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Open Access Article

1 - Explain the role of financial and economic variables on stock returns With Markov-switching Model
Atefeh Yazdani Varzi erfan Memarian ali nabavi

In the research, Explain the role of financial and economic variables on stock returns With Markov-switching Model.  Based on information collected from the two codal.ir and TSE.ir websites, availability and momentum. contrarian strategies have been dealt with in t More

In the research, Explain the role of financial and economic variables on stock returns With Markov-switching Model.  Based on information collected from the two codal.ir and TSE.ir websites, availability and momentum. contrarian strategies have been dealt with in the market. From among 318 companies from those listed in Tehran Stock Exchange for the period of 2011-2017 and based on restrictions, 108 companies have been selected as research samples.  Then, using various statistical models, three statistical models of autoregressive time series (with no help from auxiliary variable), linear regression, and Markov-switching have been applied. Using the model’s goodness of fit criterion, these three models have been compared and the best one has been selected. Based on selected model, stock trading strategy for the next 12 months has been predicted. To collect data and perform statistical analyses, Excel spread sheet, as well as SPSS and R software packages have been used. According to the findings, from among minor variables (base volume, institutional investment, and free float) and major variables (currency and inflation rates), only three variables from minor types (base volume, institutional investment, and free float) have been effective on stock trading strategy; and, these variables can be used as auxiliary variables to predict return on stock and to specify stock trading strategy in future, as a result. Manuscript profile

Open Access Article

2 - Predict the Financial Limitations of Companies Accepted in Tehran Stock Exchange Using the Relief-Svm-Caiid methods
maryam salmanian hamid reza vakilifard mohsen hamidian fatemeh sarraf Roya darabi

Predict the Financial Limitations of Companies Accepted in Tehran Stock Exchange Using the Relief-Svm-Caiid methodsAbstractDiscussion of financial constraints is one of the key issues facing all companies. Predicting financial constraints is an important phenomenon for More

Predict the Financial Limitations of Companies Accepted in Tehran Stock Exchange Using the Relief-Svm-Caiid methodsAbstractDiscussion of financial constraints is one of the key issues facing all companies. Predicting financial constraints is an important phenomenon for investors, creditors and other users of financial information. This research uses the information of 7 financial years during the period 2012-2017 and using financial information of 213 companies to study the factors affecting financial limitation and its prediction using artificial intelligence algorithm method (backup algorithm classification algorithm and the rule-oriented algorithm Chaid). In the first step, using the Relief Algorithm, among the initial research variables, five variables of the ratio of total operational assets to total assets, the ratio of total debt to the total assets, the kbitwin, the return on sales, and the ratio of institutional owners were selected as important variables in the company's financial constraint, respectively. The results also showed that the three-class support algorithm using selected financial data has the ability to predict future financial constraints with a power greater than 80% and more than the law-governed algorithm.Keywords: financial constraints, Machine learning method, financial variables and corporate governancejel: M41-B26-C63 Manuscript profile

Open Access Article

3 - Presenting a pattern for differentiating winner and loser companies using financial and non-financial variable in Tehran Stock Exchange
Mehran Matinfard

The main purpose of this study is to analyze the strategy of synthesizing the fundamental financial and  non-financial variables in order to present a pattern for ranking companies in Tehran Stock Exchange , and eventually classifying them as winner and loser. To More

The main purpose of this study is to analyze the strategy of synthesizing the fundamental financial and  non-financial variables in order to present a pattern for ranking companies in Tehran Stock Exchange , and eventually classifying them as winner and loser. To confirm the offered ranking and to demonstrate how the companies were differentiated, the researcher tries to determine whether there are any significant differences in the studies financial and non-financial variables between the winner and loser companies. The hypotheses offered are based on two main axes. First, the variables under study for the winner companies are much stronger than those of the loser companies. Second, winner companies enjoy a higher actual return average of 16.3 percent. Besides, their actual return is 17.4 percent higher than that of the market. This research study was conducted in a five-year period, from 1382 through 1386, using the method of average comparison and ranking comparison of a variable in two independent societies (t-Student and U—Mann Whitney tests). The findings of the study revealed that winner companies possess a more favorable cash status, financial leverage, profitability, and non-financial indexes (indicators). Moreover, it was concluded that activity and market’s value indexes (indicators) were not significantly different between the two groups of companies.  Manuscript profile

Open Access Article

4 - Explaining the Financial Factors Affecting Turnaround from the insolvency of Companies Listed on the Tehran’s Stock Exchange
kazem harounkolai Seyedali Nabavi chashmi ghodratolah barzegar iman dadashi

10.30495/eco.2020.674214

The aim of this paper is explaining the financial factors affecting turnaround from the insolvency. For explaining financial affecting turnaround from insolvency 54 variables were selected from relevant studies. The information of 200 cases of distressed companies which More

The aim of this paper is explaining the financial factors affecting turnaround from the insolvency. For explaining financial affecting turnaround from insolvency 54 variables were selected from relevant studies. The information of 200 cases of distressed companies which were under recovery from distress was extracted between 2001 and 2017. Appropriate statistical methods for the process of refining variables have been performed through paired mean comparison tests as well as exploratory factor analysis using main components. Then, by filtering the variables using audit analysis and in the form of linear combinations, audit functions were formed. The results showed that the financial ratios of current liabilities to total assets, net profits to sales and sales to current assets have the most power to explain the companies’ turnaround. Manuscript profile

Open Access Article

5 - Explanation of Financial Variables Effective in Predicting Turnaround: An Artificial Intelligence Approach
Kazem Harounkolai Ghodratolah Barzegar

10.30495/eco.2023.1982463.2737

The main aim of the research was to identify the financial variables that are effective in predicting turnaround of the listed companies in the Tehran Stock Exchange and to predict turnaround by using artificial intelligence method. For this purpose, the information of More

The main aim of the research was to identify the financial variables that are effective in predicting turnaround of the listed companies in the Tehran Stock Exchange and to predict turnaround by using artificial intelligence method. For this purpose, the information of 173 Distress Companies that came out of distress and turnaround was extracted during 1383 to 1399. Artificial Intelligence approach was used to analyze the data. In this approach, by using Lars and Relief Feature Selection Algorithms, 10 out of 54 financial variables which were effective in turnaround of companies were identified and then, the Learning Algorithm of Support Vector Machine and Decision Tree were used to evaluate the accuracy of the results of the identified variables in predicting turnaround. The results showed that Lars Feature Selection Method and Vector Machine Algorithm Support have better performance in predicting the time to exit from distress as compared to the Relief Feature Selection Method and Decision Tree Algorithm. Also, regardless of feature selection methods, support vector learning machine has a higher predictive power as compared to decision tree. Manuscript profile

Open Access Article

6 - Explain of the best capital structure with investigation of the relationa between financial leverage and some of functional variable
M. Fatoreh Bonabi Y. Heyvadi A. Sahebgharani

Maximize of capital structure returns and decision making with investigation of financial and functional variables is the two important subjects for management. In fact studying these variables give important signals to managers about the profitability of the firm, solv More

Maximize of capital structure returns and decision making with investigation of financial and functional variables is the two important subjects for management. In fact studying these variables give important signals to managers about the profitability of the firm, solvency and the financial strategy of the firm. In this research we attempt until with investigation of relation between financial leverage and some of financial and functional variable, designing a model for maximize the capital structure return. For reach to research goal we study the TSE̛ companies from 1381 to 1392, then we have chosen 100 sample and with the some of research method that consist of F- limer, Hausman and regression assumption test we examine the research hypothesis. Finally the results showing that there are considerable relation between financial leverage with Q/B ratio, profit quality, cash return of assets, net profit margin and growth rate of assets. Also there aren’t considerable relations with effective tax ratio and collateral value of assets. Manuscript profile

Open Access Article

7 - .Application of Meta-Heuristic Algorithms in Predicting Financial Distress using intra-corporate (Financial and non-financial) and Economic Variables (Grasshopper Optimization and Ant Colony Algorithms)
فریدون مرادی احمد یعقوب نژاد آزیتا جهانشاد

10.30495/fed.2023.1962198.2734

. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this rese More

. Abstract The purpose of this study is investigating the capability of Grasshopper Optimization Algorithm (GOA) in more accurately predicting the financial distress by-using intra-corporate (financial and non-financial) and economic variables. The method of this research is improving the performance of the basic model of Multilayer Perceptron Artificial Neural Network (ANN-MLP) by-using a hybrid model with GOA (MLP-GOA) and Ant Colony Optimization Algorithm (MLP-ACO). The statistical research population of companies active in Tehran Stock Exchange during a 7-year period (from 1391 to 1397) included 476 companies, and finally, after systematic elimination, there were 289 qualified companies (including 2023 observation year-company). Checked and screened. The results showed the ability of ANN-MLP model to predict financial distress by-using financial and non-financial variables, and in addition the hybrid models (MLP-GOA and MLP-ACO) had been improved this ability. The accuracy of the MLP-GOA model for the year t, year t-1and year t-2 (before financial distress occurs), respectively are 97.30%, 94.53% and 91.30% that higher than the accuracy of the basic model and the hybrid MLP-ACO model. Although, entering the economic variables has increased the capability of all models significantly but the results showed that the financial distress is more affected by intra-corporate variables and the effect of economic variables has already been considered through the effect on financial events recorded in the accounting system. The results of this study can be used by company managers, banks and rating and credit institutions, insurance companies, financial analysts, investors and investment companies in assessing the risk of financial distress to make appropriate decisions and actions. Manuscript profile

Open Access Article

8 - بررسی توانمندی الگوهای پیش بینی کننده بحران مالی
زهرا پورزمانی رضا کی‌پور مصطفی نورالدین

Manuscript profile

Open Access Article

9 - Localization of systematic risk assessment patterns Based on financial and non-financial variables
alireza eslampour roya darabi

Financial and economic decisions are always at risk due to future uncertainties. Therefore, one way to help investors is to provide investment risk forecasting models. But as these projections are closer to reality, decisions that are based on such predictions will be m More

Financial and economic decisions are always at risk due to future uncertainties. Therefore, one way to help investors is to provide investment risk forecasting models. But as these projections are closer to reality, decisions that are based on such predictions will be more correct. The main purpose of the present experimental study is to predict systematic risk with emphasis on financial and non-financial variables. The statistical population of this research is the companies accepted in Tehran Stock Exchange. The data of the study consist of 552 Firm-year from 2012 to 2017. To test the hypotheses, the artificial neural networks approach and night worm algorithms, decision tree and regressor of backup vector machine have been used. The results of this study showed that the results obtained from the hypothesis test showed that all three of the algorithms have the power to explain systematic risk. Manuscript profile

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