نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 44 (پاییز 1399)

The Black-Scholes pricing theory is important ways of valuating transaction options. In this paper, a new method was developed to prove and improve the Black-Scholes equation by focusing on the Black-Scholes main Schrödinger equation and solving this equation using the NikkeuroOvaryov method. In the following, while investigating the possibility of improving the Black-Scholes equation with this method, a new equation for the pricing options was presented and tested. Increasing the accuracy of pricing arbitrary deals by using the equation provided, especially for high-value trades, logical solution in a new way, comparing output with numerical solution and innovating. Option based on Lagrange polynomial functions, the goals of doing research are present. The results showed a different positive probability for the Black-Scholes equation by solving the differential equation by the method Nikkirovo-Ovaryov is feasible and at 95% confidence level, there is no significant difference between the price of the two main black-hole groups and the new model. In order to compare the output of the new model with the Black Sholes main model, information from the 50 Saffron Deal options in Iran's Overseas Branch was limited to the 1395 to 1398 period and the Mann-Whitney independent nonparametric group was used to compare.

Abstract According to statistics, risk estimation makes unusual predictions without focusing on the relevant factors and only focusing on a set of equations. In this study, we used a spreadsheet data set of time series and a new method for risk estimation. This estimation was based on a generalized dynamic factor model (GDFM) and daily data series obtained from different measures of Tehran Stock Exchange over a 10-year period during 2008 to 2018. we first utilized a generalized dynamic factor model proposed by Forni et al in order to determine statistic and dynamic factors. In the second step, by using MATLAB, we estimated the joint component of the study series as Tehran Stock Exchange risk. Next, using the generalized least squares (GLS) method, we examined the impact of each of the filtered risks on the index returns. The results showed that although both risks estimated through one-side and two-side filtering substantially and significantly explain the changes in the performance of the studied indices, but the risk estimated through two-side filtering using GDFM can explain the returns changes much better and more accurate than the one-side filter using the same model.

Banks as fund intermediaries in providing and allocating resources to the community, encounter market risk, liquidity risk and etc. In this study, the market risk, is taken into consideration in order to determine the optimal currency basket, one of the fundamental aspects of Foreign Currency Reserve Management in banks, which itself is also affected by fluctuating interest rates, exchange rates, stock prices and etc. The approach used in this paper is the value-at-risk criterion (VaR) the variance-covariance method, along with the exponentially weighted moving average (EWMA) technic. Value at risk actually summarizes the types of risks in a single digit, and it releases the senior management from bunches of risk calculations. The purpose is to design a model which provide an optimal combination for holding 6 currency reserves such as U.S. dollar, Dirham, Yen, Lira, Won, and Euro in Bank Mellat using the reference rates data of the aforementioned currencies in 2018. At the end, the model was solved using LINGO and Excel software. The results show that the maximum share of the US dollar and the dirhams in the currency basket of Bank Mellat are 33% and 67%, respectively. Accordingly, if the share of that currencies mentioned above exceed the obtained digits in the currency basket, then the maximum expected losses on the currency portfolio increase over the time and at the level of desired level of confidence. Also, other currencies are so risky, therefore Mellat Bank, to hold these currencies must plan more based on its trading needs.

recently fuzzy intelligent method was used to dynamically model Japanese candlesticks in order to accurately consider the patterns of a candlestick with uncertain information on such patterns. Since fuzzy logic is expert knowledge, although human specialists can play an important role in regulating the values of membership functions of fuzzy variables but since human knowledge is usually ambiguous, a optimal adjustment is not achieved. Therefore, providing a technique that optimally adjusts the values of membership functions in dynamic candlesticks patterns will play a crucial role in the efficiency of the fuzzy trading system. One of the most commonly used optimization methods is the meta-heuristic methods, most of the meta-heuristics have a structure similar to the particle swarm optimization method. meta-heuristic methods such as fireflies and ant colony are more powerful than particle swarm optimization due to their efficient exploitation capabilities. In this paper, fireflies and ant colony are used to adjust and optimize the membership functions of fuzzy membership function of fuzzy candlesticks variables in order to trading analysis and stock price forecasting in the stock trading system. The results of applying the proposed method to iranian stock companies indicate the high accuracy of the proposed method.

Previous research into the short-term performance of the initial public offering reflects the fact that short-term stocks perform better than the market in the short run. Statistical models have been able to make good predictions about the performance of new stocks, but the limiting assumptions of some of these models have been effective! So, other ways to deal with these limitations and improve forecasting performance were introduced. Since initial public offering is an important issue in the capital market, in this study, we investigate different classification models to achieve a model that has high efficiency and accuracy in predicting underpricing of initial public offering (IPO) stocks. To achieve the research goal, systematic elimination sampling method is considered to select 84 companies among all listed companies at Tehran Stock Exchange (TSE) and 54 companies among all listed companies at Over the Counter (OTC) from 2003 to 2017. The results showed that support vector machine (SVM), Bayesian Network and C5 decision tree models are highly accurate in predicting underpricing. The results also showed that the influential variables included assets growth, auditor tenure, auditor specialty in the industry, financing ratio, P/E, CFO ratio, ROA, stock price fluctuate, growth opportunity and audit firm size.

Momentum& Contrarian trading strategies used to exploit the serial correlations of market yields and securities fall under financial exceptions and capital market irregularities. In this strategy, incremental returns can be achieved by buying past winning stocks and selling past losing stocks. Accordingly, the purpose of this study was to explain the moderating role of the investment horizon on the additional returns resulting from the use of accelerated-reverse strategies in Tehran Stock Exchange price Explain the moderating role of the investment horizon on excess returns from Implementation of the Momentum& Contrarian strategy changing in stock price volatilities. The hypothesis testing in the present study was performed using multivariate linear regression model and econometric modeling. In examining the moderating effect of investment horizons on the above relationship, it was also found that despite the unexpected effect of the surplus resulting from the change in strategy on the stock price fluctuations, the investment horizon parameter on the additional return relationship derived from the use of strategies Momentum& Contrarian of stock price fluctuations have no significant effect.

Interpretive structural modeling is one of the methods of system design, especially management and accounting systems. This technique starts by identifying the variables and then establishes the underlying relationships between the variables using the expertise and knowledge of the experts and finally the multilevel structural model. In this study, using this approach, behavioral biases are structured to influence the decision making of real active investors in the stock market. For this purpose, expert experts in this field were used and 12 behavioral influences affecting investor behavior / decision making were identified and then coded using the matrix of initial access, their impact on investor behavior / decision model. Finally, they were leveled using the final matrix. The results of interpretive structural modeling showed that behavioral bias affecting investors' decision making was modeled at six levels, and imaginative power bias was at the highest level and more effective than other bias and late bias bias.

The purpose of present article is to design a dynamic model (DM) of financing SMEs according to Decision Making Trial And Evaluation Laboratory (DEMATEL) based on Analytical Network Process (ANP) (DANP) approach. The innovation of this article is to take hybrid of DANP and DM in financing SMEs approach. This article has paid using different scenarios in financing SMEs and model simulation. The dynamic model is formulated therefore, based on previous literature and the fuzzy screening method, extracting effective factors on financing SMEs and causal link identified. Then it was included in the model using the DANP the relationships between them and the amount of impact coefficients are determined. Finally identify new financing methods over a 10 month period in order to test the accuracy of the model and to determine the behavior of the state and rate variables, we collected data from 24 SMEs. The behavior of research variables analyzing by evaluated in the framework of the model as well as sensitivity analysis the validity of the DM is designed. The results showed that SMEs use the designed model and the scenarios can be used to finance their business optimally.

Noise traders have an undeniable role in determining market volatility, returns and stock price movements. Therefore, In this paper, the effect of noise traders on the stock returns of companies with the aim of presenting an appropriate picture of how they are affected in extreme situations.The statistical population of this study includes all companies listed in Tehran Stock Exchange during the period of 2009-2017. The sample of 13717 data from 150 companies listed on the stock exchange monthly. The main hypothesis of this study is to evaluate the Extreme Effects of noise trading on stock returns by quantile regression was used to analyze the data. The findings of the research show that the level of noise activity increases with the level of efficiency Moreover,the positive effect of the noise trading index on returns with a coefficient of 0.0001.Under extreme returns, this effect is greater than the intermediate values and reflects the intensification of noise trading activity in periods of decline and market growth.

Much evidence suggests that time series such as stock market prices are complex and random, which makes their changes unpredictable. However, these time series are likely to be a nonlinear dynamic or, in other words, a chaotic process and can therefore be predictable. Therefore, in this study, stock prices and stock returns of Tehran Stock Exchange companies during the period 2014-2018 and monthly intervals were tested to determine whether these variables have fractal properties in their behavior. To achieve the above objective, our model estimation is used to explain the mass fraction of moving average. The findings of the above tests indicate that stock prices and stock returns experience a turbulent and definite process. This implies that the capital market is inefficient, and because of its long-term memory, it can be useful in predicting long-term performance and may have a guide to better understanding market failure factors such as the lack of transparency of information flow and action to address it.

This study aims to estimate the bid-ask spread criterion based on the daily highest and lowest prices and to imply this criterion as a proxy for transaction costs. Then, using this type of transaction costs and liquidity, the consumption-based capital asset pricing model is modified. To perform experimental tests. Daily data is collected from 47 companies accepted on the Tehran Stock Exchange and for the period 2009 to 2018. This study is carried out on 20 portfolios formed based on liquidity criteria Liu (2006), DVOL, Size, and Gibbs. The results of this study show that the capital asset pricing model based on traditional consumption has a poor performance in explaining the return on cross-sectional stocks and liquidity-adjusted CCAPM can explain the bigger portion of cross-sectional return changes compared to the traditional CCAPM model. Also, the results show that the entry of trading cost variables and liquidity risk leads to improved CCAPM.

Credit risk issues and methods for identifying and predicting it have been constantly evolving over the past few decades. When a company deals with a financial problem, it may not be able to fulfill its financial obligations, which can cause direct and indirect financial losses to shareholders, creditors, investors and other people in the community. Advanced credit risk models that are based on market value include improving credit quality as well as reducing or decreasing credit ratings. In the present study, we have Investigated two models of advanced credit risk models, so two samples were selected, namely companies with financial problems and companies with financial health, in each group probabilities of default are estimated by two models which are KMV and ZPP, and then we compared probabilities of default. We have concluded that the ZPP model has more predictive ability than the KMV model. This method is denoted the Zero-Price Probability or simply the ZPP model. The main focus is on the new simulation based approach rather than the older established models.

The purpose of the present study is to evaluate the value at risk of stock indexes based on parametric, quasi-parametric and non-parametric approaches in Tehran Stock Exchange on the basis of data collected during the period of 2009-2010. The purpose of this study is practical. On the other hand, the present study is empirically oriented epistemologically, its inductive reasoning system, and field-library study using causal-historical information (ie, past information). In this regard, the performance of each of the above approaches was evaluated and finally the accuracy of accuracy was evaluated by the Basel Committee test and Bin, POF and TUFF frequency tests. The results show that parametric, quasi-parametric and semi-parametric models have priority in terms of efficiency and accuracy, respectively. In addition, the results from another perspective show that non-parametric and semi-parametric models based on error ratio and post hoc tests have overestimated the value of risk exposure, although the contribution of nonparametric model is higher

The purpose of the present study is to present a model to evaluate the efficiency of Iran Khodro Company production costs with a lean supply chain focus and using an interpretive structural approach. The method was qualitative-quantitative. In the qualitative section, 17 variables were extracted by interviewing the experts, and in the quantitative section, the interpretive structural modeling (ISM) method was used for modeling and then the Mic-Mac analysis was performed. Findings were to obtain a five-level model that was the most influential and the only variable in the fifth level of technology change and the most influential variables in the first level were profitability, gross income and basic cost efficiency index. According to the Mac-Mac analysis, technology change measures have low dependency and high conductivity, cost reduction measures, and gross revenue generating high impact and little impact on the system; the rest are interface type. According to the model of this research, technological changes can have the greatest impact on the cost efficiency of production. Other ways to improve the efficiency of production costs include: increasing production quality, optimizing production input costs, effectively managing production input price changes, increasing production capacity and ...

Stock market as a part of the capital market plays a very important role in directing savings to the manufacturing sector in all countries. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the ascension of stock indices, and in fact the relationship between the economy and the stock has been discontinued. Today, in the economy of many developing countries, the situation of macroeconomic variables is not consistent with the increase in stock indices, and in fact the relationship between the economy and the stock has been discontinued. In the present study, for the prediction of price bubbles, the daily data of 144 companies in the Tehran Stock Center during the period of 1389 (1396) has been analyzed by the generalized autoregressive conditional heteroscedasticity (GARCH). Based on the results of the data analysis, member firms in the stock center in the years under consideration have been priced bubbles that were higher in the first six months of the year. The factors that triggered price bubbles include political shocks, returns in parallel bubbles, such as oil, currency and gold.

In This Study We Compare the Efficiency of Both Artificial Neural Network Prediction Methods (ANN) and Traditional Method of Auto Regressive Integrated Moving Average (ARIMA) in Predicting Stock Prices in Iranian Stock Market. For This Purpose, Four Pharmaceutical Companies, Alborz Drug, Iran Drug, Pars Drug, and Jam Drug Were Selected and ARIMA Model and Artificial Neural Network Model Were Estimated For All Four Companies. In Order to Estimate Artificial Neural Network Model, Stock Price Variable as Dependent Variable and Stock Trading Volume, Drug Industry Index, OPEC Oil Price, Exchange Rate and Gold Price are Considered as Independent Variables. MSE, RMSE, MAD, R2 and MAPE Criteria Were Used to Compare Two Models. In Order to Estimate the Stock Price Forecast Regression Model, Use of Auto Regressive Integrated Moving Average (ARIMA) Regression Is Used and Estimation of the Coefficients of the Model is Performed Using the EVIEWS Statistical Software. An Suitable ANN Model Was Created For Predicting Stock Prices Using MATLAB Software. The Results of the Research Showed That the Research Hypothesis is Correct and the Artificial Neural Network Model (ANN) Has a Better Predictor of Stock Price in the Iranian Stock Market Than the ARIMA Method.

The present study compares and predicts the predictive ability of the capital market based on the learning pattern of the Levenberg-Marquardt algorithm, the Gradient descent and the ARIMA Algorithm. For this purpose, market data were used in the period from 1394 to 1397, and more than 75% of these data were used as training data prior to 1397, and one year end data were used as data. The results of the evaluation of the research data show that artificial neural networks have a high capacity for price prediction. The results also showed that in both training data series from 1394 to 1396 and experimental of 1397 the comparison of the results and performance of ARIMA neural networks (ARIMA) showed that the neural network had higher predictive power in Comparing with the performance and prediction accuracy of two types of neural networks with the Levenberg-Marquardt learning algorithm and the Gradient descent learning algorithm using the Levenberg-Marquardt learning algorithm has been able to increase the neural network prediction accuracy And reduce its error, so, the results of the present study show, the Levenberg-Marquardt learning algorithm improves the predictive power of the neural network.

In the past few decades, the identification of state variables and parameters of a model from measured data has increased dramatically. This widespread growth has created a growing need for integrated models. Achieving sustained and long-term economic growth requires optimal resource allocation, and this is not possible without the use of financial markets, especially efficient capital markets, so portfolio optimization and wealth allocation between different assets are among the most important issues in investing. In this research, in order to implement smart financial portfolio, it is tried to improve the existing optimization methods based on Sharp Ratio performance and to present an intelligent method for trading based on different algorithms. For this purpose, first, create a quantitative investment model using momentum algorithm and long-term investment model over a 6-year time horizon using monthly stock exchange data and then a set of smart models (general functions, general average and The general algorithm (developed by Kalman filter), which calculates the amount of capital using smart patterns to maximize return and avert negative return on equity investments and optimize capital investing to make the proposed structure perform better than other algorithms. Conventional and can fit and alternative approaches to achieve better results finally, the results indicate that the proposed model is effective and efficient.

The paper aims to examine the price discovery process and the performance of Gold Exchange Traded Funds (ETF) in Iran Mercantile Exchange (IME) for the period 2017/06/10 – 2019/09/18. The study has employed Johansen cointegration and Johansen’s Vector Error Correction Model (VECM) for the price discovery analysis. The results show that the spot prices lead the Gold ETF price during the study period and ETF is only following the spot prices. Also Tracking Error analysis shows that, Gold ETF have neither outperformed nor underperformed the spot price. Price Deviation analysis indicates that Gold ETF is trading lower than the spot price of gold. The entire analysis reveals that although the price discovery takes place in the spot market, Gold ETF have performed as well as physical gold and the slight difference in price with that of Gold is only because of certain fees, which are applicable in the management of Gold ETF. Therefore, the expected price discovery for gold ETF in IME has not been realized.

Financial development is one of the most important causes of economic growth. Economic growth is a key variable of every economy, so analysis the factors that affect Economic growth is important, too. In this paper, the effect of financial development on the economic growth of the country during the period of 1989 to 2016 has been studied. In order to increase accuracy and flexibility of results, we use the TVP-FAVAR model which make possible to change coefficient and participant of individual variables at any point of time. At first, latent financial development variable in Iran economy has been estimated; Then, we specify the model of study by using the variables of liquidity volume, oil revenues, economic growth and financial development. The results of the impulse- response functions show that a shock in the latent financial development has had a positive effect on economic growth during the years under study. The results also show that the shock caused by oil revenues will only lead to an increase in economic growth over the short term and will be adjusted over several years, while the effects of liquidity shocks on the results of most years have had a neutral effect on economic growth.

The most important courses are the ones that are taught and the one that is taught and the ones that are taught are the ones that work for each other, in order to make the most profit.In our research, it can be seen that all sorts of solutions are one of the solutions, but the concept of skewness should be considered in the future as well. In the first twenty-first of the first fifty years of 2019, the stock market is given as an example..Evolution is also a model in which the future potential of stocks is predicted by the multilayer perceptron neural network with several scenarios, including the prediction of the stock price time series method itself or the prediction of the impact of factors influencing stock price changes. The results show that the models presented in this article, compared to traditional methods, provide investors with and achieve the optimal formation of the portfolio by selecting the appropriate shares of companies.