Based on the network paradigm of complexity, a systematic analysis of the dynamics of the largest stock markets in the world has been carried out in the work. According to the algorithm of the visibility graph, the daily values of stock indices are converted into a network, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock market, and their specific behaviour in the pre-crisis period is used as indicators-precursors of crisis phenomena. The influence of globalization processes on the world stock market is taken into account by calculating the interconnection (multiplex) measures of complexity, which modifies in some way, but does not change the fundamentally predictive possibilities of the proposed indicators-precursors.
{"title":"Visibility graphs and precursors of stock crashes","authors":"V. Soloviev, V. Solovieva, A. Tuliakova","doi":"10.33111/nfmte.2019.003","DOIUrl":"https://doi.org/10.33111/nfmte.2019.003","url":null,"abstract":"Based on the network paradigm of complexity, a systematic analysis of the dynamics of the largest stock markets in the world has been carried out in the work. According to the algorithm of the visibility graph, the daily values of stock indices are converted into a network, the spectral and topological properties of which are sensitive to the critical and crisis phenomena of the studied complex systems. It is shown that some of the spectral and topological characteristics can serve as measures of the complexity of the stock market, and their specific behaviour in the pre-crisis period is used as indicators-precursors of crisis phenomena. The influence of globalization processes on the world stock market is taken into account by calculating the interconnection (multiplex) measures of complexity, which modifies in some way, but does not change the fundamentally predictive possibilities of the proposed indicators-precursors.","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2019-10-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121114745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Assessing the quality of banking services based on fuzzy logic method","authors":"O. Syniavska, V. Oliinyk","doi":"10.33111/nfmte.2018.188","DOIUrl":"https://doi.org/10.33111/nfmte.2018.188","url":null,"abstract":"","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128608759","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-18DOI: 10.21511/NFMTE.7.2018.03
Hennadii Ivanchenko, Serhii Vashchaiev
The article highlights the results of a study of the dynamic evolutionary processes of trophic relations between populations of enterprises. A model based on differential equations is constructed, which describes the economic system and takes into account the dynamics of the specific income of competing populations of enterprises in relations of protocooperation, nonlinearity of growth and competition. This model can be used to analyze the dynamics of transient processes in various life cycle scenarios and predict the synergistic effect of mergers and acquisitions. A bifurcation analysis of possible scenarios of dynamic modes of merger and acquisition processes using the neural network system of pattern recognition was carried out. To this end, a Kohonen self-organizing map has been constructed, which recognizes phase portraits of bifurcation diagrams of enterprises life cycle into five separate classes in accordance with the scenarios of their development. As a result of the experimental study, characteristic modes of the evolution of economic systems were revealed, and also conclusions were made on the mechanisms of influence of the external environment and internal structure on the regime of evolution of populations of enterprises.
{"title":"Studying the dynamics of nonlinear interaction between enterprise populations","authors":"Hennadii Ivanchenko, Serhii Vashchaiev","doi":"10.21511/NFMTE.7.2018.03","DOIUrl":"https://doi.org/10.21511/NFMTE.7.2018.03","url":null,"abstract":"The article highlights the results of a study of the dynamic evolutionary processes of trophic relations between populations of enterprises. A model based on differential equations is constructed, which describes the economic system and takes into account the dynamics of the specific income of competing populations of enterprises in relations of protocooperation, nonlinearity of growth and competition. This model can be used to analyze the dynamics of transient processes in various life cycle scenarios and predict the synergistic effect of mergers and acquisitions. A bifurcation analysis of possible scenarios of dynamic modes of merger and acquisition processes using the neural network system of pattern recognition was carried out. To this end, a Kohonen self-organizing map has been constructed, which recognizes phase portraits of bifurcation diagrams of enterprises life cycle into five separate classes in accordance with the scenarios of their development. As a result of the experimental study, characteristic modes of the evolution of economic systems were revealed, and also conclusions were made on the mechanisms of influence of the external environment and internal structure on the regime of evolution of populations of enterprises.","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132993230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-18DOI: 10.21511/NFMTE.7.2018.04
S. Klepikova
The article is devoted to the creation of a method for using of neural networks approach in solving problems of energy efficiency management at the industrial enterprise. The method allows to obtain an approximate expected value of the energy intensity of production, depending on the values of the main factors affecting it. The multilayer perceptron was chosen as the type of neural network, synthesis of which was carried out by using the genetic algorithm. When sampling for the synthesis of a neural network, we used the results that were obtained by means of a priori ranking, correlation and regression analysis based on the statistical data of industrial enterprises in machine-building profile. The recommendations of the use of the method and the application of its results in the practical implementation at the industrial enterprise are given. Calculations based on the aforementioned method ensured a high precision of prediction of energy intensity values for industrial enterprises that were included in the sample during the synthesis of the neural network, and an acceptable error while testing on industrial enterprises from a test sample.
{"title":"Neural networks application in managing the energy efficiency of industrial enterprise","authors":"S. Klepikova","doi":"10.21511/NFMTE.7.2018.04","DOIUrl":"https://doi.org/10.21511/NFMTE.7.2018.04","url":null,"abstract":"The article is devoted to the creation of a method for using of neural networks approach in solving problems of energy efficiency management at the industrial enterprise. The method allows to obtain an approximate expected value of the energy intensity of production, depending on the values of the main factors affecting it. The multilayer perceptron was chosen as the type of neural network, synthesis of which was carried out by using the genetic algorithm. When sampling for the synthesis of a neural network, we used the results that were obtained by means of a priori ranking, correlation and regression analysis based on the statistical data of industrial enterprises in machine-building profile. The recommendations of the use of the method and the application of its results in the practical implementation at the industrial enterprise are given. Calculations based on the aforementioned method ensured a high precision of prediction of energy intensity values for industrial enterprises that were included in the sample during the synthesis of the neural network, and an acceptable error while testing on industrial enterprises from a test sample.","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133266978","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-18DOI: 10.21511/NFMTE.7.2018.01
M. Bondarenko, V. Bondarenko
The problem of calibration of local volatility model of Dupire has been formalized. It uses genetic algorithm as alternative to regularization approach with further application of gradient descent algorithm. Components that solve Dupire’s partial differential equation that represents dynamics of underlying asset’s price within Dupire model have been built. This price depends in particular on values of volatility parameters. Local volatility is parametrized in two dimensions (by Dupire model): time to maturity of the option and strike price (execution price). On maturity axis linear interpolation is used while on strike axis we use B-Splines. Genetic operators of mutation and selection are then applied to parameters of B-Splines. Resulting parameters allow us to obtain the values of local volatility both in knot points and intermediate points using interpolation techniques. Then we solve Dupire equation and calculate model values of option prices. To calculate cost function we simulate market values of option prices using classic Black-Scholes model. An experimental research to compare simulated market volatility and volatility obtained by means of calibration of Dupire model has been conducted. The goal is to estimate the precision of the approach and its usability in practice. To estimate the precision of obtained results we use a measure based on average deviation of modeled local volatility from values used to simulate market prices of the options. The research has shown that the approach to calibration using genetic algorithm of optimization requires some additional manipulations to achieve convergence. In particular it requires non-uniform discretization of the space of model parameters as well as usage of de Boor interpolation. Value 0.07 turns out to be the most efficient mutation parameter. Using this parameter leads to quicker convergence. It has been proved that the algorithm allows precise calibration of local volatility surface from option prices.
{"title":"Calibration of Dupire local volatility model using genetic algorithm of optimization","authors":"M. Bondarenko, V. Bondarenko","doi":"10.21511/NFMTE.7.2018.01","DOIUrl":"https://doi.org/10.21511/NFMTE.7.2018.01","url":null,"abstract":"The problem of calibration of local volatility model of Dupire has been formalized. It uses genetic algorithm as alternative to regularization approach with further application of gradient descent algorithm. Components that solve Dupire’s partial differential equation that represents dynamics of underlying asset’s price within Dupire model have been built. This price depends in particular on values of volatility parameters. Local volatility is parametrized in two dimensions (by Dupire model): time to maturity of the option and strike price (execution price). On maturity axis linear interpolation is used while on strike axis we use B-Splines. Genetic operators of mutation and selection are then applied to parameters of B-Splines. Resulting parameters allow us to obtain the values of local volatility both in knot points and intermediate points using interpolation techniques. Then we solve Dupire equation and calculate model values of option prices. To calculate cost function we simulate market values of option prices using classic Black-Scholes model. An experimental research to compare simulated market volatility and volatility obtained by means of calibration of Dupire model has been conducted. The goal is to estimate the precision of the approach and its usability in practice. To estimate the precision of obtained results we use a measure based on average deviation of modeled local volatility from values used to simulate market prices of the options. The research has shown that the approach to calibration using genetic algorithm of optimization requires some additional manipulations to achieve convergence. In particular it requires non-uniform discretization of the space of model parameters as well as usage of de Boor interpolation. Value 0.07 turns out to be the most efficient mutation parameter. Using this parameter leads to quicker convergence. It has been proved that the algorithm allows precise calibration of local volatility surface from option prices.","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114682069","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2018-10-18DOI: 10.21511/NFMTE.7.2018.02
Halyna Velykoivanenko, S. Savina, D. Kolechko, Vladyslav Ben'
The article is devoted to solving the actual problem of increasing the efficiency of assessing the credit risks of individual borrowers by finding the optimal combination of the results of calculations of specific scoring models. The principles of the formation of an ensemble of models are given and the existing approaches to the construction of ensemble structures are analyzed. In the process of experimental research has been applied one of the modifications of the boosting algorithm and implemented the author's algorithm for constructing an ensemble of models based on the specialization of experts. The radial-basis function neural networks were used as specific expert models. As a result of a comparative analysis of the efficiency of the used ensemble technologies it was confirmed that the algorithm for constructing an ensemble based on the specialization of experts proposed by the authors is the most adapted for the task of assessing credit risk.
{"title":"Building the ensembles of credit scoring models","authors":"Halyna Velykoivanenko, S. Savina, D. Kolechko, Vladyslav Ben'","doi":"10.21511/NFMTE.7.2018.02","DOIUrl":"https://doi.org/10.21511/NFMTE.7.2018.02","url":null,"abstract":"The article is devoted to solving the actual problem of increasing the efficiency of assessing the credit risks of individual borrowers by finding the optimal combination of the results of calculations of specific scoring models. The principles of the formation of an ensemble of models are given and the existing approaches to the construction of ensemble structures are analyzed. In the process of experimental research has been applied one of the modifications of the boosting algorithm and implemented the author's algorithm for constructing an ensemble of models based on the specialization of experts. The radial-basis function neural networks were used as specific expert models. As a result of a comparative analysis of the efficiency of the used ensemble technologies it was confirmed that the algorithm for constructing an ensemble based on the specialization of experts proposed by the authors is the most adapted for the task of assessing credit risk.","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115665966","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Neural network methods for forecasting the reliability of Ukrainian banks","authors":"O. Mints","doi":"10.33111/nfmte.2018.168","DOIUrl":"https://doi.org/10.33111/nfmte.2018.168","url":null,"abstract":"","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125338952","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Investigation of traders’ behavioral characteristics: experimental economics methods and machine learning technologies","authors":"K. Kononova, A. Dek","doi":"10.33111/nfmte.2018.148","DOIUrl":"https://doi.org/10.33111/nfmte.2018.148","url":null,"abstract":"","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127333809","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"The qualıty assessment of the development of ınformatıon economy ın the republıc of Azerbaıjan","authors":"G. Imanov, Mahmud Hajizadeh","doi":"10.33111/nfmte.2018.111","DOIUrl":"https://doi.org/10.33111/nfmte.2018.111","url":null,"abstract":"","PeriodicalId":300314,"journal":{"name":"Neuro-Fuzzy Modeling Techniques in Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2018-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128544971","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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