نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 52 (پاییز 1401)

In this article, using the market timing approach and homogeneous and inhomogeneous collective learning methods, the purchase, maintenance and sales signal and market forecast are presented based on the basic characteristics, technical characteristics and time series of returns of each company in the 100 days leading to the current day. . Based on this, 208 companies were selected as active companies between 1390 and 1399 To teach data by two-level ensemble learning machine (HHEL) and market trend forecasting based on market timing strategy, use data from 5 years 1390 to 1394 and to test the data as stock portfolio optimization based on stock portfolio maximization and risk minimization. The investment portfolio uses MOPSO and NSGA II algorithms and is compared with the obtained investment portfolio with the buy and hold strategy. The results showed that the MOPSO algorithm achieved the highest stock portfolio yield with 96.437% compared to the NSGA II algorithm with a yield of 91.157% and the same investment method with a yield of 13.058%. Also, the portfolio risk in NSGA II algorithm was much lower than the portfolio risk in MOPSO algorithm with 0.792% and 1.367%, respectively

The purpose of this article is to predict probability of default of 60 corporations with structural Merton model. we used market data of 60 corporations from 2018/08/01 to 2019/09/01 witch are listed in Tehran Stock Exchange For predicting probability of default . to reach this goal we estimated assets value of corporations, volatility of assets and drift rate. We used Capital asset pricing method to estimate expected assets return. Then We used simple regression method and multivariate GARCH (MGARCH) to estimate Beta of corporations. in the end we compute d likelihood function of the average predicted default for each industry and compared the results with actual default rate of that industry in the next year after predicted date. Regarding obtained results of likelihood function probability of default prediction with multivariate GARCH (MGARCH) approach outperform the simple regression model, therefore we recommend using the MGARCH approach for its better prediction performance.

The present article deals with the research gap in "stock price Resiliency Measurement" in Tehran Stock Exchange. This research has a development-applied approach that has provided a purposeful method in modeling stock price resiliency. After presenting the resiliency measurement models, the research hypotheses, based on theoretical foundations and previous studies, were formulated and tested in companies listed on the Tehran Stock Exchange from 2009 to 2020. Data analysis recommended the use of panel data with fixed effects and the outputs of Eviews software version 10 and Stata version 12 showed that stock resilience level was different in different years and between all variables considered in the sub-hypotheses with resiliency at the confidence level. There was a 95% significant relationship among them, except "liquidity, price effect". Among all moderating variables, stock beta, and Book to Market ratio had the most significant indirect effect and the random process component had the most direct effect in the model on the stock resilience.

The aim of this study was to explain the new volatility model in option pricing using the VIX volatility index, known as the GARV model, with consideration of multiple volatility components, including conditional variance based on returns (hidden components) and realized variance dynamics (jump component). ), Pays at the time of neutral risk. Also, after comparing this model with Garch and Arch models, the measurement error of the models was investigated and finally, the pricing of the option option in all three models was estimated and discussed. The study population is the daily data on gold coin trading in the period 2017 to 2021 in the Iran Commodity Exchange. The volatility of coin prices in the Iranian Commodity Exchange was verified by the GARV model with the generalized VIX index, GARCH and ARCH according to the Ives software. The results of the analysis of the hypotheses show that the calculation of oscillation based on GARV model with hidden components and jump component in predicting VIX oscillation has less measurement error compared to GARCH and ARCH model.

Choosing the optimal stock portfolio is one of the main goals of capital management. There are several techniques and tools to solve problem the optimal portfolio. In this research, using data of 15 stocks which randomly selected from the Tehran Stock Exchange including; PKOD, ZMYD, BPAS, FOLD, MKBT, GOLG, MSMI, PTAP, SSEP, AZAB, FKAS, NBEH, PFAN, GMRO and GSBE, the First return of these stocks are calculated daily in the period of 31/3/1394 -31/3/1399 for 5 years for 1183 days. Then and their portfolio risk is calculated using the models of absolute deviation risk and conditional value at risk, and these two criteria are compared by the classical solution method. The portfolio optimization output with each of these risks represents a different weight per share. In the following, the deviation - absolute risk model and conditional value at risk model of metaheuristic method using MATLAB (R2019) software are compared. The results show that the PSO model of metaheuristic method compared to the classical method in solving portfolio optimization problem showed more return in PSO-MAD criteria and therefore it is a better method to solve such portfolio optimization problems.

Financial analysts and other users need relevant and reliable information to predict corporate bankruptcy, which should be distributed symmetrically to all users. Accordingly, the purpose of this study is to compare the prediction accuracy of Artificial Neural Network (ANN) systems based on the Multilayer Perceptron Approach and Falmer Binary-Logistics Model in order to predict bankruptcy. To test the hypotheses, the combined data of 172 companies listed on the Tehran Stock Exchange in the period 2007-2016 were used. The results of the analysis of the research data show that the ANN system can identify of the factors affecting on bankruptcy of Iranian companies in the year before bankruptcy by Precision equal 98%. Findings from the binary-logistic model showed that the forecasting model designed based on the Falmer regression method is able to predict with 82% accuracy the bankruptcy of the sample companies. Therefore, the use of artificial neural networks can more powerfully and accurately predict bankruptcy than regression models.

Portfolio optimization is a process in which the investor seeks to maximize return on investment or minimize risk. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, which means minimizing investment risk and maximizing investment profit. The aim of this study was to investigate the capability of adaptive fuzzy neural inference system (ANFIS) and grid matrix (GA) strategies in selecting and optimizing the investment portfolio from among selected Tehran Stock Exchange and OTC companies. The grouping of stocks by the network matrix and the classification of companies based on their market value and the use of the law of quarters and finally their weighting is considered in proportion to the forecast return for the next month of that share. Also, a stock portfolio optimization model has been designed and presented using an adaptive fuzzy neural inference system and its combination with a genetic algorithm in which three different categories of time, technical and fundamental series variables are used as model inputs. It becomes. Research outputs show that these systems have the ability to optimize the stock portfolio.

The commitment and acceptance financial therapy helps individual to by teaching them financial skills along with changes in monetary beliefs and financial behaviors. to have an overview of his/her own set of decisions and reduce the effect of mental errors on financial decisions.This is a practical and quasi-experimental study. The statistical population of the study was 20 bazaars in Shahrekord in 2020 who underwent training and interventions related to financial therapy based on acceptance and commitment in 10 sessions of 120minutes and completed the research questionnaires before and after training. In order to analyze the data in the traditional way, SPSS25 statistical software and Matlab software were used for fuzzy data analysis. The results show that financial therapy based on the approach of acceptance and commitment to increase financial literacy, improve money management And personal financial management are effective in both quantitatively and qualitatively.Another result of this study is more accurate measurement of fuzzy model for evaluating the effectiveness of financial therapy based on acceptance and commitment than the traditional evaluation method.

The present study has investigated the contagious risk of turbulence.In this study, the contagious effect of real and virtual currency (Bitcoin) fluctuations has been measured. In this regard, the method of self-regression vector analysis (VAR) and the conditional autoregressive model on the heterogeneity of multivariate generalized variances (MGARCH) have been used.The data used in this study, including the exchange rate of the dollar based on the euro and the price of bitcoin in the period 01/2015 and 2020/01, were collected and examined by the generalized multivariate conditional variance heterogeneity (BEKK) method. The present study is based on the classification of research based on method, nature and direction, respectively descriptive survey, applied and post-event. The results of this study confirm the relationship between the volatility of real currency and virtual currency. In other words, the main hypothesis of the research on the contagion of virtual and real exchange rate fluctuations has been confirmed unilaterally from virtual exchange rate to real exchange rate.

since so far few studies have been conducted in the field of relationship favorable and unfavorable risk management with macroeconomic variables, in the present study, the short and long term effects of inflation and economic growth on the upside and downside risk management of cement and pharmaceutical companies in the Iranian Stock Exchange in the form of ARDL panel model during the period 1391-99 were studied. The statistical population of the present study includes cement and pharmaceutical companies Iranian Stock Exchange, which selected 62 companies as a statistical sample using the systematic elimination method. The results showed that the inflation has a positive effect on the the upside and downside risks of cement and pharmaceutical companies, but economic growth has a negative effect on this variable. The results of the error correction model (ECM) indicate that 50.6% of the imbalance of upside and downside risks variable of its long-term values disappears after a period. Based on this, it can be said that if a shock causes favorable and unfavorable risks in market, it takes about two periods for the market to return to its original long-term equilibrium.

Modeling synchronous time series in financial systems is very complex. In order to analyze such series, we require procedures that can determine long-term relations with high accuracy. Multifractal detrended cross correlation analysis (MFDCCA) is a technique to analyze long-term relations through detrending the time series. In this work we propose a novel technique for a more accurate detrending of a financial time series, called indicator-based multifractal detrended cross-correlation analysis (IMFDCCA).We aim at using financial market technical analysis indicators to better determine correlations between financial time series.We investigated our method on currency pairs EUR/USD and USD/JPY and their long-term and short-term relations of these series were determined as multifractal.In order to evaluate the effectiveness of IMFDCCA, we used R.S and GHE techniques for the Hurst exponent estimation. The evaluation results on a collection of 8 years data (2011-2019) show that the proposed method compared to the baseline (MFDCCA) reduces the RMSE by 30% and 26% using R.S and GHE respectively.

Institutional ownership is one of the corporate governance mechanisms, not only because of its expertise and knowledge, they have a better supervisory role over managers, which can also be effective in aligning important company decisions with their own interests. The main question of the research is how institutional shareholders in terms of ownership thresholds influence managers' decisions regarding financial leverage and dividends paid. In the present study, 143 companies have been selected in the period 2013-2019. The research findings show that the impact of the institutional shareholder on the financial leverage is the result of two values of institutional ownership thresholds and as a result three different behavioral regimes for the financial leverage. In the first and second behavioral regimes, institutional ownership has a negative and significant effect on financial leverage, which intensifies with increasing ownership; However, in the amount of institutional ownership above the second threshold, In relation to the effect of institutional ownership on dividends paid, a threshold value has been identified that in both regimes, the effect of institutional ownership on dividends paid is positive and significant, but in high values of the threshold amount, the impact factor shows a significant increase.

In the past years, financial markets have faced uncertainties such as financial crises, oil shocks, changes in currency policies and similar cases. The statistical population in the current research are financial intermediaries such as insurance, banks, active investment companies (PGPIC) in the stock market on a daily basis in 1990-98. For the insurance market, by increasing one unit of its efficiency, the value at risk of the whole system decreases by 0.016. With the increase of one unit of its yield, the value at risk decreases by 0.051. With an increase of a unit or one percent of profit in the banking sector, the efficiency of the whole system increases by 0.0014. The results of estimating the value exposed to conditional differential risk indicate that the share of value exposed to conditional risk of capital is equal to 0.045, bank 0.026 and insurance 0.037, and the value exposed to conditional risk of capital and insurance sector is more and bank is less, and the share of value exposed to conditional risk of capital equals 0.045, bank 0.026 and insurance 0.037, which means the value exposed to conditional risk of capital and insurance sector is more and bank is less.