نشریه مهندسی مالی و مدیریت اوراق بهادار
پیاپی 41 (زمستان 1398)

One of the most important issues of banking managers as an influential variable on the banking industry is the knowledge of the status of bank deposits that the bank depends on a large extent on it. Therefore, bank managers are keen to know how much the total bank deposits will be at a given time in the future. Predicting the amount of deposits, changes and fluctuations of these deposits can help banks in planning and decision making. In this research, using statistical techniques and approach of artificial neural network models, we have tried to introduce a model with the highest estimation power and the least amount of error to predict the amount of deposits or the same sources of finance by their different types for the desired bank. To test the hypotheses, one private bank information was used during the period of 1387-1396. In this research, we compared the predictive power of ARIMA and artificial neural network method. To assess the accuracy of forecasting the bank's resources, the ARIMA method used Coopiff and Christopherson tests.  The results of the research on the amount of bank deposits monthly showed that the neural network method provides better estimates than the ARIMA method.

The variety of investment methods and the complexity of decision making has strongly developed in recent decades, and due to this  widespread  growth , there has been created need to inclusive and integrative models . to meet this need , financial modeling is created from the connection between financial approach and mathematical planning.
Assessing risk assets is one of the most important research issues in the financial field .There are various pricing models of capital assets in financial. In many models, it is not possible to consider a lot of restrictions on portfolio selection. In this paper, for choosing optimal portfolios, taking into account the prosperity and recession periods, and the types of investors in terms of risk taking and risk aversion as a limitation, fuzzy goal models(black hole algorithms ,hybrid algorithms and gravity) have been used. And finally, it has been compared to the results of the Markowitz pricing  model.

The purpose of this study was to study the reaction of investors to the objective and objective suspicion of profit components in suspected financial distress in a        11-year period from 1386 to 1396 with 1320 views in 120 companies accepted in Tehran Stock Exchange. To analyze the data and test the hypotheses, the simulation model of Mishkin (1983) and the multivariate regression model were used using the Eviews software version 10 has been used.The findings of the research indicate that investors have the mental sustainability of cash components and earnings accruals in relation to the objective stability of cash components and earnings accrual, more than actually they value it.Therefore, investors are pricing inappropriately, the profit of companies suspected of financial distress and the capital market is not as efficient as the components of profit information. In other words, companies suspect financial distress, the investors' incorrect responses and inappropriate pricing of them.

Evaluation of stocks based on return and risk related to capital assets is one of the important issues of this field. The majority of multifactor models are defined based on the assessment of one of the return and risk criteria. Nevertheless, the model presented in this study evaluated return and risk simultaneously. The multifactor patterns are static and do not express dynamic changes during time intervals affected by latent factors. In this research, unpredicted fluctuations in stock return were defined as latent factors in the penalty function. A more accurate estimate was provided by using the simulation of Fama–MacBeth regression in the estimation of effective parameters and separation of the effects of latent and manifest factors affecting stock return and risk. According to the analysis of the field of knowledge and content analysis, factors affecting the stock return were recognized, and the most effective factors including market measures were refined as manifest factors based on the tolerances. Finally, the model proposed(P-PCA) was exploited in risk prediction (at risk value). According to the results of the study, the mentioned model more efficiently showed the effects of latent and manifest factors on stock return over a long period. In addition, it was able to predict the risk of investment with proper accuracy and similar to patterns of conditional variance, such as ARCH and GARCH.

The purpose of this study is to identify the factors affecting investment decisions and to investigate the relationship between the level of financial literacy of investors and their investment decisions. This is a correlation study and a questionnaire was used to collect data. This questionnaire identifies the factors affecting investor decisions, as well as assesses the level of financial literacy and risk perception of investors. The results show that individual mentality, accounting information, economic information, and personal financial needs affect investment decisions in Iran. While there is no meaningful relationship between financial literacy and investor decisions. It was also determined that age, gender, marital status does not have a significant relationship with investors' decisions, but the level of education has a direct and meaningful relationship with the financial literacy of investors. In addition, the result shows that taking into account the level of risk perception of investors, their risk appetite is influenced by their investment decisions.

The analysis of the turbulence among markets has been widely applied and in details the emphasis on theorists and researchers in different fields. The reciprocal relationship between financial markets and the macroeconomic markets is based on the idea, which has negative shocks to the economy of the housing sector. The behavior of the housing market is also important because of the impact of housing prices on bank loans and other financial institutions. In the housing sector, a monetary and monetary policy is expected to increase demand for housing by increasing the volume of money in the asset portfolio. Of course, this will depend on a variety of issues. Therefore, the need for further reflection seems necessary in this part. In this study, using the time series analysis of vector regression (VAR), the relationship between financial and financial markets has been investigated. The results of the study show that financial markets, especially foreign exchange markets and gold markets have a significant relationship with housing.

Financial and economic decisions are always at risk due to future uncertainties. Therefore, one way to help investors is to provide investment risk forecasting models. But as these projections are closer to reality, decisions that are based on such predictions will be more correct. The main purpose of the present experimental study is to predict systematic risk with emphasis on financial and non-financial variables. The statistical population of this research is the companies accepted in Tehran Stock Exchange. The data of the study consist of 552 Firm-year from 2012 to 2017. To test the hypotheses, the artificial neural networks approach and night worm algorithms, decision tree and regressor of backup vector machine have been used. The results of this study showed that the results obtained from the hypothesis test showed that all three of the algorithms have the power to explain systematic risk.

Businesses are constantly changing, and changes are also widespread and unpredictable. So that every day we see the creation or integration of a variety of businesses based on the foundation of new models. The banking system is no exception to this, and in order to prevent failure, it has adapted itself to various existing models to stabilize its presence. Considering that in past research, the merger of banks has been investigated according to the financial approach. In this research, the researcher tried to design a model based on cooperative marketing principles when integrating between banks. The research method is qualitative. Therefore, in order to achieve the best possible variables in this field, the fundamental method of content analysis and using the ground-based theory, first, the relations between the concept and its dimensions and components are formulated. In order to achieve a conceptual model, the views of experts and banking experts were used through interviews. And collecting comments until a consensus was reached, Snowball method was used for sampling. After 37 interviews with experts, the ultimate conceptual model, which included the concept of co-operation of banks, which was dependent on six dimensions, was also based on sixteen components.

Nowadays, one of the most important issues that projects-based companies need in order to better manage project portfolios and financial planning is managing project costs. In project cost management, using a set of techniques and principles for project and financial control, it is possible to examine the financial performance of each project in terms of its actual operating cost (actual cost) compared to the estimated cost for the work performed and the corrective solutions to reduce costs And put it under control.
Since reducing the duration of activities by allocating more resources to each activity is possible but requires higher costs for the implementation of those activities, it should be noted that the hasty pursuit of activities can lead to a decrease in its quality level Therefore, in the process of compressing the time of activities, in addition to the cost index, the activity quality index should also be considered. In this paper, a mathematical model for project cost management based on the critical path method approach is presented. Considering a specific figure under the project budget ceiling, two goals are to minimize the total cost of the project (the classical model) and aim to maximize the quality of the activities, simultaneously.

Market power specifies how firms in a market influence prices, and reveals the level of competition in the market. This paper investigated the impact of market power and macroeconomic variables effect on banking deposits in Iranian deposit money market using dynamic Bresnahan-Lau’s and Bayesian model averaging approach for the 18 bank in the Iran industrial banking sector. Annual data for the period of 2006- 2016 has been collected from annual financial statements of Iranian banks, Statistical Centre of Iran, Monetary and Banking Research Institute of Iran and central bank of Iran. By applying BMA approach the main macroeconomic variables have been determined from 8 variables. The results of model estimation indicated that market power for its 18 Iranian banks were 0.58 and the macroeconomic variables of liquidity money has negative and other explanatory variables such as fixed assets, growth of GDP, first order lag of deposits, deposits rate and cross effect of GGDP with deposits rate have positive posterior average on banking sector's deposits during the period of study.

Ranking companies based on transparency of financial information can help better decision making in the stock market. In this study, by examining the financial statements of companies and measuring the transparency of financial information, their ranking is based on neural and fuzzy models. Another purpose of the present study is to compare neural and fuzzy models for ranking the transparency of corporate financial information. This is an applied research based on descriptive-analytical method. The statistical population includes the stock exchange companies during the years 2008-2009 and 198 companies were selected and analyzed by systematic elimination method. Company information was obtained through stock exchange software. Artificial intelligence algorithms were used for ranking and fuzzy and neural models were used. The results showed that among the fuzzy and neural models, the best method for ranking is the neural models and the results of the neural models method give the best results. Given that in fuzzy model, fuzzy method has the most errors and in some estimations it has unacceptable errors.

Earnings forecasting systems provide timely decisions by providing timely information. Earnings forecasting by management is widely used in assessing profitability, profit-related risk, stock price judgments, and valuation models (Manfred & Inky, 2014). Our purpose in this study is to investigate and investigate the impact of dimensionality trading strategies on predicting daily stock market returns by the fuzzy logic approach of firms. This study is a library-analytic-causal study based on panel data analysis (panel data). In this study, the financial information of 19 companies listed in Tehran Stock Exchange during the period 2011-2018 was reviewed. The results showed that using stock trading strategy and stock price reduction strategy have significant effect on prediction of daily stock market returns, but trading volume reduction strategy has no significant effect on market forecasting. I hope to accept my article. I suggest the editor remove this restriction on the number of words used in the abstract for the English text.

One of the important issues in predicting financial constraints is the choice of predictor variables. In this study, we investigated the Gaussian process machine learning method and radial neural network to predict financial constraints. For this purpose, 208 companies from 1390 to 1396 have been selected as the statistical population. Due to the availability of information, all companies have been considered as a statistical sample. The results of this study showed that machine learning methods have the ability to predict the financial constraints of corporations admitted to Tehran Stock Exchange. Therefore, the main hypothesis of this research is confirmed and machine learning methods are an effective way to predict financial constraints. Also, the results showed that the company's value, operating cash flow ratio, financial leverage, return on assets, and the percentage of institutional owners had the most importance in predicting financial constraints.

The introduction of chaos theory in various disciplines of science is in the same direction as the systemic vision was introduced in the previous years. The basis of this theory is based on the theory of holistic or system theory. Based on this theory and its principles, although complex systems have a seemingly turbulent appearance, they actually follow a non-linear and intermittent regular order and flow and they may change at any time of a single day. This theory has changed the foundation of all sciences and created innovative ways to use computers, images and charts. In this paper, we tried to study the impact of this theory on economics and, in particular, on the theory and practice of this theory in other sciences, and the importance and necessity of paying attention to the theory and the various uses of this theory in other sciences. In the documentary method, the model was presented.

Fuzzy multifunctional sets reduce the need for accurate data for decision making. Data Envelopment Analysis is a theoretical framework for performance analysis and performance measurement. Fuzzy increases the application of data envelopment analysis. Measuring the performance of companies with the help of data envelopment analysis can help investors in choosing a company. In this paper, the problem of selecting fuzzy portfolios in a multipurpose framework is examined. A comprehensive model for selecting multi-purpose portfolios in fuzzy environment is presented using a semi-variance model and a model for analyzing information development with cross-sectional returns. Data from 40 companies accepted in Tehran Stock Exchange and trapezoidal returns of 40 sheets of securities and the data required for inputs and output of data envelopment analysis were obtained from financial statements of companies from the beginning of 1396 to the end of 1396. 16 financial parameters were used. Sharp ratio, cross-sectional return model in Sharp ratio and multi-purpose firefighting algorithm for solving multi-purpose stock optimization model was used. Analysis was done with MATLAB software. The results showed that the proposed method in this research is more suitable for selection of fuzzy multipurpose portfolio than other methods and provides better results for performance analysis, efficiency and company selection for investment.

Application of traditional approaches based on Discounting the cash flow in order to evaluate and comparing investment opportunities including Venture Capital in most industries is a very common issue. Due to the lacking deep certainty in this sort of venture, relying on traditional approaches could end up with inappropriate decision making, because of ignoring the managerial flexibility. At this study the real option approach in estimated capital budgeting was compared with traditional approach based on Discounting the income. The research hypothesis was that the real option approach could improve the quality of decision making and optimize the investing portfolio from the high risk projects. Accordingly, a venture capital company in the field of pharmaceutical products and medical and therapeutic equipment was chosen for applying the model considering two indicators of the availability and completeness of the financial information of the plans by using the case study approach. The results indicated that considering the value of flexible deciding, the efficacy of the capital budgeting model based on dynamic planning and real options shows more precision than traditional budgeting approach.

One of the emerging phenomena in the world is the emergence of Fintech in banking. This has threatened governments' control over money and has made it easier to transfer money around the world. Technology development and social change affect customer behavior and lead to change in customer relationship management. The purpose of this research is to study what Fintech developments in Iran and content analysis are trying to do in terms of what Iran has done in the field of banking and in which financial areas it will be able to start a new business. Some in the financial services industry see Fintech's progress as a threat to the traditional banking industry, and some believe Fintech is a challenge that can be turned into an opportunity. The research method in this paper is grounded theory which was first analyzed using library study and interviews with government officials in the field of banking and the key points were extracted. Therefore, in order to collect additional information for content analysis, a questionnaire was used using keywords. At the output of the questionnaire, the frequency of keywords and their importance coefficients were extracted. Finally, the weaknesses, strengths, threats and opportunities in entrepreneurship and innovation were extracted. The results are listed in the SWOT strategy table, which outline the strengths of the strategies to address the weaknesses and, using opportunities, provide strategies to turn the threats into opportunities.

The purpose of this study is to create an expert system for predicting daily business decisions in a normal environment of a financial market. The template of our study system consists of a multiple FIS framework that includes three dedicated FISs for decision-making on stock purchase, buying, holding and sales. The basic information system of the relevant companies and the historical stock prices that are processed to provide technical information is received as an input to the multiple FIS framework. This framework provides an investor with a series of proprietary asset documents on a daily basis in each of the buying, holding and selling systems. The review of this framework in the markets of Tehran Stock Exchange shows that considering market information as input and technical information, as compared to systems that use only technical information as input information, increase Returns are profitable. This framework, known as a stock market index, has yielded better performance than some of the most famous technical indicators such as Divergent / Convertible Moving Average, Relative Strength Index, Stochastic Oscillator, and Chaikin Oscillator. This developed framework, in addition, shows that risk-taking is not a positive factor for increasing profitability, and having basic information can increase risk aversion

In recent years, increasing economic losses as a result of natural disasters are one of the main challenges fronting the insurance industry and researchers to discover original financial instruments so as to transmit disaster risks and minimize economic losses. In the present article, a model is suggested for catastrophe swap pricing with deterministic loss fluctuations in order to decrease the risk of insurance and reinsurance companies in Iran. The research is retrospective and applied; the data collection method is the library, and for the data collection use the documents. For the full data extraction, the correlation method is applied, For the purpose of extracting the complete data, the correlation method is used, all damages of earthquakes that have been fatal, destructive and affecting in the period 1927 to 2018 in Iran, have been investigated. The probability of the deterministic loss occurrence and severity are regarded to be Brownian motion of jump-diffusion. The extracted integral-differential model is converted into the standard differential one, and the answers are estimated via finite difference method and Matlab software. The changes to the suggested model are explored through the Lambda sensitivity analysis. As a final point, the model is implemented with real data of earthquake losses in Iran, which is extracted from the EM-DAT database and the regression results. Based on the results of the study, the price of catastrophe swap securities for less loss than the threshold has regular upward trend; however, once loss reached and passed the threshold, prices will drop dramatically.