نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 46 (بهار 1400)

The concept of financial sustainability has been in the economic literature for nearly two decades. In the theoretical financial literature, firm financial sustainability can be described as a financial system consisting of financial intermediaries, markets, and market infrastructures that are capable of withstanding risk shocks and resolving financial imbalances. Therefore, according to the above argument, the purpose of the present study is to present a proposed model for measuring the financial sustainability of the company by using Econophysics and artificial neural network using a sample of 132 companies listed in Tehran Stock Exchange during 2015-2019. The results of the first hypothesis show that the prediction of financial sustainability based on the Econophysics method can provide better results. Also, based on the result of the second hypothesis, the Bayesian method can predict better financial sustainability. Finally, by comparing the Econophysics and Bayesian approaches to predicting financial sustainability of the firm, it can be concluded that the prediction of financial sustainability based on the economophysical method yields better results than the Bayesian method.

Branches of Bank are one of the most important pillars of digital banking and surveying their performance plays an important role in profitability and achieving the bank's goals. This study evaluates the performance of bank branches using innovative methods. First, important indicators for evaluating branch performance have been identified. Then, the proposed method for data of bank branches has been implemented in the form of a case study. For this purpose, clustering was done first to separate efficient, semi-efficient and inefficient branches. Then, based on the labels created on the data of the branches, classification algorithms and decision trees were used to extract the rules in the data of efficient, inefficient and semi-efficient branches. In the present study, the proposed model of C5.0 algorithm was used due to obtaining the highest accuracy compared to other algorithms. Finally, based on the extracted rules, an expert system was designed to evaluate the performance of bank branches. Clips software was used to design the expert system. In the bank under study, the average increase percentage of cheap deposits during the period to increase the target balance had the greatest impact on performance.

Every investor wants to select the optimal combination of return and risk in order to maximize their utility. In this study, an attempt was made to explain the optimal model for estimating returns and risk in cases where there is a financial crisis and the distribution of return on assets does not follow the normal distribution.For this purpose, we use CAPM with independent and identically asymmetric power distribution (CAPM-IIAPD) and CAPM with independent identically saymmetric exponential power distribution with two tail parameters (CAPM-IAEPD) instead of traditional CAPM. When the assumption of normality is violated, higher moments are used to optimize the model. In the next step, using Polynomial Goal Programming, we calculate optimal portfolios with third and fourth moments.The time horizon of the research from 2011 to 2018 and the statistical population has been all the companies of Tehran Stock Exchange, among which 30 companies have been selected.The results show that CAPM-IIAPD Model is the best model among three models and the adjusted return on risk in optimized models with thirs and fourth moemnts in generalized CAPM models is significantly different from the traditional model and has a better performance.

The purpose of this study is analyzing the relationship between firm financial status and value at risk by emphasizing the role of risk management. Therefore, from value at risk information with the bootstrap simulation between the period of 2012 and 2019 and information of 138 companies in Tehran stock exchange (TSE) for statistical sample companies and the company's financial status criteria (performance measures and firm risk) and management criteria, as a moderating variable firms were used. In this study, the method of structure analysis which tests a specific model of relation between variables is used because this model is a comprehensive approach to test assumptions about the relationships of the observed and latent variables. The results of the hypotheses and model fitting showed that the firm's financial status on the value at risk is significantly effective and the risk management of this connection can be properly adjusted, however, performance measures were higher power in explaining the company's status as well as the value at risk.

Public pension funds in Iran, due to several factors like elder population, accumulation of government debts, increasing the number of pensioners in comparison with the current employees, inefficient management of investments, and lack of good investment policies, will encounter serious crises in the coming years. One of the solutions to tackle this problem is to employ scientific asset allocation models of pension funds. According to the strategic nature of these decisions, in this research, scenario planning was employed to identify the possible scenarios pension funds encountering with. In order to identifying the most important and relevant uncertainties and scenarios, a combination of Fuzzy Delphi Method, Wilson Matrix, and Morphological analysis were used. Findings depicted five scenarios of oil Inflation, currency inflation, non-oil inflation, and resistant economy are the most probable scenarios and pension funds will have to allocate their assets according to the characteristics of these scenarios. Next, best worst method (BWM), was employed to extract each scenario weight and asset classes weights in each scenario as well. To our knowledge, this was the first time to combine such methods in an integrated manner to tackle the asset allocation problem.

In this paper, a multi objective multi stage stochastic model is proposed to portfolio selection. This model takes into account both the investment goal and risk control at each stage. A scenario generation method is proposed that acts as the basis of the portfolio management model. Scenarios for multistage portfolio management are proposed that use by consumption that rate of returns are not correlated during stages. One of the most important aspects of this model is using transaction cost in model and providing this ability that investors could add or withdrawal cash during time. In the end some numerical example are illustrated and model effectiveness proved. As is presented using stochastic programming with recourse and combination of this model with scenario generation model provides this possibility for investors to plan their medium and short term investing. As can be seen result of the model proved effectiveness of the model in financial markets. As result presented having such tool that investor could adjust his or her portfolio during time according to targets such as maximizing rate of return and minimizing risk of his or her decisions could bring powerful superiority in competitive financial markets.

The purpose of this study is to investigate the factors affecting the stock price crash risk of listed companies between the years 2009 to 2016. Based on this, the researcher first calculated the stock price crash risk based on two different criteria and then tested the internal and external factors of the company on the crash risk. The statistical model used in this study is regression model and data type is panel data type. Dependent variables include calculating stock crash risk based on the first criterion, the Hutton model, which considers the monthly price fall in a fiscal year for a company when that company experiences a monthly return of 3.2 standard deviations below the company's average specific monthly return for the entire fiscal year and second criteria, Chen model, Based on the negative skewness criterion in the company's stock returns, Independent variables include in-company factors (financial ratios) and external factors. Based on the findings of the present study, there is a significant relationship between dependent variables and factors within the company and the organization external factors, can help in predicting stock crash risk.

The purpose of this study was to determine the optimal level of risk and optimal capital structure based on the logarithmic model of boundary operating efficiency in banks. In this study ، data envelopment analysis based on logarithmic boundary function modeling with assumption of geometric convexity is used to evaluate the efficiency of each Iranian bank for the performance years under study. Among the 28 factors identified through content analysis and knowledge domain analysis, sixteen factors included inputs, outputs, risk, Capital Structure (Minimum Capital, Minimum Capital Risk and Minimum Leverage Ratio), Bank Level Properties (Bank Size, Return on Assets, Return on Equity, Deferred Facility Rate and Liquidity Rate), Factors affecting efficiency, risk and capital structure of banks are identified. The research method relied on a theoretical-applied research design and a descriptive-deductive inference method. Twenty-five banks were selected from the Iranian banks by elimination method and studied over a six-year period ending on 29/12/97. Validation of estimation models based on coefficients of determination showed that regression equations have high explanatory power in determining the optimal level of risk and optimal capital structure based on different measures and Fisher's significant level of significance and generalizability of the estimated results are significant.

The purpose of this research is designing a credit rating model for issuers and tools for financing Islamic securities in the Iranian capital market. To do this, three major steps were taken. The first step was to identify the evaluation criteria or the risks associated with the Islamic securities, which was carried out by the experts and a review of theoretical basics. The second step, is modeling of Islamic securities using adaptive-network-based fuzzy approach in which the mean error of the training of all main and subset models was below the threshold. The third step is to apply adaptive fuzzy neural network modeling in credit rating of Islamic securities. In order to do this, the issuer’s ranking was used in the first stage and the results of the research showed that the issuer of the government had the least risk and private companies had the highest risk. In the second stage, for ranking financial instruments, the results showed that for issuer of government, treasury bonds had the lowest risk and forward bonds had the highest risk. For the issuer of state-owned companies, the forward bonds had the highest risk and lease bonds had the lowest risk.

Network theory can be used to better understand the financial markets, which are one of the most complex concepts in today's world. Financial networks are a set of Nodes(vertices) and edges. Each node represents one stock and each edge indicates the relationship between the stocks. Early studies in this area used correlation coefficients to describe short-term relationships between stocks and market modeling. This modeling method applies to data that have symmetry and only determines the presence or absence of a relationship between stocks, and does not specify the type of relationship, the direction and weight of that relationship. In recent years, financial time series modeling has been done using the concept of cointegration. In this study using the time series stationary test,unit root tests of Dickey Fuller and KPSS ,and Engle-Granger test, cointegration based network were designed. In order to analyze this network, the centrality measures such as: centrality degree, between centrality, closeness centrality, page ranking and Bonacich were used. The results of this study show that cointegration networks can provide more complete graph of markets, and also central measures analysis can play an effective role in stock portfolio selection and provide a good model for understanding stock relationships.

The transfer of financial crises between different markets in a economy indicates the existence of channels of contagion. Parallel markets are closely linked to other markets in any economy. The channels of shocks and financial crises to other markets can include information, macroeconomic variables, investment behaviors, etc. in this study, the existence of volatility overflow between coin market, oil, currency and stock markets was investigated using monthly data during 2009 to 2017. the results indicate the existence of the fluctuations, as well as structural failures due to the existence of this overflow. Granger causality tests also confirmed the existence of causal links between financial markets. Between coin and currency markets, exchange and oil are two - way causality and between the oil and gold markets, exchange and the stock are one-way causality. in this study, the Granger causality tests, structural failure tests, the correlation of variance and other necessary tests were used

Investing in the Iran Energy Exchange is one of the ways to achieve returns. Among the capital asset pricing models, one of the most important new tools for risk and return determination is the Fama and French five-factor model. In this study, we examined the power of explaining the return on investment portfolio consisting of 40 companies operating in Iran Energy Bruce from 2009 to 2018 using the five-factor model of Fama and French. For this purpose, we have used F-Limer and Hausman statistical tests and multivariate regression analysis method in three-factor and five-factor models of Fama and French to explain the efficiency of energy portfolio returns. The results of this study show that by adding the two factors of profitability and investment to the three-factor model, the coefficient of determination increases from 0.85 to 0.96. Among these five factors, the value factor has the greatest impact on energy portfolio returns. Also, with the addition of market factors, the value and profitability of energy portfolio returns increased, but size and investment factors were inversely related to energy portfolio returns and reduced it.

Investors are faced with a wide range of investment options. Considering the growing demand for energy and the limitations of non-renewable resources (RES) , the importance of modeling and evaluating investment in RES has become significant for both investors and policymaker. The purpose of this study is to model and evaluate investment in RES with the aim of attracting investors without delay by determining the optimal level of the RE tariff by applying the Real Option approach (ROA). For modeling, a five-factor set of uncertainty, delay option, climatic differences has been considered and for implementation, a combination of backward dynamic programming algorithm, Monte Carlo simulation and geometric Brownian motion has been used. The results show investing in RES without considering the subsidy is not attractive for investors to investment without delay and then investors and policymakers need to apply ROA to evaluate and invest. The optimal tariff is estimated at an average of 503,000 Rials and the tariff should be adjusted according to the climatic conditions. By technology advancement, Implementation of CO2 trading scheme, the investment value increases and the optimal subsidy is reduced. The optimal subsidy is directly related to tax rate, capacity, exchange rate, discount rate and volatility.

The present study has endeavored to represent a more precise model to calculate the risk of banks in this study by ARIMA-GARCH-COPULA Model has been introduced.In obtaining the iid distributions and variance estimation the mean model and conditional variance have been determined and estimated simultaneously.In so doing, the ARIMA methodology has been employed to model the average return on assets of the study, and for modeling the research conditional variance of GARCH have been applied. Also mean error criterion has been used to compare the different models of VAR estimation, and for the purpose of testing statistical results backtesting methods have been employed. Based on mean error criterion, the proposed model of the study at hand has demonstrated the most accuracy The GEV model derived from the EVT has been ranked second The output of the Dow ranking method, however, has been very similar to one another According to Dow ranking method, the GEV model has had the lowest loss function at 5% level of significance, and at 1% level of significance, the HS model has demonstrated the least loss function. ES calculations have also been carried out for the four models with ARIMA-GARCH-COPULA model showing the least loss.

Bankruptcy as a systematic issue can cause significant losses for financial institutions and countries, disrupt resource allocation and slow economic growth. Accordingly, bankruptcy is a phenomenon that all central banks, financial and credit institutions pay particular attention to it. Signs of bankruptcy appear before it occurs. Disability or failure is an important warning sing before bankruptcy which is known as the stage before it occurs. In this paper, we attempted to identify pole banks by using the financial statements of Iranian banks over the period 2006-2017 and also by using Logit model and to analyze the factors affecting the probability of occurrence of fraud in the Iranian banking system by designing an econometric model. The results indicate that the cost to income ratio has a positive and significant effect on the probability of failure and the performance index ROE had on inverse and significant effect on the dependent variable. Therefore, continuous monitoring and observation of these two variables for banks can provide an appropriate warning of the possibility of a bank failure.

One of the most important issues in finance is how to choose an investment portfolio. Activists in this field are seeking to select a portfolio that controls risk with high return. Due to the increasing limitations of the capital market, the efficiency of classical methods has been discussed. Hence, researchers have turned their attention to metaheuristic algorithms. The aim of this study is to determine the optimal portfolio of pharmaceutical companies accepted in the Tehran Stock Exchange by two methods of multi-objective genetic algorithm (NSGA-II) and maximum Sharp ratio. In this study, the multi-objective genetic algorithm (NSGA-II) is under Conditional Value at Risk criterion. Also, the data of 13 companies in the period of 90-97 were used to form the portfolio. The results show that in the multi-objective genetic algorithm (NSGA-II) method, the stock with the lowest Value at Risk gains the most weight in the optimal portfolio. Also, the optimized portfolio by multi-objective genetic algorithm is more return and at the same time less risky.

Over the past few decades, the number of universities has grown exponentially, but many of them are financially constrained. In the meantime, financial performance appraisal can support university administrators in making appropriate decisions by recognizing financial status. Accordingly, the purpose of this study was to codify a model for evaluating the financial performance of the university. This research has been applied in terms of the purpose, applied-developmental research and descriptive research in terms of the method which has been done intermittently. The statistical population of this research was all units of Islamic Azad University throughout the country. The statistical sample size was selected based on Morgan's table of 214 units of IAU. To collect data from the questionnaire, the Delphi method and the existing university documents were used. In order to analyze the research data, from Delphi methods and adaptive neuro-fuzzy inference system, adaptive neuro-fuzzy inference system combined with genetic algorithm and adaptive neuro-fuzzy inference system combined with a particle swarm optimization have been used. The research results show that among the designed systems, adaptive neuro-fuzzy inference system combined with a particle swarm optimization with the least error is able to evaluate and predict the financial performance of the university.

Oppose to traditional risk management where individual risks are managed separately in "risk silos", corporate risk management enables companies to manage a wide range of risks in a seamless and comprehensive way. International studies have shown that the evolution of traditional risk management to a holistic perspective and the adoption of Enterprise risk management have enhanced the firm's performance from a strategic perspective. This study investigates the impact of enterprise risk management on the performance of listed companies in the Tehran Stock Exchange (measured by Tobin's Q ratio) in the merger and acquisition process using Heckman's two-stage model over the period 1392 to 1397. The statistical sample of the study consists of 54 target companies of Tehran Stock Exchange. The results of the first stage showed that the variables of firm size, financial leverage and industry control variable had a significant relationship with the determinants of enterprise risk management. Secondly, it was confirmed that organizational risk management had a significant impact on the performance of listed companies in the merger and acquisition process.

Risk estimation cannot deliver appropriate reliable predictions by focusing on one or two models and considering the irrelevant factors. This study aims to estimate the value at risk (VAR) and the conditional value at risk (CoVaR) in the Tehran Stock Exchange using Fréchet distribution (FD). In doing so, generalized extreme value (GEV) is used with the help of Friche distribution approach. In this study, the return of 21-day and 63-day data of the time series of the total index, free-float-stock index, and index of 50 active companies of Tehran Stock Exchange during 01/01/2012 to 12/29/1398 have been used. The obtained results through the estimation of three parameters of GEV, including location, scale, and shape, have shown that the parameter of distribution shape within all 21 and 63-day periods of each indicator is positive, and the distribution for indexes studied follows FD as the second type of generalized distribution of GEV. By using the same pattern, the measurement of CoVaR and VaR has presented that it is possible to estimate CoVar and VaR through the use of FD and CoVaR is higher than VaR within the whole range of alpha.

The present study examines the contagiousness of the Tehran Stock Exchange from the price of parallel assets using the dynamic neural network. To perform calculations, the time series of coin price variables as a representative of the gold market, the average price per square meter of residential building as a representative of the housing market. The price of each barrel of Iranian crude oil and the US dollar exchange rate and their conditional fluctuations as explanatory variables and the total index of Tehran Stock Exchange and its conditional fluctuation as the target variable from 1387 to 1397 are examined daily .The dynamic neural network is evaluated with four input variables and one target variable with different neurons with the MSE criteria, and the models with 20 neurons and 10 neurons have the lowest MSE, .Research results show that the stock exchange has a maximum of two lag from competing markets has become contagious, indicating the poor performance of the Tehran Stock Exchange. The results show that the proposed neural network patterns have a high power in predicting the index of Tehran Stock Exchange and its fluctuations from 1387 to 1397 as in-sample forecast and in 1398 as extra-sample forecast.

Major developments in the business environment, such as globalization and the rapid pace of change in technology, have increased competition and management difficulty in organizations. In complex environments, organizations need managers to consider these inherent complexities when making important decisions. Effective risk management is an important part of this decision-making process. The purpose of the present study is to evaluate corporate risk management by analyzing the relationship between gray matter and entropy weight in listed companies of Tehran Stock Exchange. The sample included 20 companies during the years 2013 to 2018. In this study, we first sought to find relative weight for measuring corporate risk management by using variables (strategic risk management, operational risk management, risk management reporting and risk management non-compliance). Then, based on the results of the entropy analysis, we will try to determine the rankings of the companies in terms of risk management performance using the gray relationship analysis in the companies. The results of this study indicate that many companies have high performance in risk management.

The purpose of this paper detecting the variables affecting on Bitcoin price using daily Time series data from 2015 to 2019 invoking two method of Bayesian model Averaging and Weighted-Average Least Square. The results of this study show that the price variables of cryptocurrencies with different creation mechanisms from Bitcoin and also the number of circulating cryptocurrencies with similar mechanism to Bitcoin and the volume of liquidity of US dollars affect the price of Bitcoin. On the other hand, the Forex market currency pairs, such as the dollar to Canadian dollar, the dollar to Australian dollar and the dollar to New Zealand dollar, which are less valuable than other major currency pairs in the Forex market, affect the price of Bitcoin. Also, the variables in the number of bitcoins, the number of cryptocurrencies in circulation with a different mechanism from bitcoin, the global price of gold and the number of searches for the word bitcoin in Google on its price have low coefficients. Overall, the results of the two methods of Bayesian averaging and Weighted Averaging Least Square are largely the same, and the use of the optimal pattern selection method confirms this.

Stock price forecasting is a challenging and attractive topic. Investors are interested in being able to predict the price of different stocks in financial markets. This paper presents a hybrid model that predicts the final stock price for the next day based on the adaptive neuro-fuzzy inference systems (ANFIS) and Return Neural Network (RNN) algorithm using historical data and indicators. Then the results of this model and the status of market rumors enter the fuzzy expert system based on the output of the fuzzy neural system and the return neural network along with the market rumor status and finalize the forecast. The combined model proposed to predict the stock price data of Mobarakeh Steel Company of Isfahan was implemented. In this study, for research data, the data of Tehran Stock Exchange Company related to the stock data of Mobarakeh Steel Company of Isfahan from April 26, 2016 to March 20, 2017 has been used. Four technical indicators used in this study are: Moving Average(MA), Exponential Moving Average(EMA), Relative Strength Index(RSI), and Moving Average Convergence Divergence(MACD). These variables have been used as the input of the adaptive neuro-fuzzy inference systems(ANFIS) to predict the final price of the next day's shares.

Convolutional neural networks are being applied to identification problems in a variety of fields, and in some areas are showing higher discrimination accuracies than conventional methods. Hence, in this research, an attempt is made to apply a convolutional neural network to the prediction of corporate bankruptcy. The financial statements ratios has been choice 66 companies that have been delisted from the Iran Stock Market due to de facto bankruptcy as well as the financial statements of 66 listed companies over 2000 to 2019 financial periods. In this method, a set of financial ratios are derived from the financial statements and represented as a grayscale image. The image generated by this process is utilized for training and testing a convolutional neural network. The images for the bankrupt and continuing enterprises classes are used for training the convolutional neural network based on GoogLeNet. The findings shows, in prediction of going concern of firms, Convolutional neural network has predicted with 50 percent of precision. This means that 50 percent of continues firms and 50 percent of bankrupt firms has been predicted precisely.

Today, the debate on the recovery and reuse of consumer products has become particularly important. Since the closed loop supply chain is not only a stream of forward but also a reverse flow, therefore, companies are succeeding to create a direct and reverse supply chain of integrity. In the present study, after reviewing the subject literature, a more completed and comprehensive model is presented than the previous models. The model is multi-objective, multi-level, and single-product with product return in the uncertain conditions. The objective functions of the model include minimizing costs, increasing the profit from the quality of the recycled product, increasing the cost savings from recycling and environmental impacts. In order to solve the multi-objective problem approach (TH) is used, which is one of the methods for converting multi-objective functions to single-objective. To validate the proposed model, a numerical example of the design was solved and for analyzed using the GAMS software. And the results of the sensitivity analysis are described below.

This study seeks to analyze the effects of exchange rate movements on an investment risk of the banking industry by applying the idea of risk separation into two sorts of the period, the high and low risk. In line with this, the daily time series data, from 26th March 2011 to 19th February 2020, and a hybrid model of exponentially conditional heteroscedasticity and Markov- Switching approaches have been used. The results of this paper, based on the hybris MS-TGARCH model used in the investigations of (Bibi, 2019) and (Aloui and Jammazi, 2020), are statistically significant and prove the accuracy of the hybrid model in the case of Iran. Therefore, based on this finding, it could be possible to more precisely analyze the effects of different determinants on the modelled risk. Regarding this approach, the impacts of exchange rate movement on the investment risk of the banking industry are far more in the high-risk periods than those of low-risk ones.

The moral investment movement that began in the 1960s in the United States has recently led to a massive move around the world. Growing cases of corporate scams and scandals have pushed investors to consider the quality of corporate governance and the ethics of their behavior. Also, investors are becoming aware of the desirability of moral aberration of assets.The growing influence of institutional investors has strengthened this awareness. Hence, in order to research in this field, there should be an understanding of the progress made in constructing models that are consistent with financially ethical considerations. We use multiple methodologies to achieve this goal. To obtain the ethical performance scores of each asset, based on the investor's preferences, a hierarchical process approach has been used. A multi-faceted decision-making method is used to obtain the rating of each asset based on the investor's rate on the financial benchmark. Model of portfolio optimization is available to obtain diverse, reliable, and well-matched portfolio portfolios. The purpose of this model is to maximize the financial purpose as the primary purpose and maximize the ethical goal adopted by the investor.

In the financial-behavioral perspective, analyzing the impact of investors' psychological components, including their attitudes and behaviors on investment decisions, can lead to a fair distribution of services to capital market participants. The creation of such justice requires an understanding of investors' decision-making behaviors. Transactions should be based on the psychological components of investors in the Tehran Stock Exchange. The research course is from 1392 to 1396 and the statistical sample consists of 138 companies listed on the Tehran Stock Exchange. The psychological components of research include rationality, optimism, investor pessimism, loss avoidance, and momentum. The research findings indicate that there is a significant positive, Also positive relationship between the variables of pessimism, optimism of investors, and the trend of the trading volume; however, there is no significant relationship between the variables of investor rationality, loss, and momentum with the trend of trading volume volume. key words: Investor Psychology, Volume Trend

In this study, the investment management problem in the Tehran Stock Exchange and OTC companies in Iran as a stock portfolio optimization problem is investigated. This model has two objective functions including risk minimization and return maximization. The constraints are including the assigning all budgets to the companies considering budget limitation. In order to deal with the uncertainty conditions in the model parameters, the chance constraints approach is used and the objective functions are considered as a single problem using the goal-programming method. To solve the problem in the two-purpose mode, the augmented epsilon constraint method is used. According to the numerical results, it can be seen that problem solving in two-objective mode is able to produce Pareto solutions that do not dominate each other in a feasible space. Also, in case of uncertainty, the use of goal-programming leads to numerical solutions with the appropriate level of performance and costs are consistent with reality. In fact, the problem results in both multi-objective and single-objective situations can be implemented in real-world conditions. Finally, it can be said that the use of computational results of this study can be used as an operational tool.

By anticipating financial turmoil, it is possible to take the necessary precautions before financial distress occurs by managers and investors. This study compares two algorithms for prediction of bankruptcy using Artificial Neural Network (ANN) and Neural network optimized metaheuristic Firefly Algorithm (FA). To run test, first initial values are set for the network weights and biases and then during the optimization process, a population of different weights and biases is generated by FA algorithm. The conversion function used in the output layer is linear and for the middle layer a non-linear sigmoid function is selected. To conduct this research, the data of 79 companies listed on TSE during 2012 to 2015 were collected and analyzed statistically by backpropagation neural network and FA algorithms. The results show that FA, compared to ANN predicted the companies’ bankruptcy much better. Also, FA Algorithm maintains a good correlation between bankrupt and non-bankrupt companies, just like real data.

Given thechanges that have taken place in the world today,in developing countriesthat face many threats,these countries need appropriate solutions tosolve their economicproblems tomake better useof theirGod-givenfacilities and wealth.Oneof the important strategies is investment development.Due to limited resources,inaddition tothe issue ofinvestment development,increasing investment efficiency and awareness of the riskofstock falls are among the most important issues.The purposeof thisstudywas to investigate the effectofmanagerial abilityon the investment efficiencyof companieslisted on theTehran StockExchange andto investigate the effectof investment efficiency onthe risk of fallingstock pricesof companieslisted ontheTehranStockExchange usingthe methodThe data panel isfor the period 2011-2018.The results showedthat managerialability has a positive effecton investment efficiency so that if themanagerial abilityof a unit increases,investment efficiency/0005.The unit increases;The risk of falling stockpriceof the first benchmark of theprevious year and investmentefficiency have a negative effect on therisk ofstock falling, so that if the risk of falling stock priceofthe first benchmarkof the previousyear and the investment efficiencyof a unit increases,the riskof fallingstock is0.08 And decreasesby1.54 points;The riskof fallingstock priceof the second benchmark of theprevious year and investment efficiency have a significant negative effect onthe risk of stockfalling, sothat if the riskof fallingstock price of thesecond benchmarkof the previous year and the investmentefficiency of one unit increases,the risk ofstock falling is/07Anddecreasesby1.81points