نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 57 (زمستان 1402)

In the current research,wavelet analysis is used to analyze the time series of prices in a pair of assets into general and detailed time series, and the property of collocation between different and corresponding levels of analysis of two series is checked in order to find collinear pairs at different levels of analysis. And then its profitability is examined. In this research, the profitability of the pair trading system based on wavelet analysis was investigated on 14 indices of the Tehran Stock Exchange betwee 2013-2022. The results show that for the second level of detail in the wavelet analysis, the results are quite impressive and the number of trading positions is more than doubled, the daily return is increased to four times and the Sharpe ratio is also increased to about two times. The system formed based on the first level of detail also has a better profitable performance than the normal aggregation, and the performance of the third level of detail is within the limits of aggregation. In addition, the average duration of the transaction also shows significant decrease in the first and second levels. Profitability performance at the level of general series is generally weaker than the aggregate.

In the hectic world of today, with the rapid changes in various aspects such as political, cultural, social, and technological, leading organizations and industries, are paying special attention to their strategic planning. In addition, with the emergence of new technology and the fast growth of the internet, Digital ecosystems became an inevitable part of the strategic plans, since the future is intertwined with digital business. Digital business creates significant changes in the business ecosystem of organizations, which makes the organization more complex and the need to make strategic decisions related to digital transformation. Digital ecosystems are the digital counterpart of economic ecosystems, which change the way businesses cooperate and compete with strong, and organized architecture and can provide dynamic solutions to complex problems.In this research, by using the COBIT framework and ANP, graph network, and Markov chain, a model has been proposed to measure and analyze the risk of branding management in a digital ecosystem. By evaluating the current status of the risk parameters in an FMCG investment corporate, the research proposed solutions and optimizations, based on a Genetic algorithm and MATLAB software to increase the effectiveness of branding management in the investment Corporate.

Today, artificial intelligence has made a big change in the recognition of chart patterns in technical analysis. Although, the emergence of new and complex analytical methods in technical analysis has provided a new challenge for artificial intelligence methods. One of the popular and complex technical analysis methods is Elliott Wave Theory. On the other hand, the speed of progress of artificial intelligence methods is such that a more powerful method is introduced every time. One of the new and powerful artificial intelligence methods is the deep learning method. Therefore, in this research, a model has been presented to predict the trend of the stock market through the detection of fractal patterns based on Elliott wave theory using deep learning method. In this research, 15 Elliott wave patterns were considered, and then 1002 samples of stock price charts of companies listed on Tehran Stock Exchange were collected and labeled for patterns, and finally entered as input into deep learning algorithm using recurrent neural network model for recognition. In this research, RapidMiner 9.9 software was used and accuracy criteria were used to determine the power of the model. Based on the results, the accuracy of developed model in recognizing patterns is 61%.

This research was conducted with the aim of investigating the financial marketing model of the Melli Bank of Iran with an emphasis on customer segmentation. This research is of a mixed type and is developmental-applicative in terms of purpose, and it was carried out in a descriptive-survey manner. the statistical population includes experts and employees of Melli Bank of Iran and university professors and elites in the field of marketing, and 385 people were selected using Cochran's formula. The tool included a questionnaire and structural equation modeling and SPSS and PLS software were used to analyze the data. Based on the results, the financial marketing model in the Melli Bank of Iran was designed with an emphasis on customer segmentation in 6 main dimensions of causal conditions, interveners, central category, strategies and consequences, and causal conditions have a significant effect on the financial marketing phenomenon with a significance of 51.673; Also, the influence of background conditions and intervening conditions on the phenomenon of financial marketing was confirmed with significance, 41.965 and 40.074, respectively; Also, other relationships were investigated and all hypotheses were confirmed.

Financial leverage is predicted as one of the main factors in dealing with the cause of firms' financial vulnerability. In this article, the effect of financial leverage on the financial vulnerability of firms has been studied by using the the regime-dependent approach through the estimation of a threshold model. The estimation results of a two-threshold model in a sample including data related to 145 non-financial and non-banking firms admitted to the Tehran Stock Exchange during 2010-2019 show that the effect of financial leverage on the vulnerability of firms has a vulnerability regime-dependent effects. Moreover, such an effect is even asymmetric because the negative effect of financial leverage on the vulnerability of firms in low vulnerability regimes is greater than their positive effect in high vulnerability regimes. Based on this, in using debt instruments in financing, it is necessary for firms to consider their initial situation in terms of financial vulnerability, because their financial health and performance in using financial leverage depends on the their vulnerability regime

In terms of market value, the basic metals group is important after the chemical products group in the stock exchange, so the optimal use of marketing capacities in this industry can lead to financial benefits. The purpose of this research was to design a framework for marketing capabilities to achieve financial benefits in companies affiliated to the steel industry accepted in the stock exchange. became. Qualitative phase data was collected through semi-structured in-depth interviews with 15 marketing experts who were selected through purposive sampling and snowball methods until theoretical saturation was reached. The data was done using open, central and selective coding and analyzed using Maxqda data analysis software. The results obtained from the analysis led to the final model of marketing capabilities with 7 main categories including: product, distribution, market, customer, analysis of competitors, advertising and brand, which in 6 dimensions are causal conditions, main category, background conditions, environment, strategies and As a result, the paradigmatic model of the research was drawn.

Finding solutions for price prediction, forming an optimal portfolio and achieving more profit are the basic goals of financial market activists. The purpose of this research is to predict the price of financial assets such as several stocks, gold, coin and a number of digital currencies using the LSTM neural network model and then form an optimal portfolio by calculating the rate of return, risk and the Sharpe ratio. The data used is from the archives of the Tehran Stock Exchange website, the website of the gold, coin and currency information network, as well as the website of buying and selling digital currencies. The time series of the prices of the investigated assets is between 2017 and 2020. Also, we used Python programming language and Gephi software to build the model and analyze the data. In the end, it was found that the LSTM neural network model is capable of predicting the price of financial assets with a very low error rate in each asset, and according to the Sharpe ratio obtained for each financial asset and the correlation matrix, Vebank stock, Khbahman 1 stock, and Digital currencies TRON, Tether and Bitcoin allocate more shares in the proposed portfolio.

The main goal of this research is to investigate the formation and spread of bubbles in the financial markets of the stock exchange, currency and gold markets using semi-experimental studies, considering that previous studies in this field mostly study the effect of volatility transmission or the effect of the return of one asset on the return of another market. Therefore, no study has been done in this field or it is limited; In this study, the data was collected in the period from 1389 to 1400 and was analyzed by descriptive and econometric statistical methods. The results of the analysis of the right-sequence unit root test show the existence of bubbles in all three markets under study. It shows the results of the analysis of vector auto-regression tests and copula (joint) functions. The structure of dependence between the three financial markets is quite dynamic and this dependence is greater when the market is in a developing situation than in a recession. Also, the sequential dependence between the gold coin and the exchange rate is much stronger than the dependence between the stock market and gold.

In recent years, in the financial literature, more attention has been paid to the level of cash holding of companies. So; Forecasting is important to determine the optimal level of cash holding. In this research, using linear and non-linear methods and 13 influential input variables, the amount of cash in 103 companies admitted to the Iran Stock Exchange during the years 2013 to 2021 has been predicted. The methods used include multiple linear regression (MLR), k nearest neighbor (KNN), support vector machine (SVM) and multi-layer neural networks (MLNN) for prediction. The results show that the traditional method of multiple linear regression has not been successful in predicting cash, but machine learning algorithms have been superior with an accuracy of 0.99. The variables of profit per share, the ratio of current assets to current liabilities and the ratio of short-term debt to total assets have had a greater impact in all algorithms. Therefore, managers can use advanced machine learning algorithms to predict the future cash flow of companies.

One of the most important issues in financial knowledge which is related to portfolio selection, efficiency market and asset allocation is spillover effect between markets and this effect includes return, volatility and shock effect. These days every shock or volatility in one market effect on other markets. Correct identification of spillover effect is very important. This paper aims to measure and analysis spillover effect between stock, currency, gold-coin, oil and housing markets. For these purposes we collect daily data of stock, currency, gold-coin, oil and housing for the time period of 2009 to 2020.we used VARMA-BEKK-AGARCH model for estimation. Results show return spillover from currency to stock and from stock to housing and shock spillover from currency, gold-coin and oil to stock and also volatility spillover from currency and gold-coin to stock and from stock to housing. Besides result show leverage effect of shocks from stock to housing market.so we suggest in order to minimize investment risk we had better to evaluate the spillover effects in selecting markets(assets)for our portfolio.

AbstractThe aim of the current research is to validate a model for using co-creation of value with customers in the banking industry. This study is cross-sectional in terms of research type. The statistical population includes Bank Mellat customers. The sample size was estimated to be 384 people and sampling was done by simple random method. Partial least squares technique and PLS software were used to validate the value co-creation model with customers. Finally, value co-creation indicators with customers were prioritized by Swara method. The research data collection tool is a questionnaire, which has been validated with convergent validity, construct validity and divergent validity methods. The reliability of the questionnaire was also evaluated using Cronbach's alpha and composite reliability. The results of the research showed that value co-creation strategy is considered as the underlying factor of the model, which affects organizational knowledge management and human resource management. Knowledge management and human resources also affect customer experience management. In the same way, customer experience management also affects strategic value co-creation and service development skills and ultimately lead to value co-creation with customers.

This research examines The Role of Intellectual Capital Efficiency on the Relationship Between Corporate Social Responsibility and Financial Performance (Evidence of Environmental, Social and Governance performance of Companies Listed on the Tehran Stock Exchange); The Time Frame of this Research is 2017-2021; The Statistical Population of this Research is all the Companies Accepted in Tehran Stock Exchange, 105 Companies were selected by Systematic Removal method. The Data of the Research was Extracted and Measured from the Financial Statements included in the Stock Exchange Sites; then, Statistical Regression Analysis was Performed Based on Panel Data. According to the Analysis, the Result of the First Hypothesis Showed that there is a Positive and Significant Relationship between the Corporate Social Responsibility and Financial Performance. The Result of the Second Hypothesis indicated that there is a Positive and Significant Relationship between the Corporate Social Responsibility and Intellectual Capital. Finally, in the Third Hypothesis, the Findings indicated that the Mediating role of Intellectual Capital on the Relationship between the Corporate Social Responsibility and financial performance is positive and significant.

Taxation is one of the main incomes of the government, and the government uses tax incentives as a key tool to improve tax compliance among taxpayers. This study aimed to understand the effects of tax incentives on tax compliance and business outcomes of taxpayers. Considering its purpose, this study is an applied research, and regarding its data collecting method, both field and secondary research methods have been used. In secondary research, we used books, national journals, and relevant articles form national and international databases, while in field research we used direct observations and interviews. Using various methods and tools for collecting the data, we investigated the agents, their interactions with each other and with the environment, and at the end, we analyzed the collected data using AnyLogic software, and the three hypotheses of the article were investigated. The results indicate that the tax incentives for any taxpayer can decrease the corresponding tax perception, which leads to tax compliance and change in the profitability and costs.