نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 55 (تابستان 1402)

Organizations have been placed in a complex economic, technological, political and social environment and grow along with other businesses by trying to achieve their strategic goals. What is considered as one of the main components in business continuity is creating and maintaining the company's competitive advantage. This research measures expenses, actual cost, cash, and inventory strategies based on consistent decisions in industry group companies.In this research cost strategies, actual cost, cash and inventory have been measured based on similar decisions in industry group companies, among 114 companies accepted in the Tehran Stock Exchange and for the period 2014-2019, with a total of 624 observations are Available for research. In this research, variables were identified by using interviews with experts. The statistical method used in this research is the multivariate regression method using the panel data method. The results of research hypotheses show that the strategies of inventory, cost, cash and expenses in each company are significantly influenced by similar strategies of peer companies in their industry.

Trade and relations between countries are a necessary condition for the financial convergence. With this approach, the present study investigated the impact of the Iranian stock market on the stock market of Iran's trading partner in the MENA region. for this purpose, information about the total stock market index of selected countries was collected during September 2015 to June 2022 and index fluctuations calculated using wavelet analysis. Next, the VAR model was estimated and the Granger causality test was performed. Finally, the effect of the news on the country's stock market was investigated using GARCH models. The results of wavelet analysis showed that, the range of stock market fluctuations in MENA countries has increased. Based on the results of the VAR model and Granger causality test, Iran's stock market is unilaterally affected by fluctuations in the stock market of trading partner and OPEC member countries, including Kuwait, Oman, Qatar, Saudi Arabia, the United Arab Emirates, and Lebanon. However, there was no sign of Iran's stock market being affected by fluctuations in the stock market of Jordan, Bahrain, Egypt, Tunisia, and Morocco. Finally, the results of the conditional variance models indicate that the selected countries follow a similar asymmetric fluctuation pattern.

The aim of the current research is to optimize the multivariate portfolio optimization algorithms under illiquid market (commodity and financial) perspective. In this regard, an optimization model for portfolio risk-return assessment with LVaR constraints is investigated using reasonable financial and operational scenarios. This approach is achieved by minimizing LVaR. The research method is descriptive and correlational. The statistical population is the companies admitted to the Tehran stock exchange, which were selected by systematic elimination sampling (screening) of 100 companies that were present in the stock exchange during the financial years of 1392-1399.The required information was extracted through the new Rah Avard software and the official website related to the Tehran Stock Exchange Organization. The unit root test of the variables was investigated using the method of Lin and Chui, and the basics of econometrics were discussed, and the variables were investigated using the vector auto-regression method (VAR) using Eviews and MATLAB statistical software. Based on the results, it can be said; Liquidity affects commodity and financial markets. Also, the effect of optimization algorithms and modeling techniques on portfolio management and risk assessment was confirmed

Stock prices are influenced by many factors, making forecasting challenging. This prediction is often ineffective if it only considers numerical data or textual information. This research aims to provide a method of forecasting the future price of stocks based on the structure of a deep neural network using price data, a set of technical indicators, and news headlines as input to the model. For this purpose, Dow Jones stock data and Reddit channel news data have been used. Technical features are extracted from the stock data, and the news data are converted into a feature vector by the Bag of Words method and fed into the Long-Short term memory network for prediction. Accuracy is used as a performance evaluation measure and experiments on two data sets. The only numerical and only text has been used to evaluate the simultaneous use of two information sources. Also, three networks, SVM, MLP, and RNN, have been used to evaluate the model. The results show that the LSTM model achieved the highest prediction accuracy of 69.19% using news and financial data. News data is 65.62% accurate, and numerical data is 51.89%. Also, the LSTM model performs better than SVM, MLP, and RNN neural networks.

It is very important to choose an efficient monitoring system to assess the financial distress of banks, therefore, one of the most important monitoring systems used to assess the financial distress of banks is the CAMELS monitoring system. Which includes six indicators; Capital adequacy, asset quality, management quality, revenue quality, liquidity, market risk sensitivity. Therefore, in this study, the criterion of financial helplessness of banks is CAMELS indicators. Initially, 17 banks listed on the Tehran Stock Exchange in the fiscal year 1399 were ranked and divided into healthy and helpless financial groups by CAMELS indicators. Then, models, Data Envelopment Analysis and logistic Regression were used to predict the financial distress of banks. Then, with the pairwise comparison test (T), the prediction accuracy of both models was investigated. In logistic regression method, binary model with ForwardlR method was used. And in data envelopment analysis method, SBM model with different application was used. The results showed that the overall accuracy of the logistic regression model is higher than the data envelopment analysis model in assessing financial distress and also the CAMELS monitoring system can be a good assessor for banks' financial distress.

Presenting a Model of Insurance_Linked Securitisation using Fuzzy Delphi method and Dematel Technique in Iranian Environmental ConditionsAbstractInsurance_Linked Securities are one of the financial risk management tools. Insurance_Linked Securitiesh has helped to expand the capacity of the reinsurance market, and because Insurance_Linked Securities performance is considered to be uncorrelater with the economic cycle, they provid helpful diversification for investment portfolios. With the publish of insurance securities, it is possible to move in the direction of diverse development of the financial services industry. Therefore, the present study was conducted to review and develop a model of insurance securitisation in Iran.The present study is a type of exploratory researches. Therefore, experts and commentators in insurance securities were interviewed. Second, based on the theme analysis, the content of the interviews was analysed, and a proposed model was developed. The extracted indicators were validated using the Fuzzy Delphi method. In the next step The model of communication between factors in insurance securities was designed.using Fuzzy Dematel method.Keywords: Insurance Linked Securitisation model, Iran’s environmental conditions, theme analysis, reinsurance. Catastrophic Insurance Bonds.

The exchange rate affects the economic activity, foreign trade and wealth of economic enterprises. The aim of this study is to explain a model to investigate the effect of exchange rate shocks on the profitability of export-oriented production groups. In this regard, the data on exchange rate, tax, oil revenue, export and profitability were collected for 19 export-oriented production groups from 2008 to 2019. To achieve the research goal, the Panel Vector Autoregressive Model (PANEL VAR) using the generalized method of moments (GMM) was used to analyze the data. The results of the the Impulse Response Function show that the effect of exchange rate volatility, tax and oil revenue on the profitability variable is initially negative, but has a positive effect over time and is neutralized after several periods. But, the export impulse has positive effect on profitability. According to the results of variance decomposition, it can be said that the exchange rate fluctuations have more explanatory power for profitability fluctuations.

What has been said so far in the financial calculations and in the field of stock portfolio selection is that it prioritizes the existing investments in terms of degree of risk and return, so that investors can, considering the financial possibilities and Their risk level to form their preferred stock portfolio. Therefore, in this research, to present a multi-objective mathematical model for measuring stock portfolio risk by combining return metrics with two risk metrics, namely half variance and conditional risk exposure value along with transaction cost limit for fifteen shares of the top fifty stocks. The period of twelve months ending in 1398 has been discussed in the context of the Iranian capital market. According to the tables and graphs obtained from solving this type of model with the help of dynamic planning in different investment times, we will see better results in the efficiency of investors' decisions by spending less time and money and consequently more profitability of the portfolio.

This study aimed to add cost-sensitive learning technique to imbalanced data-based bankruptcy prediction models in order to reduce type I error and increase the geometric mean criterion of overall accuracy to reduce the misclassification costs of bankrupt companies for stakeholders. For this purpose, type I error, type II error, and the geometric mean of overall accuracy of bankruptcy models based on cost-sensitive learning were compared with bankruptcy prediction models with highly imbalanced datasets. The statistical sample included 1200 year-companies since 2001- 2020, consisting of 90% healthy companies and 10% bankrupt companies. Hypotheses test results showed that adding a cost-sensitive learning technique to the bankruptcy prediction models led to a significant decrease in the type I error, a significant increase in the type II error, and a significant increase in geometric mean of accuracy of imbalanced data-based models at 95% confidence level. Also, with the increase in the misclassification cost of bankrupt companies, type I error had a downward trend and the II type error had an upward trend, and the geometric mean of accuracy had an upward trend.

This research aims to model and present an appropriate pattern in order to forecast systemic liquidity risk of corporate stocks in capital market of Iran. For this purpose, 486 listed companies in Tehran stock exchange and OTC from 2011 to 2020 were sampled and then the companies were divided into four groups (portfolios) according to combination of indicators and types of activites of companies. Then by using types of multivariate GARCH models and comparing them, finanlly the VAR(1)-DBEKK(1,2) was selected as an optimum pattern . The results of research showed significant relationships among of liquidity shocks and volatilities with all of subsections, and consequently the main hypothesis based on “presence of systemic liquidity risk of corporate stocks in capital market of Iran” was accepted. In a way that the portfolios of company stocks with a “low level of liquidity- industry section” and “low level of liquidity- financial section” respectively had maximum and minimum liquidity shocks transmission of effects on future returns of the other portfolios, as well as the portfolio with a “high level of liquidity- financial section” had maximum volatility persistence and liquidity risk transmission to other portfolios.

Today, the use of new methods of project management and scheduling is an inevitable matter in oil projects. On the other hand, in classic scheduling case, the focus is on balance between time and cost of carrying out projects, which in such a situation, one of possible solutions to shorten time of implementing project is to accelerate activities. In addition to imposing more costs, this acceleration can also affect the quality of performance and environmental effects. Therefore, in this study, the environmental effects and the quality of the activities were also considered as new indicators the cost-time balancing issue of the project. A new mathematical model with four indicators; cost, time, performance quality and environmental effects are provided. Unlike the traditional models in which only one execution mode was considered to perform the activities and one type of prerequisite relationship between the activities, the execution modes of the activities are multi-mode and the dependency relationships between the activities are generalized from the type of prerequisite relationships and including these types of relationships, brings the issue closer to the real world. Because the problem is NP-hard in large dimensions, meta-heuristic algorithms were used to solve the model.

The stock market index is one of the effective features in investment because it can well reflect the health status and macro change trend of a country’s economic development. Various features affect the stock index. The various combinations of these features create a wide state space. Hence, it is impractical to provide a data set containing all these combinations to train the stock index prediction model. in this research, an attempt has been made, after collecting a significant number of effective features on the index, to provide a method for selecting appropriate features for the stock index prediction model with aim of increasing prediction accuracy. For this purpose, the mRMR algorithm is used as the basic algorithm. Also, to select the appropriate model, a number of the most applicable artificial intelligence models for predicting the stock index were compared and according to the results, the LSTM network was selected to predict the stock index. The results of this study show that using the LSTM network and the proposed method in selecting features, with 8 selected features, high accuracy can be achieved in the daily prediction of the Tehran Stock Exchange Index. So that MPE is calculated to be about 2.66,

The purpose of portfolio optimization is to select an optimal combination of financial assets, which should be a guide for investors to achieve the highest returns against the lowest possible risk. On the other hand, one of the key factors in portfolio optimization decisions is related to predict the stock prices. To do this, classical nonlinear mathematical and intelligent models such as regression are commonly used. In the present study, a nonlinear model of support vector regression with multiple outputs is applied to reduce the prediction errors. To show the effectiveness of the proposed model, the data of S & P500 index companies in the period 12/09/2016 to 02/08/2021 is used. The results show that the selection of a portfolio based on prediction using multiple vector backup regression due to considering the relationships between outputs simultaneously in terms of Sharp criteria has a better performance than the selection of portfolio based on prediction using regression method.

The stock price crash risk is one of the main concerns of financial market participants. According to financial theories, Retail investor attention is one of the factors affecting the stock price crash risk. This study investigates the effect of retail- investors' attention based on the GSV index on the stock price crash risk in the Tehran Stock Exchange. Also, considering the moderating role of institutional ownership and the quality of companies 'audits in the relationship between investors' attention and the probability of stock price crashes, the role of these two variables has been investigated in the study. The research period was 2009-2020 and the sample included 164 companies. The study's results using the panel data regression approach indicate that investors' attention in the current period increases the risk of a stock price crash in the subsequent period. Also, there is a negative relationship between institutional shareholders and the stock price crash risk in the next period and there is no relationship between the quality of companies' audits and the stock price crash risk of companies in the next period. Also, institutional ownership and audit quality do not affect the relationship between retail-investor attention and stock crash risk.