Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501823
P. Werbos
Summary form only given, substantially as follows. Problems of portfolio management have included several fundamental time-series problems. Parts of these problems are involved with the inevitable noisiness of financial data, parts with interactions and mode-locking among measures, and parts with the basic probabilistic nature of predictive systems in a rich environment. Modern neural networks have been used, to limited effect, to resolve them. Innovative techniques should prove more helpful. Among the fundamental issues for comprehending time series data are: (1) adjusting models dynamically, as errors emerge and corrections are identified; (2) promoting model-wide adjustment; (3) avoiding the tendency of least-squares forecasts to decay with time; (4) locating the range of plausible outcomes; and (5) complex prediction/correction optimization strategies. Techniques pioneered in neural networks have addressed each of these issues. The most common algorithms employed have been backpropagation variants. Recent advances in backpropagation make possible substantial improvements in identifying seasonality, modality and structural stability. Advances in recurrent networks allow context-sensitive adjustment of sharing and "elastic fuzziness", and new forms of reinforcement learning which permit the detection of interaction among dimensions and dynamic adjustment to that interaction. Reconstruction of priors and "deconstruction" of observer effects are also consequences of elastic fuzzy networks and dual heuristic programming.
{"title":"Finding time series among the chaos: stochastics, deseasonalization, and texture-detection using neural nets","authors":"P. Werbos","doi":"10.1109/CIFER.1996.501823","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501823","url":null,"abstract":"Summary form only given, substantially as follows. Problems of portfolio management have included several fundamental time-series problems. Parts of these problems are involved with the inevitable noisiness of financial data, parts with interactions and mode-locking among measures, and parts with the basic probabilistic nature of predictive systems in a rich environment. Modern neural networks have been used, to limited effect, to resolve them. Innovative techniques should prove more helpful. Among the fundamental issues for comprehending time series data are: (1) adjusting models dynamically, as errors emerge and corrections are identified; (2) promoting model-wide adjustment; (3) avoiding the tendency of least-squares forecasts to decay with time; (4) locating the range of plausible outcomes; and (5) complex prediction/correction optimization strategies. Techniques pioneered in neural networks have addressed each of these issues. The most common algorithms employed have been backpropagation variants. Recent advances in backpropagation make possible substantial improvements in identifying seasonality, modality and structural stability. Advances in recurrent networks allow context-sensitive adjustment of sharing and \"elastic fuzziness\", and new forms of reinforcement learning which permit the detection of interaction among dimensions and dynamic adjustment to that interaction. Reconstruction of priors and \"deconstruction\" of observer effects are also consequences of elastic fuzzy networks and dual heuristic programming.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"104 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133737829","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501843
T. Kariya, H. Tsuda
Kariya and Tsuda (1995) demonstrated the predictive power of TDM (time dependent Markov) model for individual bond prices with the end-of-month price data of JG (Japanese Government) bonds with initial maturities of 10 years. The model predicted well the monthly term structure of the individual JG bond prices for the period 1991.1-1992.12 though there are only four parameters in the model, where there are about 80 bonds for each month. In fact, the prediction standard error for the period is 0.9 yen while the estimation standard error is less than 0.3 yen, where the face value of a JG bond is 100 yen. We again test the prediction power of the TDM model with the end-of-month price data of JG bonds for the period 1993.1-1995.12 when the interest rate level was low, and observe that the model loses the predictive power when interest rates change volatilly even though the overall performance is good. The observation follows from the fact that the VAR (vector autoregressive) model for predicting four time dependent parameters in the model, which is modelled based on the cross-sectionally estimated parameters, fails to keep a stable prediction power for months of volatile interest rates. It is remarked that the TDM model is proposed by Kariya and Tsuda (1994) as a time series extension of the CSM (Cross-Sectional Market) model for individual bond prices Kariya (1993) formulated.
{"title":"Prediction of individual JG bond prices via the TDM model","authors":"T. Kariya, H. Tsuda","doi":"10.1109/CIFER.1996.501843","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501843","url":null,"abstract":"Kariya and Tsuda (1995) demonstrated the predictive power of TDM (time dependent Markov) model for individual bond prices with the end-of-month price data of JG (Japanese Government) bonds with initial maturities of 10 years. The model predicted well the monthly term structure of the individual JG bond prices for the period 1991.1-1992.12 though there are only four parameters in the model, where there are about 80 bonds for each month. In fact, the prediction standard error for the period is 0.9 yen while the estimation standard error is less than 0.3 yen, where the face value of a JG bond is 100 yen. We again test the prediction power of the TDM model with the end-of-month price data of JG bonds for the period 1993.1-1995.12 when the interest rate level was low, and observe that the model loses the predictive power when interest rates change volatilly even though the overall performance is good. The observation follows from the fact that the VAR (vector autoregressive) model for predicting four time dependent parameters in the model, which is modelled based on the cross-sectionally estimated parameters, fails to keep a stable prediction power for months of volatile interest rates. It is remarked that the TDM model is proposed by Kariya and Tsuda (1994) as a time series extension of the CSM (Cross-Sectional Market) model for individual bond prices Kariya (1993) formulated.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"11 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127534690","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501818
Roil Even, B. Mishra
Describes the Complex Adaptive Financial Environment (CAFE/spl acute/), a simulator for complex adaptive systems implemented in Java. CAFE/spl acute/'s object-oriented design makes it suitable for many types of simulation. We give an example of a market simulation where food is traded for gold and explore the effects of adding several kinds of speculators to the system. This paper describes the software structure and design of CAFE/spl acute/, building upon the object-oriented and distributed features of the Java programming language. Although the primary application for this system is in the computational finance area, we envision a much more general usage.
{"title":"CAFE/spl acute/: a Complex Adaptive Financial Environment","authors":"Roil Even, B. Mishra","doi":"10.1109/CIFER.1996.501818","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501818","url":null,"abstract":"Describes the Complex Adaptive Financial Environment (CAFE/spl acute/), a simulator for complex adaptive systems implemented in Java. CAFE/spl acute/'s object-oriented design makes it suitable for many types of simulation. We give an example of a market simulation where food is traded for gold and explore the effects of adding several kinds of speculators to the system. This paper describes the software structure and design of CAFE/spl acute/, building upon the object-oriented and distributed features of the Java programming language. Although the primary application for this system is in the computational finance area, we envision a much more general usage.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"12 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128565700","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501829
T. Rubinson, R. Yager
We discuss the applicability of fuzzy logic multi criteria ranking techniques and genetic algorithms in solving problems concerning financial risk management. Fuzzy logic techniques are useful in soliciting information on user perceptions of risk factors. However, since people are notoriously inaccurate and unreliable in reporting their preferences, we also employ a genetic algorithm to help validate user supplied data. The genetic algorithm helps clarify how and when user preferences effect the perceived desirability of a particular outcome. The genetic algorithm also helps tune the parameters of fuzzy multiple criteria decision models.
{"title":"Fuzzy logic and genetic algorithms for financial risk management","authors":"T. Rubinson, R. Yager","doi":"10.1109/CIFER.1996.501829","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501829","url":null,"abstract":"We discuss the applicability of fuzzy logic multi criteria ranking techniques and genetic algorithms in solving problems concerning financial risk management. Fuzzy logic techniques are useful in soliciting information on user perceptions of risk factors. However, since people are notoriously inaccurate and unreliable in reporting their preferences, we also employ a genetic algorithm to help validate user supplied data. The genetic algorithm helps clarify how and when user preferences effect the perceived desirability of a particular outcome. The genetic algorithm also helps tune the parameters of fuzzy multiple criteria decision models.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125802955","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501854
Y. Alici
Corporate failure prediction has been used in the application of both parametric classical classification and non-parametric artificial neural network techniques. Although discriminant and logistic regression analysis have been accepted as standard pattern recognition devices, different kinds of neural network technology have recently demonstrated promising outcomes, in terms of accuracy, when compared with results from classical pattern recognition techniques. Most of the neural net studies in corporate failure prediction have centred on implementing a large variety of supervised learning algorithms. Considering stochastic properties of financial ratios due to creative accounting practices, different accounting policies and deviant patterns of so-called healthy companies, little work has been conducted in identifying different patterns of both failed and solvent firms. Therefore, the purpose of the study is to extract solvency maps of UK listed manufacturing firms, by employing self-organising maps. The results obtained from this research indicate that there is marked difference between failed and non-failed firms in terms of financial characteristics although different financial structures exist amongst both bankrupt and solvent companies.
{"title":"A corporate solvency map through self-organising neural networks","authors":"Y. Alici","doi":"10.1109/CIFER.1996.501854","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501854","url":null,"abstract":"Corporate failure prediction has been used in the application of both parametric classical classification and non-parametric artificial neural network techniques. Although discriminant and logistic regression analysis have been accepted as standard pattern recognition devices, different kinds of neural network technology have recently demonstrated promising outcomes, in terms of accuracy, when compared with results from classical pattern recognition techniques. Most of the neural net studies in corporate failure prediction have centred on implementing a large variety of supervised learning algorithms. Considering stochastic properties of financial ratios due to creative accounting practices, different accounting policies and deviant patterns of so-called healthy companies, little work has been conducted in identifying different patterns of both failed and solvent firms. Therefore, the purpose of the study is to extract solvency maps of UK listed manufacturing firms, by employing self-organising maps. The results obtained from this research indicate that there is marked difference between failed and non-failed firms in terms of financial characteristics although different financial structures exist amongst both bankrupt and solvent companies.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"43 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128160868","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501830
S. Ghoshray
Predicting foreign exchange rates and stock market have been a well researched topic in the field of financial engineering. However, most methods suffer from serious drawback due to inherent uncertainty and the data acquisition problems. In this research, we have analyzed the very nature of the time series data from a pure dynamical system point of view and explored the deterministic chaotic characteristic in it. A fuzzy reconstruction method based on fuzzy multiple regression analysis have been used to predict the foreign exchange rates with accuracy.
{"title":"Foreign exchange rate prediction by fuzzy inferencing on deterministic chaos","authors":"S. Ghoshray","doi":"10.1109/CIFER.1996.501830","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501830","url":null,"abstract":"Predicting foreign exchange rates and stock market have been a well researched topic in the field of financial engineering. However, most methods suffer from serious drawback due to inherent uncertainty and the data acquisition problems. In this research, we have analyzed the very nature of the time series data from a pure dynamical system point of view and explored the deterministic chaotic characteristic in it. A fuzzy reconstruction method based on fuzzy multiple regression analysis have been used to predict the foreign exchange rates with accuracy.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"29 7","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134092899","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501849
D. Vaccari
A new class of models is proposed for use in economic correlation and forecasting. The new model, termed the multivariable polynomial regression (MPR) model, is essentially a multiple regression model with polynomial and cross-product (interaction) terms. For example, if Y is a function of Q, R, and S, terms can be included such as QR/sup 2/S or Q/sup 3/S. MPR models can be fitted using conventional multiple regression software, although an automated program facilitates the analysis. Only terms which are statistically significant are retained in the model. MPR models are likely to be applicable to low-to-moderate dimensionality problems as are encountered in economics. If the number of independent variables is not too great, MPR models compare favorably to artificial neural network (ANN) models: MPR models can provide a better fit with fewer coefficients; as a result there is less overfitting of "memorizing" of data; the fitting procedure converges absolutely; MPR models result in a simple explicit equation for prediction or analysis; standard statistical tests can be applied to all coefficients and forecast predictions. The technique was applied to correlation of the performance of retail stores to a set of thirteen potential causative variables. An MPR model was developed which was able to explain 82% of the variation in the gross margin of the stores under study.
{"title":"Nonlinear analysis of retail performance","authors":"D. Vaccari","doi":"10.1109/CIFER.1996.501849","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501849","url":null,"abstract":"A new class of models is proposed for use in economic correlation and forecasting. The new model, termed the multivariable polynomial regression (MPR) model, is essentially a multiple regression model with polynomial and cross-product (interaction) terms. For example, if Y is a function of Q, R, and S, terms can be included such as QR/sup 2/S or Q/sup 3/S. MPR models can be fitted using conventional multiple regression software, although an automated program facilitates the analysis. Only terms which are statistically significant are retained in the model. MPR models are likely to be applicable to low-to-moderate dimensionality problems as are encountered in economics. If the number of independent variables is not too great, MPR models compare favorably to artificial neural network (ANN) models: MPR models can provide a better fit with fewer coefficients; as a result there is less overfitting of \"memorizing\" of data; the fitting procedure converges absolutely; MPR models result in a simple explicit equation for prediction or analysis; standard statistical tests can be applied to all coefficients and forecast predictions. The technique was applied to correlation of the performance of retail stores to a set of thirteen potential causative variables. An MPR model was developed which was able to explain 82% of the variation in the gross margin of the stores under study.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129221592","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501819
Thomas Ankenbrand, M. Tomassini
Presents an integrated approach for modelling the behaviour of financial markets with artificial neural networks (ANNs). The method allows the forecasting of financial time series. Its originality lies in the fact that it is based on statistics and macroeconomics principles, integrating fundamental economic knowledge in a multivariate, nonlinear time-series ANN model. The core of the work is a feasibility analysis, which is seldom attempted in ANN work, consisting of a series of different univariate and multivariate, linear and nonlinear statistical tests. The enhancement of prior work is a sensitivity analysis with bootstrap as part of the feasibility analysis. The feasibility analysis evaluates the "a priori" chance of forecasting the defined system and helps in defining the topology of the ANN. The method is applied to a real-life case study with a few data samples.
{"title":"Predicting multivariate financial time series using neural networks: the Swiss bond case","authors":"Thomas Ankenbrand, M. Tomassini","doi":"10.1109/CIFER.1996.501819","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501819","url":null,"abstract":"Presents an integrated approach for modelling the behaviour of financial markets with artificial neural networks (ANNs). The method allows the forecasting of financial time series. Its originality lies in the fact that it is based on statistics and macroeconomics principles, integrating fundamental economic knowledge in a multivariate, nonlinear time-series ANN model. The core of the work is a feasibility analysis, which is seldom attempted in ANN work, consisting of a series of different univariate and multivariate, linear and nonlinear statistical tests. The enhancement of prior work is a sensitivity analysis with bootstrap as part of the feasibility analysis. The feasibility analysis evaluates the \"a priori\" chance of forecasting the defined system and helps in defining the topology of the ANN. The method is applied to a real-life case study with a few data samples.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126502021","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501831
R. Grossman, H. Poor
An optimization tree approach to the mining of very extensive and complex databases for performance optimizing opportunities is described. This methodology is based on a combination of three innovations: a data management system designed explicitly for data intensive computing; a distributed algorithm for growing classification and regression trees (CART); and a tree based stochastic programming paradigm for the selection of control attributes to optimize a specified objective function. This methodology provides a general technique for optimization in financial applications that is scalable as the number of objects in the database and as the number of attributes per object grow. This scalability allows for a complete data driven analysis of large scale data sets, without the need to restrict attention to sparsely sampled data sets that limits previous methods.
{"title":"Optimization driven data mining and credit scoring","authors":"R. Grossman, H. Poor","doi":"10.1109/CIFER.1996.501831","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501831","url":null,"abstract":"An optimization tree approach to the mining of very extensive and complex databases for performance optimizing opportunities is described. This methodology is based on a combination of three innovations: a data management system designed explicitly for data intensive computing; a distributed algorithm for growing classification and regression trees (CART); and a tree based stochastic programming paradigm for the selection of control attributes to optimize a specified objective function. This methodology provides a general technique for optimization in financial applications that is scalable as the number of objects in the database and as the number of attributes per object grow. This scalability allows for a complete data driven analysis of large scale data sets, without the need to restrict attention to sparsely sampled data sets that limits previous methods.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131771000","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 : 1996-03-24DOI: 10.1109/CIFER.1996.501828
S. Schwarze, Matthias Lechner, Michael Jensen
In recent years, service companies have received similar problems to industrial companies. An example is the process of credit allocation in a bank. This process is not supported sufficiently through information systems. Thus, the "lead time" for credit allocation is too long which causes high costs. An approach to improving the process of credit allocation is described. The focus lies on tightening the range of individual credit types with the help of standardized treatment and on the management of vague knowledge in the process of credit allocation. Solutions which have been developed in a project with a German bank are suggested for both aspects.
{"title":"Computer supported determination of bank credit conditions","authors":"S. Schwarze, Matthias Lechner, Michael Jensen","doi":"10.1109/CIFER.1996.501828","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501828","url":null,"abstract":"In recent years, service companies have received similar problems to industrial companies. An example is the process of credit allocation in a bank. This process is not supported sufficiently through information systems. Thus, the \"lead time\" for credit allocation is too long which causes high costs. An approach to improving the process of credit allocation is described. The focus lies on tightening the range of individual credit types with the help of standardized treatment and on the management of vague knowledge in the process of credit allocation. Solutions which have been developed in a project with a German bank are suggested for both aspects.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123267826","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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