fuzzy logic

The most important factor in investing is choosing the asset that will bring us the highest expected return. Each investment forms its portfolio by predicting the investment risk and estimating the expected return. Various methods have been used to estimate the expected return on investment in recent decades, the most important of which is the Markowitz mean-variance model. But this model has been faced with flaws, so financial researchers have tried in the following decades to obtain more reliable findings for portfolio optimization by adding higher moments such as skewness and kurtosis and entropy. The statistical population of this research is based on trading data (adjusted price) of the top 35 symbols of the the Iranian capital market during the last ten years from 2013 to 2023 from the software bourseview, tsetmc, and the variables including variance, mean, skewness, kurtosis and entropy after processing. Data were implemented using Matlab version 6.7 software and portfolio optimization was done based on mv, mvs, mvsk models and finally MVSKE model. The findings of this research indicate that informational entropy is a reliable variable in portfolio optimization.

Optimization of banks' foreign exchange portfolios aiming to determine an optimized combination of foreign exchange assets is in such a manner that it leads to a minimum risk and maximum yield. The approach used in this article, which is actually a priority over other methods is to use a combination of inexact mathematical models (fuzzy) and optimization. Thus, in this research, linear programming with fuzzy target coefficients has been used. The target coefficients are the same as the exchange rate. In other words, the daily rates of all currencies due to fluctuations are considered as fuzzy numbers. Therefore, in this investigation, by developing a multi-purpose mathematical model and by applying the fuzzy data relating to transaction rate of six foreign exchanges in year ended on March 20, 2020, including US dollar, Emirates Dirham, Japanese Yen, Turkish Lira, Korean Won and Euro, case by case risks related to the fluctuations of noted foreign exchanges and also their yield in the foreign exchange portfolio of Bank Mellat were measured and estimated for the future time horizon. All banks may benefit from the developed model. By determination of the optimal weight of each foreign exchange, upon describing and analyzing the existing status it specifies the optimal status, enabling banks to fulfill their foreign exchange obligations on due dates by properly and optimally making investment in foreign exchange assets meanwhile gaining a competitive advantage. "Gams Software" was used for solving the model.

The study of the relationship between the development of information and communication technology(ICT), financial development and economic growth has always been the focus of researchers and policy makers and due to differences in views on the relationship between those and the diversity of modeling of studies in this field, researchs has not resulted in a single result. On the other hand, with the development of technology and the introduction of fuzzy logic, developed modeling methods and the possibility of consolidating expert opinions in an intelligent inference system based on collective wisdom to create indicators of ICT development and financial development now exists. Accordingly, in this study, after consolidating the opinions of experts in a knowledge base consisting of 414 questionnaires on the relationship between the constructive sub-indicators of financial development and ICT development, fuzzy indicators of financial development and ICT development using the Mamdani Fuzzy Inference System(MFIS) then assess the short-term and long-term causal relationships between ICT development, financial development and economic growth in the form of a panel error correction model considering the Dynamic Common Correlated Effects(DCCE) using annual provincial data during The period from 2001 to 2017 was covered. After consolidating the opinions of experts and creating fuzzy indicators, only in the short run, the positive causal relationship from ICT development to economic growth was confirmed.

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.

Asset return is associated with uncertainty and always occurs during unexpected fluctuations in economic, social and political conditions, and so forth. In return on assets such as stocks. Fuzzy logic can be one of the best options for modelling asset returns. For this purpose, a rule based fuzzy expert system has been developed to support investment managers in their mid term investment decisions. Considering the non linearity of the portfolio selection problem and its NP Hard, the performance of the proposed fuzzy system is evaluated by the information of 157 companies that have been active in Tehran Stock Exchange between 2008 to 2018 using of Fuzzy Genetic and Fuzzy Hunting Search Algorithms. The system's performance in terms of risk taking and duration of investment was comparable to average market returns. Besides, the performance of the proposed fuzzy system for the risk averse investor in the short run yields good results.

The purpose of this research is designing a credit rating model for issuers and tools for financing Islamic securities in the Iranian capital market. To do this, three major steps were taken. The first step was to identify the evaluation criteria or the risks associated with the Islamic securities, which was carried out by the experts and a review of theoretical basics. The second step, is modeling of Islamic securities using adaptive-network-based fuzzy approach in which the mean error of the training of all main and subset models was below the threshold. The third step is to apply adaptive fuzzy neural network modeling in credit rating of Islamic securities. In order to do this, the issuer’s ranking was used in the first stage and the results of the research showed that the issuer of the government had the least risk and private companies had the highest risk. In the second stage, for ranking financial instruments, the results showed that for issuer of government, treasury bonds had the lowest risk and forward bonds had the highest risk. For the issuer of state-owned companies, the forward bonds had the highest risk and lease bonds had the lowest risk.

Major developments in the business environment, such as globalization and the rapid pace of change in technology, have increased competition and management difficulty in organizations. In complex environments, organizations need managers to consider these inherent complexities when making important decisions. Effective risk management is an important part of this decision-making process. The purpose of the present study is to evaluate corporate risk management by analyzing the relationship between gray matter and entropy weight in listed companies of Tehran Stock Exchange. The sample included 20 companies during the years 2013 to 2018. In this study, we first sought to find relative weight for measuring corporate risk management by using variables (strategic risk management, operational risk management, risk management reporting and risk management non-compliance). Then, based on the results of the entropy analysis, we will try to determine the rankings of the companies in terms of risk management performance using the gray relationship analysis in the companies. The results of this study indicate that many companies have high performance in risk management.

The present study aims to investigate effective factors on productivity through analytical approach and using AHP method, fuzzy approach and econometric analysis. Paper Analyses the effect of investment and industrial labor productivity on the total factor productivity in this sector (at macro level) with the panel data approach, and it examines the individual productivity indicators of the industrial labor by analyzing the responses of 130 employees of the company National Distribution of Iranian Oil Products in Tehran Region as a Case Study at Micro Level (Using AHP and Fuzzy Method). The result show that: The indicators of physical and human investment, R & D and real exchange rate are factors influencing the total productivity of the factors of production in the industry; among other things, the R & D index, as compared to other indicators, has a longer-term impact on total factor productivity In addition, direct impact of physical investment in the industrial sector in Iran, is the concept of a faster evaporation of the variable on productivity, even in the short term. Also, the effectiveness of individual labor productivity index on total factors productivity emphasize on factors affecting on the productivity of individual labor, and shows that monthly salary indices, reward system, education, promotion of job satisfaction and system Meritocracy (from the perspective of employees), respectively, has the greatest effect on the productivity of industrial labor and consequently, the total efficiency of the production factors of this sector. .

Researchers have been researching portfolio optimization issues for several years. One of the main issues is to determine the optimization method, which is to form an optimal investment portfolio, ie to minimize investment risk and maximize investment profit. The aim of this study is to investigate the strategic capability of network matrix and fuzzy genetic neural model (ANFIS) in optimizing the investment portfolio among companies on the Tehran Stock Exchange. Grouping stocks by network matrix based on new variables including aggressive, indifferent and defensive stocks provided by Roodpashti (2009) and traditional variables including growth, growth-value and value stocks and classification of companies based on their market value and use. From the law of quarters and finally their weighting is considered in proportion to the return of that share. The design and presentation of a stock portfolio optimization model using adaptive fuzzy neural inference system and its combination with genetic algorithm (ANFIS) in which two different categories of technical and fundamental variables are used as model inputs. Research outputs show that these systems have the necessary ability to optimize the stock portfolio. Therefore, a combined model of neural networks and fuzzy reasoning theory with genetic algorithm has been used to weight the factors affecting stock portfolio optimization in the 7 years leading up to 1398.

Economic appraisal of investment projects requires a new thought referred to as real option theory given the governing uncertainty over their financial conditions which can cover deficiencies of traditional methods of capital budgeting such as stationary and not considering uncertainty factors. According to this theory, a model was proposed in the present study to identify and rank environmental uncertainties and effective managerial flexibilities or options on investment decisions in a photovoltaic plant in the south of Isfahan. To this end, uncertainty factors of this plant were identified via field study from the viewpoint of a sample consisted of 36 experts through confirmatory factor analysis. Then, impact factor of each factor on advantages and expenses of the above plant was evaluated using the fuzzy hierarchical analysis. In the next step, the effect of these options on the project value was tested by means of linear regression method. Tools of data collection were two researcher self-made questionnaires. Validity and reliability of each questionnaire were confirmed through Lawshe's content validity index and Cronbach's alpha coefficient. The results indicated the effect of uncertainty factors on investment environment of the plant and a significant relationship between the use of real option and flexibilities with increased effect of investment in this plant. Meanwhile, the experts positively evaluated the effect of expansion, wait and abandonment options to increase the flexibility of investment decisions in this plant.

The problem of portfolio optimization and stock selection is one of the major areas for financial investors in financial markets. In this paper, some of the challenges of simultaneously multi-objective portfolio optimization are addressed. Four different models are designed: a fuzzy multi-objective programming model has been used to consider the multi-criteria nature of stock selection and the uncertainty associated with the return on assets and a simple model for doing this. The models are designed in such a way that both the nature of the multiplicity of the problem of portfolio selection is considered and the considerations of the investor in the choice of portfolios are involved. After designing the evolutionary 3 and 4 objective models of portfolio optimization, multi-objective evolutionary algorithm NSGA-II was used to solve this models. Concretely, it optimizes return, the downside-risk, skewness and the Kurtosis of a given daily returns, taking into account budget, and investor constraints. Because of the NP-HARD nature of the above models, the NSGA-II proprietary algorithm was coded in the MATLAB, and after solving each model and extracting the Pareto frontier, the best portfolio on the Pareto front was selected based on the maximum Sortino ratio. Finally, the results of the obtained portfolios in both fuzzy and non-phase conditions were compared according to the trainer's ratio, and it was determined that the use of fuzzy logic in quadratic evolutionary algorithms, compared to a situation where fuzzy logic is not used in the design and use of these algorithms., Creates more favorable results.

One of the most important problems in capital market is allocating financial resources in an optimal fashion. In an effective capital market, from an operational point of view, the capital is allocated for the best investment option. Therefore, in order to establish more output, making use of appropriate management tools is a step toward more effective market management of transactions. Regarding backgrounds of applying Artificial Neural Networks and Fuzzy Logic in stocks investment and financial prediction, applying them in selecting an appropriate portfolio can lead to desired results for investors. The major goal of the current research is to achieve an optimal investment portfolio in capital market by applying Artificial Neural Network and Fuzzy Logic. Accompanied by Markowitz Model, models were used which were created through Artificial Neural Network. In order to establish investment portfolio, some of those companies were selected which were active in Tehran stock exchange, and which have had positive efficiency from the year 1386 to 1395. In order to evaluate the suggested portfolios in different conditions, the output of different portfolios based on the monthly and yearly output of the member companies were compared and optimization of suggested portfolios using genetic algorithm were carried out. The study shows that using the Fuzzy models versus mentioned models would provide higher output for the investors.

The purpose of this study was to compare the predictive power of fuzzy neural network with fuzzy wavelet neural network in predicting stock prices of banks in Tehran Stock Exchange. The period of this research was from 2011 to 2016. In this research, the fuzzy logic system with the use of a multi-layer neural network system with an error-optimized back-propagation optimization structure and a Maximum Overlapping Discrete Wavelet Transform for exchange rate variables, opec oil, each ounce of gold, the total stock index as well as the volume of trades were used in order to predict stock prices.The results of the model were done by using the updated cost function. The results of the research in comparison of fuzzy wavelet network and fuzzy neural network showed that the reliability of banks with fuzzy wavelet neural network is over 90% and with fuzzy neural network above is 80%. As a result, fuzzy wavelet neural network is more reliable than fuzzy neural network

Selection of appropriate time and price in trading stocks has an important role in investment decisions on profit and loss of investors in capital markets. Nonlinear intelligent systems, such as artificial neural networks, fuzzy- neural networks and genetic algorithms, would be used to forecast stock prices motions. In this article,a model of stock prices motions has been designed using Adaptive Neuro- Fuzzy Inference System (ANFIS)integrated with genetic algorithm, in which two different groups of fundamental and technical variables have been employed as model inputs. According to Model outputs,the rate of forecasting errors in both groups of inputs is not significant and these systems are able to forecast daily stock prices. The Mann-Whitney test has been used to measure the accuracy of models and it was found that there is no significant difference between results of prices forecasted in both methods. Both methods are able to forecast next day price with an insignificant error provided that at least one of the inputs in both methods has a linear dependence with price, . Also, results show that these systems do not work properly to forecast prices of high volatility stocks.

One of the most important economic project evaluation methods and the comparison between them is current values. This method is one of the most important and the simplest techniques in engineering economy. The calculation of the present value of the traditional methods to compare projects is not enough because it does not consider a major uncertainty factor. In this paper, we present an approach for the selection of projects by using current value. In order to include the uncertainties in the problem, fuzzy logic is used. Then, using analytic hierarchy process and binary integer programming with respect to the other objectives of the project, the most economical project was chosen. This model considers qualitative and quantitative objective and uncertainty to select the best projects in real-world situations.

One of the new areas in financial researches is using artificial intelligence to assist building decision making systems. Stock trading system is one of those systems developed to help investors make successful trading operations. Successful trading operation must be done near turning point of price trends. In recent years many studies focused on preparing systems to suggest price trend reversal. Technical analysis which tries to provide trading signals is mostly used in such systems and is usually one part of the system.
Technical analysis with a lot of rules try to give trader the signals of price trend reversal but the disadvantage of technical analysis is its dependency to investors experience to decide on technical rules and parameters. In fact the performance of technical analysis is deeply dependent to quality of setting technical parameters.
In this study we try to build a trading system based on technical rules and enhance its performance by using Genetic Algorithms, Fuzzy Logic and Artificial Neural Networks. GA helps us to train technical parameters in technical rules. Fuzzy logic helps us to discern how is the condition of market (trending market or none trending market). Because it is important to select kind of rules. When different enhanced rule provide their trading signal concerning market condition, an ELMAN network combines different signals together to provide trading suggestion.
Results from Tehran stock exchange consist of 10 stocks demonstrate that statistically there is significant difference between performance of our proposed system and grand trading strategies such as buy and hold strategy. In other words, our system possesses profitability potentials.

Interest rate is one of the most important monetary policy instrument in an economy. The changes in interest rate leads to changes in other key economic variables like cost of production, Consequently changes in output and price level. However, like the other developing countries, lack of the advanced financial markets and governmental determination of interest rate , the market interest rate cant be observered in Iran. For this purpose, the main objective of this study is to estimate market interest rate index by using information on proxy variables such as inflation rate, rate of return on housing and amount of liquidity in economy during 1981-2012 by using fuzzy logic method. The empirical result indicates that interest rate has shown a lot of fluctuations over time. The maximum (78/7) and minimum (22/4) interest rate index has been in years 1995 and 1985 respectively.

The aim of this study is to construct appropriate portfolios by considering investor’s risk profile and his/her preferences in a flexible, applicable and realistic manner. For this purpose, a fuzzy rule- based expert system is developed to support investment managers in their middle term decision makings. The proposed expert system is validated by using 106 stocks, traded in Tehran Stock Exchange between 1384 and 1389. Performance of the proposed expert system is analyzed in terms of risk profile and investment length compared to market average. The results revealed that performance of the expert system is better than the market average in most cases. Also, in parallel to our expectation, the proposed expert system performance is better for risk-averse investors and in middle term.