جستجوی مقالات مرتبط با کلیدواژه "anfis" در نشریات گروه "مالی"

Financing is one of the factors of future success for small and medium-sized technology businesses. Because of the immaturity of these businesses, the majority of their assets are intangible, rely on technical knowledge, and lack significant financial and credit records. To control the probable risks, financing systems traditionally rely on patterns based on maximum authentication of physical assets, which do not fulfill the needs of these firms and must be altered. The fundamental purpose of this research is to create a model for assessing and determining the collaterals of technical projects and knowledge-based firms so that finances could be more easily obtained and the risk of non-refunding could be managed. In this research, in the first step, the criteria for evaluating and assessing the intended project and company were defined. By using a fuzzy expert system, the appropriate method of dealing with companies was determined. In the second step, in order to create a learning system, the results of the first step were used as input data in the form of three neural network algorithms implemented in MATLAB software and ANFIS algorithm, with 93% accuracy compared to the input data.

The final neuro-fuzzy model was tested according to the repayment data available in Iran National Innovation Fund. The statistical population included knowledge-based firms receiving facilities. By using The Cochran formula, 103 companies were selected as a sample. The results obtained by testing and evaluating the model, in 85% of cases, could correctly identify the companies' performance in repaying resources and suggest appropriate collateral (Real estate collateral or Bank Guarantees).

The model presented in this study can be used to evaluate, validate, and determine the collaterals of technological projects and knowledge-based firms. It can facilitate their access to financial resources and also help them with managing the attendant risks. It also employs new optimization methods and has the ability to learn.

Iran Stock Exchange has developed a lot in recent years. Today, the importance of forecasting and its benefits for decision-making and policy-making from various dimensions, especially in the field of investment, is not hidden from anyone. Risk is one of the first concerns of investors and is an important criterion in decision making. Value at risk as a risk measure has given way to measuring a variety of risks, but despite the high efficiency of this model due to some shortcomings, including the lack of aggregation feature of a coherent risk measure. Conditional Risk Value (CvaR) is considered as a coherent risk measure that has recently been welcomed and has been proposed as a useful tool for measuring risk.To predict the risk, various models have been presented so far, each of which has its strengths and weaknesses. Some of them are weak in terms of lack of appropriate theoretical foundations and others have not shown proper efficiency in practice despite using appropriate theoretical foundations. Provide adequate empirical risk assessment that helps both investors and anticipate unexpected risks that may threaten companies. In recent years, much attention has been paid to the application of neural network models and hybrid models. In the present study, a combined model of coherent risk prediction is presented and developed using fuzzy neural network inference system (ANFIS) based on Markov switching models and Garch family models.

The effect economic variables at investment markets is the important subject in financial theory. Tehran stock exchange to have special position in country financial system and efficiency development investment market is dependent being active this constitution in country. Two important function Tehran exchange market are gathering small savings and available liquidity in society and guide them to production process in country. In this way presentation optimization portfolio model from market index prediction model and exchange return rate is impact. One of the tools with high accuracy and applicable for predicting was neural network why so accuracy isnot decrease with increasing thesis data and its accuracy was very higher than regeression, linear and non linear for prediction. After some tests from artificial neural network and adaptive neuro fuzzy inference system and support vector regression with matlab software has been done. We design a model with high accurancy for predicting rate of liquidity index and total return index and then we design Ideal optimization portfolio.

Over the past few decades, the number of universities has grown exponentially, but many of them are financially constrained. In the meantime, financial performance appraisal can support university administrators in making appropriate decisions by recognizing financial status. Accordingly, the purpose of this study was to codify a model for evaluating the financial performance of the university. This research has been applied in terms of the purpose, applied-developmental research and descriptive research in terms of the method which has been done intermittently. The statistical population of this research was all units of Islamic Azad University throughout the country. The statistical sample size was selected based on Morgan's table of 214 units of IAU. To collect data from the questionnaire, the Delphi method and the existing university documents were used. In order to analyze the research data, from Delphi methods and adaptive neuro-fuzzy inference system, adaptive neuro-fuzzy inference system combined with genetic algorithm and adaptive neuro-fuzzy inference system combined with a particle swarm optimization have been used. The research results show that among the designed systems, adaptive neuro-fuzzy inference system combined with a particle swarm optimization with the least error is able to evaluate and predict the financial performance of the university.

In this paper, Symbiotic organism search and memetic algorithms are used to solve constrained mean-semi variance portfolio problem. Then AR model, Neural network and ANFIS are compared to predict expected return of stocks. 23 active stocks from June 22, 2014 to Jan 21, 2016 are used as our sample. The results indicate that, memetic algorithm despite its longer time consuming has better performance than SOS algorithm. And ANFIS has more accurate prediction than others in predicting expected rate of return. Finally, we compare expected and real efficient frontier, the results indicate that, in lower risk, ANFIS has better prediction accuracy. So in that area, we can allocate our asset with higher certainty.

Stock market prediction is important because successful prediction of stock prices may promise attractive benefits. This task is complicated and very difficult. In this paper, the predictability of stock market index with Adaptive Network-Based Fuzzy Inference System (ANFIS) is investigated. The goal is to determine whether an ANFIS algorithm is capable to predict stock market return and trying to find best architecture. We attempt to model and predict the return on stock price index of the Tehran Stock Exchange (TEDPIX) with ANFIS. We use six macroeconomic variables as input variables. The experimental results reveal that the model successfully forecasts the daily return of TEDPIX Index. ANFIS can be a useful tool for economists and practitioners dealing with the forecasting of the stock price index return.