نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 48 (پاییز 1400)

insurance companies as one of the most important financial institutions have a special place in economic growth and development , so that efficient performance of this sector will affect other economic sectors.Therefore, evaluating the performance of companies operating in this industry and identifying their strengths and weaknesses is of great importance. One of the performance appraisal techniques that has received a lot of attention in recent years is data envelopment analysis. In this paper, using the two-stage model of SORM-SBM data envelopment analysis, the performance evaluation of 21 insurance companies operating in Iran with financial indicators has been performed, which in addition to total efficiency, calculates and shows the efficiency of each company in both marketing and profitability. The company's performance is efficient if it has been efficient in both areas. The model values for each company are also calculated and the relationship between these values and total performance is shown

The main purpose of this study is to analyze the cost behavior of the companies accepted based on chaos theory. The purpose of the study is of an applied type. . In this study, 69 companies with 416 observations . Corporate financial data was generated by innovative management software and leadership software. Various tests were used for data analysis such as BDS correlation tests for nonlinearity of time series, Dickey Fuller for correlation, Lyapunov power for turbulent cost behavior. Dickey Fuller test results showed that all of Mana's costs behaviors were the same. The results of BDS test showed that the studied cost behavior is nonlinear and chaotic non-random. As a result, the time series studied are generally chaotic. The results of the Lyapunov test view showed that all the Lyapunov coefficients were calculated for positive cost variables. Therefore, it can be said that the research variables have a butterfly effect. That is, the company's cost behavior is unstable (divergence). According to the Lyapunov power test, non-operational costs are less sensitive and more stable and have high predictability; and investment costs are more sensitive and less stable and less predictable.

The purpose of this study is to investigate the relationship between the net cash flows of the Tehran Stock Exchange funds and the Tehran Stock Exchange index during the period of 2013 to December 2019, using the information of the 10 largest active mutual funds established and active in the Tehran Stock Exchange during this period. .In this study, an index of net cash flows into mutual funds daily and cumulatively is considered as a measure of cash flow compared to the TSE Index (TEDPIX). The results of this test indicate that the two indices are coherent in series and their relationships are significant in the long run. Also, the Granger causality test was used to examine the interrelationships between these two indices.The results of this test showed that there is an interaction between the two indices. This means that in the long run, both indices affect each other so net cash inflows to the funds can be a measure for predicting the overall indices trend but with Attention to the behavioral errors identified in similar articles.for predicting the index cannot be relied solely on net cash inflows into the funds.

The purpose of this study is to create a Financial network to identify stock market leaders using centrality measures.This study finally provides a clustering of superior stocks that can be used as an optimal stock portfolio by investors.The statistical population of all stock exchanges is that the 100 companies with the most capital were selected as a statistical sample over a period of 11 years.Due to the nature of the research ranking, the Kendall correlation coefficient was used to calculate the correlation.The Prime algorithm was used to identify relationships and construct the minimum spanning tree, and the fast-greedy algorithm was used to cluster stocks.The results show that in terms of degree centrality measure, stocks of Sepahan Cement companies,Omid Investment Management and Bank Melli Investment, based on closeness centrality measure, stocks of Sepahan Cement companies,International Building Development, and Khuzestan Steel, based on Betweenness centrality measure, the stocks of Sepahan Cement, Ghadir Investment and Bank Melli Investments, and finally based on the bottleneck centrality measure,the shares of Sepahan Cement, Khuzestan Steel and International Building Development have the greatest impact on the stock exchange network. Also, the top stocks were classified into 11 clusters,each of which shows a strong relationship between its components.

Mutual funds are one of the types of financial intermediaries, including institutions that sell their investment unit to the public, raise funds and invest them in a variety of securities. However, managing mutual funds is fraught with complexities and difficulties that arise from achieving optimal returns and cash flow management as the most important management criteria. Market scheduling, meanwhile, is an important dimension for mutual funds. With this approach, in this study, an attempt has been made to identify and rank the factors affecting the timing of liquidity in Iranian mutual funds. The results of the study showed that the returns of parallel markets and stocks (weighing 0.206), bank interest rates (weighing 0.194) and political developments (weighing 0.193) are the most important external and macroeconomic factors that affect liquidity and liquidity timing in mutual funds. In addition, among the internal factors, the three variables of ability and management skills of the fund (weighing 0.206), net value and percentage of cash assets of the fund (weighing 0.1734) and the risks facing the fund (weighing 0.1726) are the most They have had an impact on liquidity and the timing of liquidity in mutual funds.

Empirical facts derived from research done on the effects of earnings management of listed companies shows that the economy of earnings management has always impose huge costs on economic, social and political experts provide organizations of all sizes. The role of crisis management on financial profit represents earnings management considers failure prediction models. The present study considers this failure in identifying explanatory variables and experimental model design, factors that this research is trying to improve.In this study, 13 variables affecting real earnings management and 56 variables affecting accrued earnings management were included in the model. According to the output of the results, it can be said that profit management in Iranian stock companies is a multidimensional problem; Because the variables related to profitability, liquidity and debt index affect this type of management.The multidimensionality of the factors influencing this process will require coordination between financial and capital market policymakers to reduce the effects of adjusting and reducing earnings management in the stock market.

Relationship Analysis of Risk and Performance Criteria Conservative with an emphasis on the role of intellectual capital: Structural Equation ApproachAbstractConservatism is one of the characteristics of financial reporting that in the form of a limiting principle in the framework of accounting principles and concepts, plays an important role in limiting the optimistic behavior of managers in the position of information producers on the one hand and estimating the minimum income of investors and credit. Providers, on the other hand, are the most important users. The main purpose of this research is to approach structural equations in analyzing the relationship between risk and performance criteria and conservatism with emphasis on the role of intellectual capital. According to the research goal of 138 companies' financial information during the years 1390 to 1397 using structural and software analysis LISREL has tested research hypotheses. Research has shown that there is a significant relationship between risk and conservatism criteria and performance, but the company's intellectual capital cannot play a role in adjusting to the impact of conservatism and performance.

One of the significant problems facing capital market investors is choosing the right securities to invest in and create an optimal portfolio. Since the portfolio selection parameters cannot be considered fixed due to market and price fluctuations, a method that takes into account data uncertainty should be used. Robust optimization is a scientific solution to the problems in which data uncertainty is present. The present study has been conducted for Robust portfolio optimization based on the omega approach. The present paper introduces the linear omega model as a criterion for calculating risk and provides an optimal Robust omega model. Robust approach used in this research is the Bertsimas and Sim approach. In this approach, Robust counterpart presented for a linear programming model remains linear, maintaining the advantages of the linear programming model in the optimal model. The model developed in this research is randomly selected by real data of 20 stocks of the S&P 500 index for three years, the results show the high efficiency of the model that developmenting models under uncertainty conditions. The results also show that if conservatism increases, the value of the objective function will increase.

Study of the comovement between financial markets plays a crucial role in increasing the performance of trading strategies. Due to the strong dependence of Iran economy on oil prices and the fluctuation of the dollar exchange rate in the open market, it is very important to study these macroeconomic level factors on stock market performance for portfolio management. In this research, using time-frequency domain analysis, while discovering the interdependence between financial markets, capital turnover trends in Tehran stock exchange are analyzed and the effect of exchange rate fluctuations and the price of the OPEC oil basket on the indices including TEPIX, industry, banking, automobile, and oil products are studied. The results show that the longer the investment horizon, the stronger this effect is, and the increase in the exchange rate causes the index to increase in order for the market to prosper more, while there is a weak interdependence between TEPIX and oil price. During the investment horizon of 4 to 9 months, any increase in USD encourages shareholders to trade more, while in similar conditions, the increase in oil prices, except in the banking and the petroleum sectors, cause money to flow out of the market in other sectors.

The type of customer service during the warranty is crucial for each complex. The purpose of customer service will be to meet the satisfaction of customers. Many components can contribute to accomplish this goal. One of the most important components is financial components. Today's world is a world of wide developments in all dimensions. The majority of companies are, more than ever, aware that the delivery of after-sales service is very effective in the loyalty and repetition of customer purchases. The intense focus on the quality of service causes the product to be valuable in terms of customers and their loyalty. Therefore, in this study, designing a credit risk management model for the for the Saipa Yadak Company and its Representatives Network using financial components of after-sales service and meta-heuristic algorithms was discussed. The sample studied in this research is the representatives of Saipa Company.The results showed that using financial components including, service cost, performance, good accounting, the amount of collateral and the amount of after-sales service agents have an impact on optimal credit risk management. Also, firefly algorithm and bee colony algorithm have the ability to predict the optimal management of credit risk using financial components.

The purpose of study was to model the relationship between Tehran Stock Exchange indices and oil prices. The statistical population of the present study included total price indices, industry index, price index of 50 companies, and second market index and oil price in the period 2008/09/13 to 2018/11/25. To analyze the data, Copula statistical method was used using R software. Relationship between indices and oil prices was studied using eight capillary normal, Gamble, Clayton, Frank, Jo, Galambos, Hostelries and Copula function, respectively. The results showed that the relationship between the indices and the oil price using the Copula Clayton function was the best. The results showed that the relationship between the indices and the oil price using the Copula Clayton function was the best. Then based on the fitted Copula function, the correlation value was calculated based on Spearman and Kendall correlation coefficients. The results of correlation analysis showed that the correlation between indices and oil prices was positive. most financial variables do not have a normal distribution, the various analyzes used in academic institutions have skewed results, so it is suggested that the relationship between financial variables be modeled using copula and then fitted based on the copula function.

It is impossible to advance the economic goals of any country without financial markets. Since the stock exchange is one of the most important financial markets in the country, and the stock index is one of the important parameters in determining it,s performance, So the stock index and economic development have an important interconnected relationship. Stock market forecasting has been considered as one of the most challenging financial issues and the accuracy of these forecasts is crucial for improving trading and investment strategies in the stock market. The total price of Tehran stock exchange price using intelligent methods. An optimization Bacterial Foraging Optimization Algorithm has been for modeling. In this research, the total index of Tehran stock exchange price data for the 23rd March 2006 to 21rd March 2018. Ten using the total price index data (consists of the highest price, lowest price, closing price and total volume of stocks traded for the day) and finally, by Matlab software, the forecast price index waz calculated. The results of the research show that the algorithm has the accuracy of ninety seven percent ability to predict the total index price of the stock exchange.

Today, markets, especially the coin market, are one of the most important sectors of the economy. What we are seeing today is that a conventional economy cannot respond to immediate market decisions, we have to accept that our economy is traditional; We can not manage the market based on traditional theories, the attitudes of people and the environment and cultural influences affect the market, markets are always a reflection of our performance, they show the impact of our performance. The main purpose of this study is to investigate the factors affecting coin market fluctuations and their ranking in Iran during the years 94 to 97.The present research is correlational in terms of applied purpose and descriptive research method. The statistical population studied in this study is the Iranian coin market (gold price) during the years 1394 to 1397. The results show that changes in gold prices have a positive and significant effect on exchange rate changes at the level of 5%.

Corporate social responsibility is more on the agenda of business organizations for its ability to increase the company's competitiveness. This has led researchers to investigate what factors influence social responsibility affect the core path of the business.So , the purpose of this study is to make managers aware of CSR's consequences. The research used an interpretive philosophy, qualitative approach, and a methodology data approach with an emergent approach, and investigated the consequences of CSR in organizational and social level . In this regard, semi-structured interviews were conducted through snowball sampling methodology with Mellat bank's top managers in Tehran. The method of analysis was based on the Glaser coding approach. At the end of theoretical coding, the consequences of CSR were encoded based on Glaser coding families, the coding of the "anticipated consequence" family. These consequences have two economic dimensions (including increase in brand equity, increase in revenue, reduction of costs and rehabilitation in society) and non-economic dimension (including improvement of recruitment, retention and motivation, customer acquisition and retention, reputation improvement, increase of legitimacy, Health development, environmental sustainability and social, cultural and ethical development( .The results showed that CSR will improve the economic and non-economic condition of the bank.

Forecasting stock prices plays an important role in setting a trading strategy or determining the appropriate timing for buying or selling a stock. Deep Learning (DL) is a type of Artificial Neural Network (ANN) that consists of multiple processing layers and enables high-level abstraction to model data. The key advantage of DL models is extracting the good features of input data automatically using a general-purpose learning procedure which is suitable for dynamic time series such as stock price.In this research the ability of Long Short-Term Memory (LSTM) to predict the stock price is studied; moreover, the factors that have significant effects on the stock price is classified and legal and natural person trading is introduced as an important factor which has influence on the stock price. Price data, technical indexes and legal and natural person trading is used as an input data for running the model. The results obtained from LSTM with Dropout layer are better and more stable than simple form of LSTM and RNN models.

Due to the importance of identifying the factors affecting financial health and predicting the degree of financial health of companies in this study, appropriate indicators have been identified to explain financial health and also predict the degree of financial health of Tehran Stock Exchange companies with an approach based on artificial intelligence. For this purpose, the information of 138 companies during the years 1390 to 1398 and Matlab software were used to test the research hypotheses. Accounting performance criteria used in this research: systematic risk, long-term debt to total assets ratio, current assets to total assets ratio, working capital ratio, instantaneous ratio, account receivable to total assets ratio, return on assets, company growth, growth Sales, are. The results showed that the artificial intelligence algorithm method with a power of more than 90% has the ability to predict the financial health of companies and also among the criteria of financial performance, company growth, return on assets, sales growth and current assets to assets to explain the financial health of companies have the highest impact.

The main objective of this study is to estimate and evaluate the performance of the dynamic realized conditioned autoregressive value at risk model (Realized-ES-CAViaR-Add-RV-SAV) in forecasting tail risk measures (VaR and ES). In this regard, daily as well as intraday (hourly) data of Tehran Stock Exchange Index in the period of 24/6/2014 – 2/2/2021are used. The results of the model are compared to the results of ES-CAViaR-SAV and ES-CAViaR-AS models to investigate the effect of incorporating the realized component to the model. Using backtesting tools such as Bin, POF, TUFF, CC, CCI, VRate tests, Lopez loss function (LL) (in VaR part) and McNeil and Frey test and ranking according to MCS method in The ES part, the efficiency of the models are examined. The results of this study indicate the efficiency of all three models in forecasting the tail risk measures. In addition, the results show that the use of realized criteria increases the tail risk forecasting efficiency.

Managers today deal with complex and unique social, environmental, technological and market trends. Managers are still hesitant to come up with sustainable strategies because they believe the cost outweighs the benefits. Studies show that sustainability practices have a positive effect on business performance. Therefore, the purpose of this study is the sustainability model of the company based on the financial efficiency model based on the P-VAR model. Based on this goal, data related to 91 companies of Tehran Stock Exchange in the period 1390 to 1398 were analyzed. The research variables include 4 variables (profitability, efficiency, debt management and asset management) and secondary indicators in a total of 14 indicators. Data analysis was performed based on secondary variables and indicators. The results of the P-VAR model showed that the interaction between the variables of profitability, efficiency, debt management and asset management is small. Therefore, the mentioned variables do not have much effect on each other. Also, in the long run, these effects either decrease or do not increase enough. This small amount of interaction between variables is also related to the model used in the research.

 This study addressed the effects of financial information on stock price momentum in winning and losing portfolios using data mining methods (neural network and decision trees) in firms listed in Tehran Stock Exchange. The financial analysis included three general categories: profitability variables (return on assets, cash flows, accruals, and profitability growth), leverage and liquidity variables (financial leverage, liquidity ratio, and share issue), and operating efficiency variables (profit margin and assets turnover). The study sample comprised firms listed in Tehran Stock Exchange in the 2008-2019 time period. The results of the data mining model showed that the profitability variables had the greatest effect on the stock future returns in both winning and losing portfolios. This result has an important implication for investors in stock exchanges: when selecting a share, the investor is advised to choose a firm with high profitability on the basis of simple indices the present study introduces.

The purpose of this study is to optimally select stocks using the bat and random forest algorithm. In this study, based on the analysis of 6 variables: stock price to earnings per share ratio, annual earnings growth rate, annual sales growth rate, return on assets, return on equity and free float shares extracted from 181 companies listed on the Tehran Stock Exchange, It has been used during the period of 1394 to 1398. Six scenarios are considered to estimate the accuracy of the two algorithms, so that for scenarios 1 to 6, the algorithms are asked to participate 5, 10, 15, 20, 25 and 30, respectively. The results show that the nature of the random forest algorithm requires training and selection of features, which makes the algorithm faster and increases the convergence time. One of the main reasons for the higher accuracy of the random forest algorithm in scenarios 1 to 3 could be this. In scenarios 4 to 6, due to the increasing complexity of the problem, the accuracy of the random forest algorithm decreases, but due to the random nature of the bat algorithm, its accuracy does not differ much and it can maintain stability in its selection.