mental pattern, ‘strategic investment function’ as the third mental pat-tern. The study results revealed different types of investment functions among the CEOs’ of stock exchange companies and thus contributed to the development of financial theories from the perspective of CEOs’ cognition.

Multiplicity of the companies experiencing financial distress in different countries and as a consequence, their bankruptcy and the impacts on other companies have necessitated conducting research on methods of prediction of such conditions and also their effects on other companies in the market. In this regard, this research has investigated the financial distress spillover in the automobile supply chain companies. For doing so, the methods of default probability time series KMV and the distance from default of four supply chain companies of Iran Khodro and four supply chain companies of SAIPA were calculated. Then, the financial distress spillover in these two major companies was measured in separated models using multivariate GARCH model. The results of the default probability of Iran Khodro companies showed that the default probability with pause of Khodro on the default probability of supply chain companies was significant and negative in 10% level. The results for SAIPA supply chain companies revealed that the default probability with pause of Khaspa had an impact on default probability of Kaspa, Pask and Khazin in significance level of 10%.

One of the most important aspects of investment is determining the “optimal investment portfolio”. To date, scientific research has been conducted to determine the optimal portfolio with “artificial intelligence” and “Fuzzy logic”. However, we determine the optimal portfolio based on Dynamic Stochastic General Equilibrium (DSGE) model. On the other hand, several factors affect returns, which is one of the most important issues in investment decision-makings, and various models have been developed to analyze the return of “capital” and “other assets”. In this regard, some of the most important models include linear and non-linear models, artificial neural network models, Fama–French model, Generalized AutoRegressive Conditional Heteroskedasticity (GARCH) model, and optimal stable model. All of these models are indicative of the use of quantitative methods and models in the investment industry. One of the causes of using these models is developing the financial economy. In fact, the development of financial economy has more increased with the concept of “portfolio optimization”. In fact, the portfolio optimization and diversification concept is the basis of developing and expanding classical finance and financial decision-making. Financial markets, especially the capital market, can have a great deal of relevance to other sectors of the economy. In the present study, we design and calibrate a new Keynesian DSGE model in relation to the optimal investment portfolio and effectiveness of shocks (e.g., productivity and foreign exchange earnings fluctuation) on macroeconomics variables.

Behavioral finance had been becoming a fast-growing field of study in the past few years and because of the importance of investors' behavior in market performance, it's extremely noteworthy. By studying biases from their orientation perspective, we can divide them into two major groups, past-oriented, and current-oriented biases. In this research, a model had been developed for the past-oriented behavioral bias, which is closely related to the random walk theory. The research sample included the daily price information of 9 different industry indices in the Tehran Exchange Price Index (TEPIX), the index of 50 Top Companies in the Tehran Stock Exchange, and the S&P index in the New York Stock Exchange from 03/25/2011 to 03/19/2019. The results of the ARIMA model based on Markov switching models were measured for the degree of rigidity of these indexes by random walk theory, and then the effect of past-oriented behavioral bias was calculated in each of these 12 indexes by developing a new model. The results indicate that the cement index had the highest past-oriented behavioral bias (57%), followed by the top 50 companies index (46%), chemicals (41%), and oil product index (12%). However, the S&P index had no past-oriented behavioral bias.

One of the basic priorities of any country is to designate appropriate financial strategy to reach an optimized education system to to tenure different jobs. The goal of this research is to determine criteria of importance and skills and knowledge ranking needed by Accounting graduates in order to achieve future financial success. To have access to the research goal, accounting education skills related variables recognized and categorized into 7 principal components (strategic management, analyzing skills, leadership skills, increasing capabilities skills, general skills of Accounting, communication skills and personality skills) are selected and the fuzzy AHP is utilized to data analysis. Research results show that strategic management is placed as the first priority and communication skills is considered as the last one according to professionals' points of view.

Predicting macroeconomic indicators is very important for policymakers and economists. Unemployment is one of the key indicators of macroeconomics that has adverse economic and social consequences. So far, many models have been proposed to predict this variable, but models in which accounting information was used to predict unemployment rate were ignored. The purpose of this paper is to investigate the relationship between aggregate cost stickiness, as one of the known variables in accounting, and unemployment rate. To this end, seasonal macro level time series data of Tehran Stock Exchange (TSE) and macroeconomic data are analyzed in two stages from 2008:2 to 2018:1. In the first stage, the relationship between these two variables is determined by specifying a linear regression model that is estimated using the OLS method. To investigate the predictive power of this model, the RMSE criterion was estimated in two scenarios with and without aggregate cost stickiness. Secondly, the reaction of the unemployment rate in response to a shock from aggregate cost stickiness is estimated by a Vector Autoregressive (VAR) model and the share of this variable is measured in the fluctuations of unemployment rate. The results show that aggregate cost stickiness improves the forecast of unemployment rate in the horizon previous. Also, the shock of aggregate cost stickiness explains about 6.5 percent of unemployment rate fluctuations.

The Supply Chain Network Design (SCND) with perishability is an active research topic. The Agri-fresh Food Supply Chain (AFSC) is a relevant topic to SCND and this study aims to model a new AFSC for a real-world case study. Regarding the traditional AFSC, the geographically dispersed small farmers transport their product individually to market for selling. This leads to a higher transportation cost, which is the major cause of farmers’ low profitability. This paper formulates a traditional product movement model to represent the existing AFSC. The concept of sharing economic approach is employed by the aggregate and collaborative transportation of products to minimize transportation inefficiency. This paper proposes an aggregate product movement with the vehicle routing model to re-design an AFSC for a case study in Iran based on the data of ETKA Company-the largest domestic agri-fresh food supply chain. A four-echelon, multi-period, Mixed Integer Non-Linear Programming (MINLP) approach for the proposed location-inventory-routing model is formulated for perishable products via considering the clustering of farmers to minimize the total distribution cost.

The purpose of the present study was to validate the Adaboost machine learning and probit regression in the prediction of Management's overconfidence at present and in the future. It also compares the predicted models obtained during the years 2012 to 2017. The samples of the research were the companies admitted to the Tehran Stock Exchange, (financial data of 1292 companies/year in total). Data collection in the theoretical part of the study benefitted from the content analysis international research paper in library method and for calculating the data's Excel software was used, and in order to test the research hypotheses, Matlab 2017 and Eviews10.0 were used. The empirical findings demonstrate that The Adaboost's algorithm nonlinear prediction model represents the highest power in learning and prediction (performance of this model) the managerial over-confidence for this year and the following year, proved to be better than the probit regression prediction model.

Market manipulation remains the biggest concern of investors in today’s securities market. The development of technologies and complex trading algorithms seems to facilitate stock market manipulation and make it inevitable for regulators to use Deep Learning models to prevent manipulation. In this research, a Denoising GAN-based model has been designed. The proposed model (GAN-DAE4) consists of a three-layer encoder along with a 2-dimension encoder as the discriminator and a three-layer decoder as the generator. First, using statistical methods such as sequence, skewness, and kurtosis tests and some unsupervised learning methods such as Contextual Anomaly Detection (CAD) and some visual and graphical methods, the manipulated stocks have been detected in the Tehran Stock Exchange from 2015 to 2020; then GAN-DAE4 and some supervised deep learning models have been applied to the prepared data set. The results show that GAN-DAE4 outperformed other deep learning models (with F2-measure 73.71%) such as Decision Tree (C4.5), Random Forest, Neural Network, and Logistic Regression.

This study examines the criterion of value at risk from another perspective and presents a new type of mean-value at Risk model. To solve the portfolio optimization problem in Tehran Stock Exchange, we use NSGA II, MOACO, and MOABC algorithms and then compare the mean-pVaR model with the mean-SV model. Given that, finding the best answer is very important in meta-heuristic methods, we use the concept of dominance in the discussion of multi-objective optimization to find the best answers and show that, at low iterations, the performance of the NSGA II algorithm is better than the MOABC and MOACO algorithms in solving the portfolio optimization problem. As the iteration increases, the performance of the algorithms improves, but the rate of improvement is not the same, in a way, the performance of the MOABC algorithm is better than that of the NSGA II and MOACO algorithms. Then, to compare the performance of the “mean-percentage of Value at Risk” model and the “mean-semi variance” model, we examine both models in the standard mean-variance model and show that the mean-pVaR model, compared to the mean-SV model, Has better performance in stock portfolio optimization.

Price return and P/E are two interesting factors for a lot of investors; The Bohmian quantum mechanics used referring to the time correlation of return and P/E of the stock market under consideration. In this study, we extend the quantum potential concept to determine the behaviour of P/E and also price return in two different industry of Tehran stock market during a time interval of April. 2008 to march 2019. The obtained results show that the quantum potential behaves in the same manner for P/E and price return, also confines the variations of the P/E and price return into a specific domain. Furthermore, a joint quantum potential as a function of return and P/E is derived by the probability distribution function (PDF) constructed by the real data of a given market. It serves as a suitable instrument to investigate the relationship between these variables. The resultant PDF and the corresponding joint quantum potential illustrate that where we have light points in joint quantum potential chart, the probability of those amount of P/E and price return are more than other points. In addition, because of the rectangular shape of the joint quantum potential chart we can say that these two variables behave as two independent variables in the Market.

Predicting financial markets has always been one of the most challengingissues, attracting the attention of many investors and researchers. In this regard, deeplearning methods have been used a lot recently. Due to the desired results, such networks are always in development and progress. One of the networks that is beingimplemented in various fields is capsule network. The first time the classification capsule network was introduced, it was able to attract a lot of attention with its successon MNIST data 1. In such networks, as in the other ones, the parameters are obtainedby minimizing a loss function. In this paper, we first change the classification capsulenetwork to a regression capsule network by modifying the last layer of the network.Then we use different information measures such as Kullnack-Leibler, Lin-Wang andTriangular information measures as a loss function, and compare their results with wellknown models including Artificial Neural Network (ANN), Convolutional Network(CNN) and Long Short-Term Memory (LSTM) as well as common used loss functionssuch as Mean Square Error (MSE) and Mean Absolute Percentage Error (MAPE). Usingappropriate accuracy metrics, it is shown that the capsule network using triangularinformation measure is well able to predict the price of bitcoin for the medium andlong term period including 10, 90 and 180 days.

Traders in the capital market always seek methods to make full use of available information and combine them to find the best buying and selling strategy. The present study uses a linear hybrid system to combine 106 signals from moving averages oscillators and RSI signals in the technical analysis along with two buy and sell bonds. In addition, the system has correctional property and modifies its parameters over time and according to new information. The result of the research on the Tehran Exchange overall index in the period 1380 to 1397 indicates that the system after the optimal training on training data has an average of daily returns of 0/0025, 0/0048 risk, and a daily Sharp ratio of 0/52, which is better than the individual performance of each signal and market performance in daily average return and sharp ratio criterion.