Pub Date : 2000-03-28DOI: 10.1109/CIFER.2000.844600
R. D'Vari, J. C. Sosa, K. Yalamanchili
We have previously developed a fixed-income sector optimization methodology to facilitate tradeoffs between various sectors based on their contribution to the total portfolio return and risk. We maximize portfolio return subject to constraints including value-at-risk (VaR) and other downside risk measures, both absolute and relative to a benchmark (market and liability-based). Our method optimizes interest rate, curve, credit, and volatility exposures to achieve the highest expected return (view-oriented, historically based, or quantitatively forecast) within the allowed risk space defined by various specified risk constraints. This work advances the state-of-the-art in the risk-controlled optimization process for cases where there are a large number of subsector decision variables. These advances include: 1) introduction of a multi-level optimization process to avoid ill-conditioned joint risk characterization of a large number of subsectors, and to reduce required length of time histories, 2) refinement of our previous VaR and CVaR methodologies to add opportunistic nondollar bonds as well as high yield and emerging markets, and 3) ability to control risk at subsector levels as well as the total portfolio.
{"title":"Multi-level risk-controlled sector optimization of domestic and international fixed-income portfolios including conditional VaR","authors":"R. D'Vari, J. C. Sosa, K. Yalamanchili","doi":"10.1109/CIFER.2000.844600","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844600","url":null,"abstract":"We have previously developed a fixed-income sector optimization methodology to facilitate tradeoffs between various sectors based on their contribution to the total portfolio return and risk. We maximize portfolio return subject to constraints including value-at-risk (VaR) and other downside risk measures, both absolute and relative to a benchmark (market and liability-based). Our method optimizes interest rate, curve, credit, and volatility exposures to achieve the highest expected return (view-oriented, historically based, or quantitatively forecast) within the allowed risk space defined by various specified risk constraints. This work advances the state-of-the-art in the risk-controlled optimization process for cases where there are a large number of subsector decision variables. These advances include: 1) introduction of a multi-level optimization process to avoid ill-conditioned joint risk characterization of a large number of subsectors, and to reduce required length of time histories, 2) refinement of our previous VaR and CVaR methodologies to add opportunistic nondollar bonds as well as high yield and emerging markets, and 3) ability to control risk at subsector levels as well as the total portfolio.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128326213","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 : 2000-03-28DOI: 10.1109/CIFER.2000.844598
S. Uryasev
This article has outlined a new approach for the simultaneous calculation of value-at-risk (VaR) and optimization of conditional VaR (CVaR) for a broad class of problems. We have shown that CVaR can be efficiently minimized using LP techniques. Our numerical experiments show that CVaR optimal portfolios are near optimal in VaR terms, i.e., VaR cannot be reduced further more than a few percent. Also, CVaR constraints can be handled efficiently using equivalent linear constraints, which dramatically improves the efficiency of the optimization techniques.
{"title":"Conditional value-at-risk: optimization algorithms and applications","authors":"S. Uryasev","doi":"10.1109/CIFER.2000.844598","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844598","url":null,"abstract":"This article has outlined a new approach for the simultaneous calculation of value-at-risk (VaR) and optimization of conditional VaR (CVaR) for a broad class of problems. We have shown that CVaR can be efficiently minimized using LP techniques. Our numerical experiments show that CVaR optimal portfolios are near optimal in VaR terms, i.e., VaR cannot be reduced further more than a few percent. Also, CVaR constraints can be handled efficiently using equivalent linear constraints, which dramatically improves the efficiency of the optimization techniques.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124342634","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 : 2000-03-28DOI: 10.1109/CIFER.2000.844590
R. Simutis
The goal of this study was to build and to evaluate a human skill based fuzzy expert system for decision making support in a stock trading process. Our focus was concentrated on computer software that is capable to reproduce the knowledge from the skilled stock trader. Using classical technique and soft computing methods the expert system STRASS (Stock Trading Support System) was developed. The proposed system was tested for the historical collection of NASDAQ, NYSE and AMAX stock records. At present, it is being tested by "KOLEGU" mutual fund in a real stock trading process.
{"title":"Fuzzy logic based stock trading system","authors":"R. Simutis","doi":"10.1109/CIFER.2000.844590","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844590","url":null,"abstract":"The goal of this study was to build and to evaluate a human skill based fuzzy expert system for decision making support in a stock trading process. Our focus was concentrated on computer software that is capable to reproduce the knowledge from the skilled stock trader. Using classical technique and soft computing methods the expert system STRASS (Stock Trading Support System) was developed. The proposed system was tested for the historical collection of NASDAQ, NYSE and AMAX stock records. At present, it is being tested by \"KOLEGU\" mutual fund in a real stock trading process.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"124 1 Pt 2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130922720","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 : 2000-03-28DOI: 10.1109/CIFER.2000.844619
Bouchra Bouqata, A. Bensaid, R. Palliam, A. Gómez-Skarmeta
Every organization needs adequate forecasts for planning the future. The accuracy of forecasts is influenced by both the quality of past data and the method selected to forecast the future. In this paper, we carry out a comparative study between the time series forecasts from (1) the Quick-prop neural network, (2) a fuzzy neural network (adaptive-network-based fuzzy inference system (ANFIS)), (3) a fuzzy regression and identification decision tree (ADRI), and (4) traditional time series methods (ARIMA models). We augment ANFIS by using fuzzy curves to identify the input variables that have the most influence on the output. This method identifies the significant input variables that lead to a considerable decrease in training time for ANFIS, while keeping the performance at least as good. We test the performance of ANFIS with the fuzzy curve pruning technique on empirical time series data (the national private consumption) from the Spanish economy. ANFIS produced the best performance on forecasting the empirical time series data compared to ADRI and ARIMA.
{"title":"Time series prediction using crisp and fuzzy neural networks: a comparative study","authors":"Bouchra Bouqata, A. Bensaid, R. Palliam, A. Gómez-Skarmeta","doi":"10.1109/CIFER.2000.844619","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844619","url":null,"abstract":"Every organization needs adequate forecasts for planning the future. The accuracy of forecasts is influenced by both the quality of past data and the method selected to forecast the future. In this paper, we carry out a comparative study between the time series forecasts from (1) the Quick-prop neural network, (2) a fuzzy neural network (adaptive-network-based fuzzy inference system (ANFIS)), (3) a fuzzy regression and identification decision tree (ADRI), and (4) traditional time series methods (ARIMA models). We augment ANFIS by using fuzzy curves to identify the input variables that have the most influence on the output. This method identifies the significant input variables that lead to a considerable decrease in training time for ANFIS, while keeping the performance at least as good. We test the performance of ANFIS with the fuzzy curve pruning technique on empirical time series data (the national private consumption) from the Spanish economy. ANFIS produced the best performance on forecasting the empirical time series data compared to ADRI and ARIMA.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"293 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132828147","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 : 2000-03-28DOI: 10.1109/CIFER.2000.844586
C. Schittenkopf, P. Tiňo, G. Dorffner
There are two notions of volatility in literature: historical volatility and implied volatility. We concentrate on the latter by analyzing the profitability of a pure volatility trading strategy which is delta-neutral and independent of an option pricing model, for the German stock index DAX. Several very different methods ranging from linear and nonlinear, real-valued models to symbolic models of volatility changes are applied to predict the change in volatility to the next trading day and to gain profits by buying or selling straddles accordingly. The trading performance is evaluated for one historical and one implied volatility measure. The results are carefully evaluated concerning transaction costs, stationarity issues, and statistical significance. The main contribution of the paper is that, for the first time, the trading performance of models based on different modelling paradigms is compared.
{"title":"The profitability of trading volatility using real-valued and symbolic models","authors":"C. Schittenkopf, P. Tiňo, G. Dorffner","doi":"10.1109/CIFER.2000.844586","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844586","url":null,"abstract":"There are two notions of volatility in literature: historical volatility and implied volatility. We concentrate on the latter by analyzing the profitability of a pure volatility trading strategy which is delta-neutral and independent of an option pricing model, for the German stock index DAX. Several very different methods ranging from linear and nonlinear, real-valued models to symbolic models of volatility changes are applied to predict the change in volatility to the next trading day and to gain profits by buying or selling straddles accordingly. The trading performance is evaluated for one historical and one implied volatility measure. The results are carefully evaluated concerning transaction costs, stationarity issues, and statistical significance. The main contribution of the paper is that, for the first time, the trading performance of models based on different modelling paradigms is compared.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130751264","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 : 2000-03-26DOI: 10.1109/CIFER.2000.844608
O. Castillo, P. Melin
We describe the application of a new method for automated simulation of nonlinear dynamical systems, using a fuzzy-genetic approach, to the problem of simulating companies as they compete for the market of their products. A particular company can be viewed as a dynamical system evolving in time and also competing with similar companies for the market of their products. Also, within an international trade agreement there are also competing companies from foreign countries, which complicates the problem even more. We can apply our new method for automated simulation (O. Castillo and P. Melin, 1998) to simulate the evolution of a company or a group of companies as they compete for a fixed market. As a result of these simulations, we can formulate specific mathematical conditions for a specific country to go bankrupt or specific conditions for another company to have success.
{"title":"Intelligent simulation and forecasting of competing dynamic companies with a fuzzy-genetic approach","authors":"O. Castillo, P. Melin","doi":"10.1109/CIFER.2000.844608","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844608","url":null,"abstract":"We describe the application of a new method for automated simulation of nonlinear dynamical systems, using a fuzzy-genetic approach, to the problem of simulating companies as they compete for the market of their products. A particular company can be viewed as a dynamical system evolving in time and also competing with similar companies for the market of their products. Also, within an international trade agreement there are also competing companies from foreign countries, which complicates the problem even more. We can apply our new method for automated simulation (O. Castillo and P. Melin, 1998) to simulate the evolution of a company or a group of companies as they compete for a fixed market. As a result of these simulations, we can formulate specific mathematical conditions for a specific country to go bankrupt or specific conditions for another company to have success.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"36 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122888798","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 : 2000-03-26DOI: 10.1109/CIFER.2000.844607
Germán G. Creamer, T. Noe, P. Spindt
The article compares the financial performance, value-at-risk, and efficiency of the financial sector in major Latin American countries ([MERCOSUR, Argentina, Brazil, and Chile] and Andean region [Venezuela, Colombia, Ecuador, Peru and Bolivia]). The results of this research are also compared with US and international standards (the Basle Committee). A multivariate regression analysis will determine the factors that explain or help to predict the potential success associated with the levels of efficiency or risk of commercial banks, or at the other extreme, the potential bankruptcy associated with high levels of inefficiency and risk.
{"title":"Efficiency, performance and value-at-risk of Latin American banks in a process of economic integration","authors":"Germán G. Creamer, T. Noe, P. Spindt","doi":"10.1109/CIFER.2000.844607","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844607","url":null,"abstract":"The article compares the financial performance, value-at-risk, and efficiency of the financial sector in major Latin American countries ([MERCOSUR, Argentina, Brazil, and Chile] and Andean region [Venezuela, Colombia, Ecuador, Peru and Bolivia]). The results of this research are also compared with US and international standards (the Basle Committee). A multivariate regression analysis will determine the factors that explain or help to predict the potential success associated with the levels of efficiency or risk of commercial banks, or at the other extreme, the potential bankruptcy associated with high levels of inefficiency and risk.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130592375","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 : 2000-03-26DOI: 10.1109/CIFER.2000.844617
Y. Kawasaki, Seisho Sato, S. Tachiki
We consider a simple application of a Kalman filter to the OLS (cross-sectional regression) framework that produces almost the same result as the OLS estimates without smoothing. That is, simply introducing smoothness priors is not effective for obtaining smooth factor payoffs enough to be used in prediction. After showing that this comes from inadequate modeling of the covariance matrix R/sub t/, we introduce a GLS type specification. Secondly, even if an appropriate GLS type formulation for R/sub t/ is given, application of the Kalman filter sometimes encounters a huge computational burden, because, as is often the case, the number of stocks in a model (N, the dimension of observation vector) is much larger than that of explaining factors (K, the dimension of coefficient vector).
{"title":"Vector-valued multiple regression model with time varying coefficients and its application to predict excess stock returns","authors":"Y. Kawasaki, Seisho Sato, S. Tachiki","doi":"10.1109/CIFER.2000.844617","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844617","url":null,"abstract":"We consider a simple application of a Kalman filter to the OLS (cross-sectional regression) framework that produces almost the same result as the OLS estimates without smoothing. That is, simply introducing smoothness priors is not effective for obtaining smooth factor payoffs enough to be used in prediction. After showing that this comes from inadequate modeling of the covariance matrix R/sub t/, we introduce a GLS type specification. Secondly, even if an appropriate GLS type formulation for R/sub t/ is given, application of the Kalman filter sometimes encounters a huge computational burden, because, as is often the case, the number of stocks in a model (N, the dimension of observation vector) is much larger than that of explaining factors (K, the dimension of coefficient vector).","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114421486","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 : 2000-03-26DOI: 10.1109/CIFER.2000.844621
Fernando Niño, G. Hernández, A. Parra
The paper shows how to model times series by using random iterated neural networks with place-dependent probabilities. The model assumes that the time series comes from a dynamical system which has a compact global attractor and a physical probability measure supported on the attractor. Also, an evolutionary algorithm is used to train a random iterated neural network that models a financial time series.
{"title":"Financial time series modeling with evolutionary trained random iterated neural networks","authors":"Fernando Niño, G. Hernández, A. Parra","doi":"10.1109/CIFER.2000.844621","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844621","url":null,"abstract":"The paper shows how to model times series by using random iterated neural networks with place-dependent probabilities. The model assumes that the time series comes from a dynamical system which has a compact global attractor and a physical probability measure supported on the attractor. Also, an evolutionary algorithm is used to train a random iterated neural network that models a financial time series.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"173 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116331444","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 : 2000-03-26DOI: 10.1109/CIFER.2000.844606
M. Nasir, R. John, S. Bennett, D.M. Russell
The paper reports on the use of modular neural networks for predicting corporate bankruptcy. We obtained our financial, as well as, political and economic data from The London Stock Exchange, JORDANS financial database of major British public and private companies, and the Bank of England. In the past, various statistical techniques, such as univariate and multivariate discriminant analysis have been used in the modelling of corporate bankruptcy prediction. We use domain expert knowledge to select, and organise data in the modular neural network architecture constructed for this study. There are three sub-networks representing the periods, 1994, 1995, and 1996. Each sub-network is made of five adjacent networks representing the Balance Sheet network, the Profit and Loss network, the Financial Summary network, the Key Financial Ratios network, and the Economic and Political factors network. These adjacent networks although coupled but not linked at the input level represent five facets of failure in predicting corporate bankruptcy. The training sets represent data for 2500 companies selected randomly from a population of 270000 sample. The trained neural network will access 435000 data records before making a prediction for the particular company. The results obtained shows that neural networks outperform statistical techniques in modelling corporate failure prediction.
{"title":"Predicting corporate bankruptcy using modular neural networks","authors":"M. Nasir, R. John, S. Bennett, D.M. Russell","doi":"10.1109/CIFER.2000.844606","DOIUrl":"https://doi.org/10.1109/CIFER.2000.844606","url":null,"abstract":"The paper reports on the use of modular neural networks for predicting corporate bankruptcy. We obtained our financial, as well as, political and economic data from The London Stock Exchange, JORDANS financial database of major British public and private companies, and the Bank of England. In the past, various statistical techniques, such as univariate and multivariate discriminant analysis have been used in the modelling of corporate bankruptcy prediction. We use domain expert knowledge to select, and organise data in the modular neural network architecture constructed for this study. There are three sub-networks representing the periods, 1994, 1995, and 1996. Each sub-network is made of five adjacent networks representing the Balance Sheet network, the Profit and Loss network, the Financial Summary network, the Key Financial Ratios network, and the Economic and Political factors network. These adjacent networks although coupled but not linked at the input level represent five facets of failure in predicting corporate bankruptcy. The training sets represent data for 2500 companies selected randomly from a population of 270000 sample. The trained neural network will access 435000 data records before making a prediction for the particular company. The results obtained shows that neural networks outperform statistical techniques in modelling corporate failure prediction.","PeriodicalId":308591,"journal":{"name":"Proceedings of the IEEE/IAFE/INFORMS 2000 Conference on Computational Intelligence for Financial Engineering (CIFEr) (Cat. No.00TH8520)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130150355","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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