Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501840
H. Kargupta, K. Buescher
The paper introduces the gene expression messy genetic algorithm (GEMGA)-a new generation of messy GAs that may find many applications in financial engineering. Unlike other existing blackbox optimization algorithms, GEMGA directly searches for relations among the members of the search space. The GEMGA is an O(|/spl Lambda/|/sup k/(l+k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length L and alphabet set /spl Lambda/. The GEMGA is designed based on the alternate perspective of natural evolution proposed by the SEARCH framework (Kargupta, 1995) that emphasizes the role of gene expression. The paper also presents the test results for large multimodal problems and identifies possible applications to financial engineering.
{"title":"The gene expression messy genetic algorithm for financial applications","authors":"H. Kargupta, K. Buescher","doi":"10.1109/CIFER.1996.501840","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501840","url":null,"abstract":"The paper introduces the gene expression messy genetic algorithm (GEMGA)-a new generation of messy GAs that may find many applications in financial engineering. Unlike other existing blackbox optimization algorithms, GEMGA directly searches for relations among the members of the search space. The GEMGA is an O(|/spl Lambda/|/sup k/(l+k)) sample complexity algorithm for the class of order-k delineable problems (Kargupta, 1995) (problems that can be solved by considering no higher than order-k relations) in sequence representation of length L and alphabet set /spl Lambda/. The GEMGA is designed based on the alternate perspective of natural evolution proposed by the SEARCH framework (Kargupta, 1995) that emphasizes the role of gene expression. The paper also presents the test results for large multimodal problems and identifies possible applications to financial engineering.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"26 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":"116291332","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.1996.501827
Y. Bentz, L. Boone, J. Connor
Sensitivity analysis of asset returns to various economic variables provides investors with a useful tool to build portfolios and manage their risk. However, there are strong reasons to believe that stock exposures evolve through time and that factor models involving them are only pertinent if they use reliable estimates of future sensitivities. Both Kalman filtering and neural networks may be used to provide such estimates. While the Kalman filter is good at modelling the time structure of sensitivities, neural networks are capable of relating them to exogeneous variables in a non linear way. Furthermore, because the two approaches perform complementary tasks of sensitivity forecasting, they may be combined to achieve better performances. These procedures are evaluated in a controlled simulation experiment and in a real stock exposure analysis. Stock sensitivities to interest and exchange rates are forecasted for 90 French shares and portfolios are built accordingly.
{"title":"Modelling stock return sensitivities to economic factors with the Kalman filter and neural networks","authors":"Y. Bentz, L. Boone, J. Connor","doi":"10.1109/CIFER.1996.501827","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501827","url":null,"abstract":"Sensitivity analysis of asset returns to various economic variables provides investors with a useful tool to build portfolios and manage their risk. However, there are strong reasons to believe that stock exposures evolve through time and that factor models involving them are only pertinent if they use reliable estimates of future sensitivities. Both Kalman filtering and neural networks may be used to provide such estimates. While the Kalman filter is good at modelling the time structure of sensitivities, neural networks are capable of relating them to exogeneous variables in a non linear way. Furthermore, because the two approaches perform complementary tasks of sensitivity forecasting, they may be combined to achieve better performances. These procedures are evaluated in a controlled simulation experiment and in a real stock exposure analysis. Stock sensitivities to interest and exchange rates are forecasted for 90 French shares and portfolios are built accordingly.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"10 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":"124103452","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.1996.501836
E. Ordentlich, T. Cover
We solve the problem of tracking the best constant rebalanced portfolio computed in hindsight in a max-min optimal sense and relate our results to the pricing of a new derivative security which might be called the hindsight allocation option. This option pays the return of one dollar invested in the best constant rebalanced portfolio computed in hindsight.
{"title":"Max-min optimal investing","authors":"E. Ordentlich, T. Cover","doi":"10.1109/CIFER.1996.501836","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501836","url":null,"abstract":"We solve the problem of tracking the best constant rebalanced portfolio computed in hindsight in a max-min optimal sense and relate our results to the pricing of a new derivative security which might be called the hindsight allocation option. This option pays the return of one dollar invested in the best constant rebalanced portfolio computed in hindsight.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"15 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":"132059064","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.1996.501837
George H. John, Peter Miller
We approach stock selection for long/short portfolios from the perspective of knowledge discovery in databases and rule induction: given a database of historical information on some universe of stocks, discover rules from the data that will allow one to predict which stocks are likely to have exceptionally high or low returns in the future. Long/short portfolios allow a fund manager to independently address value-added stock selection and factor exposure, and are a popular tool in financial engineering. For stock selection we employed the Recon system, which is able to induce a set of rules to model the data it is given. We evaluate Recon's stock selection performance by using it to build equitized long/short portfolios over eighteen quarters of historical data from October 1988 to March 1993, repeatedly using the previous four quarters of data to build a model which is then used to rank stocks in the current quarter. When trading costs were taken into account, Recon's equitized long/short portfolio had a total return of 277%, significantly outperforming the benchmark (S&P500), which returned 92.5% over the same period. We conclude that rule induction is a valuable tool for stock selection.
{"title":"Building long/short portfolios using rule induction","authors":"George H. John, Peter Miller","doi":"10.1109/CIFER.1996.501837","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501837","url":null,"abstract":"We approach stock selection for long/short portfolios from the perspective of knowledge discovery in databases and rule induction: given a database of historical information on some universe of stocks, discover rules from the data that will allow one to predict which stocks are likely to have exceptionally high or low returns in the future. Long/short portfolios allow a fund manager to independently address value-added stock selection and factor exposure, and are a popular tool in financial engineering. For stock selection we employed the Recon system, which is able to induce a set of rules to model the data it is given. We evaluate Recon's stock selection performance by using it to build equitized long/short portfolios over eighteen quarters of historical data from October 1988 to March 1993, repeatedly using the previous four quarters of data to build a model which is then used to rank stocks in the current quarter. When trading costs were taken into account, Recon's equitized long/short portfolio had a total return of 277%, significantly outperforming the benchmark (S&P500), which returned 92.5% over the same period. We conclude that rule induction is a valuable tool for stock selection.","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":"122421045","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.1996.501851
S. Ghoshray
This research proposes a new regression analysis model based on fuzzy statistics. With the increase in number of variables that interact in a complex economic environment, the accumulation of perfect knowledge for the purpose of prediction has become increasingly unrealistic. Thus, predicting future exchange rates in a composite currency situation has become increasingly difficult. Our research is an effort in that direction in which we try to predict certain key parameters based on the imperfect and uncertain information obtained from the related economic variables. In this regard, the theoretical foundation of fuzzy regression analysis has been extended. Here we utilize the fact that the relationship between the dependent variable and the independent variables is not sharply defined as in the non-fuzzy linear regression analysis. The most important assumption for this work is that the deviations between the estimated values and the corresponding real values of the output variables lie in the imprecision or the ambiguity in the system parameters. The significant contribution of this research is in its efficient modeling of fuzzy prediction analysis system which can be implemented in an uncertain economic environment such as chaotic fluctuations of composite currency.
{"title":"Application of fuzzy regression models to predict exchange rates for composite currencies","authors":"S. Ghoshray","doi":"10.1109/CIFER.1996.501851","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501851","url":null,"abstract":"This research proposes a new regression analysis model based on fuzzy statistics. With the increase in number of variables that interact in a complex economic environment, the accumulation of perfect knowledge for the purpose of prediction has become increasingly unrealistic. Thus, predicting future exchange rates in a composite currency situation has become increasingly difficult. Our research is an effort in that direction in which we try to predict certain key parameters based on the imperfect and uncertain information obtained from the related economic variables. In this regard, the theoretical foundation of fuzzy regression analysis has been extended. Here we utilize the fact that the relationship between the dependent variable and the independent variables is not sharply defined as in the non-fuzzy linear regression analysis. The most important assumption for this work is that the deviations between the estimated values and the corresponding real values of the output variables lie in the imprecision or the ambiguity in the system parameters. The significant contribution of this research is in its efficient modeling of fuzzy prediction analysis system which can be implemented in an uncertain economic environment such as chaotic fluctuations of composite currency.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"27 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":"123072830","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.1996.501832
M. Hambaba
Summary form only given. Database mining is the process of finding patterns and relations in large database. A number of database mining techniques have been developed in domains that range from space and ocean exploration to financial and business analysis. The models generated from using data mining processes are statistical (e.g., linear regression, and nonlinear regression), symbolic (e.g., decision tree, CART, ID3), fuzzy symbolic (fuzzy logic systems), neural (feedforward neural network, recurrent neural networks, and self-organizing memory SOM), and genetic (genetic algorithm based on the biological survival of the fittest). Some scientists are trying to introduce chaos theory and fractal statistics for better data mining. It is the conflict between the symmetry of the Euclidean geometry and the asymmetry of the real randomness and determinism, chaos and order coexist. While these intelligent techniques have produced encouraging results in particular tasks, certain complex problems cannot be solved by a single intelligent technique alone. Each intelligent technique has particular computational properties that make them suited for particular problems. These limitations have been a central driving force behind the creation of intelligent hybrid systems. For example, the combination of neural network and fuzzy logic systems has been applied successfully in loan evaluation, fraud detection, financial risk assessment, financial decision making, and credit card application evaluation. We present a novel hybrid system for data mining in financial analysis.
{"title":"Intelligent hybrid system for data mining","authors":"M. Hambaba","doi":"10.1109/CIFER.1996.501832","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501832","url":null,"abstract":"Summary form only given. Database mining is the process of finding patterns and relations in large database. A number of database mining techniques have been developed in domains that range from space and ocean exploration to financial and business analysis. The models generated from using data mining processes are statistical (e.g., linear regression, and nonlinear regression), symbolic (e.g., decision tree, CART, ID3), fuzzy symbolic (fuzzy logic systems), neural (feedforward neural network, recurrent neural networks, and self-organizing memory SOM), and genetic (genetic algorithm based on the biological survival of the fittest). Some scientists are trying to introduce chaos theory and fractal statistics for better data mining. It is the conflict between the symmetry of the Euclidean geometry and the asymmetry of the real randomness and determinism, chaos and order coexist. While these intelligent techniques have produced encouraging results in particular tasks, certain complex problems cannot be solved by a single intelligent technique alone. Each intelligent technique has particular computational properties that make them suited for particular problems. These limitations have been a central driving force behind the creation of intelligent hybrid systems. For example, the combination of neural network and fuzzy logic systems has been applied successfully in loan evaluation, fraud detection, financial risk assessment, financial decision making, and credit card application evaluation. We present a novel hybrid system for data mining in financial analysis.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"25 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":"123917802","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.1996.501850
Helen Z. H. Lai, Yiu-ming Cheung, L. Xu
We empirically compare the behavior of open-to-open and close-to-close returns on the Shanghai Stock Exchange (SHSE) with different trading mechanisms (call market at the opening in the morning followed by continuous market). We use non-linear regression based on a neural network to study the volatility and efficiency of SHSE. The experimental results have shown that the volatility of the call market is significantly higher than that of the continuous market and the call market is more efficient than the continuous market.
{"title":"Trading mechanisms and return volatility: empirical investigation on Shanghai Stock Exchange based on a neural network model","authors":"Helen Z. H. Lai, Yiu-ming Cheung, L. Xu","doi":"10.1109/CIFER.1996.501850","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501850","url":null,"abstract":"We empirically compare the behavior of open-to-open and close-to-close returns on the Shanghai Stock Exchange (SHSE) with different trading mechanisms (call market at the opening in the morning followed by continuous market). We use non-linear regression based on a neural network to study the volatility and efficiency of SHSE. The experimental results have shown that the volatility of the call market is significantly higher than that of the continuous market and the call market is more efficient than the continuous market.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"5 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":"124585683","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.1996.501855
C. Haefke, C. Helmenstein
In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment of the Vienna stock exchange might be less efficient than the whole market and hence easier to forecast. Extending the focus of investigation in the paper, we use feedforward networks and linear models to predict the all share index WBI as well as various subindexes covering the highly liquid, semi-liquid, and initial public offering (IPO) market segment. In order to shed some light on network construction principles, we compare different models as selected by hold-out cross-validation (HCV), Akaike's (1974) information criterion (AIC), and Schwartz' (1978) information criterion (SIC). The forecasts are subsequently evaluated on the basis of hypothetical trading in the out-of-sample period.
{"title":"The applicability of information criteria for neural network architecture selection","authors":"C. Haefke, C. Helmenstein","doi":"10.1109/CIFER.1996.501855","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501855","url":null,"abstract":"In most of the empirical research on capital markets, stock market indexes are used as proxies for the aggregate market development. In previous work we found that a particular market segment of the Vienna stock exchange might be less efficient than the whole market and hence easier to forecast. Extending the focus of investigation in the paper, we use feedforward networks and linear models to predict the all share index WBI as well as various subindexes covering the highly liquid, semi-liquid, and initial public offering (IPO) market segment. In order to shed some light on network construction principles, we compare different models as selected by hold-out cross-validation (HCV), Akaike's (1974) information criterion (AIC), and Schwartz' (1978) information criterion (SIC). The forecasts are subsequently evaluated on the basis of hypothetical trading in the out-of-sample period.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"3 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":"117032768","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.1996.501841
M. Cottrell, E. D. Bodt, P. Grégoire
The goal of the paper is to classify the observed shocks on the interest rate term structure and to verify that these classes of shocks are compatible with the theoretical shocks predicted by the general equilibrium models and, consequently, respect the no-arbitrage condition. To classify the observed shocks on the interest rate structure, we use data of the US bonds market. Our data are daily interest rate structures for maturity from 1 to 15 years.
{"title":"Analyzing shocks on the interest rate structure with Kohonen map","authors":"M. Cottrell, E. D. Bodt, P. Grégoire","doi":"10.1109/CIFER.1996.501841","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501841","url":null,"abstract":"The goal of the paper is to classify the observed shocks on the interest rate term structure and to verify that these classes of shocks are compatible with the theoretical shocks predicted by the general equilibrium models and, consequently, respect the no-arbitrage condition. To classify the observed shocks on the interest rate structure, we use data of the US bonds market. Our data are daily interest rate structures for maturity from 1 to 15 years.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"2 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":"131599089","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.1996.501852
D. Fatouros, G. Salkin, Nicos Christofides
We deal with the problem of international tax planning for multinational corporations. We seek to minimise the overall tax burden of the group, subject to meeting the group's financing requirements, by appropriately structuring the holdings of the group and financing the subsidiaries through loans. We consider a multinational company with profits generated in a number of countries through wholly-owned subsidiaries. These profits are to be repatriated as dividend flows after interest payments have been made. We consider the problem of designing a corporate structure for such a company so that the net amount repatriated is as large as possible. We consider the problem of the source of funding and method of funding, under thin capitalisation rules, taking into account different tax systems and methods of computation of tax credit. Due to complexity, we use heuristic techniques to provide near-optimal solutions for corporate structuring. We provide examples of simulated annealing, genetic algorithms and bionomic algorithm applications to this problem. We define the structure of the neighbourhoods for the local search methods and we present crossover operators for the genetic approach.
{"title":"Heuristic techniques in tax structuring for multinationals","authors":"D. Fatouros, G. Salkin, Nicos Christofides","doi":"10.1109/CIFER.1996.501852","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501852","url":null,"abstract":"We deal with the problem of international tax planning for multinational corporations. We seek to minimise the overall tax burden of the group, subject to meeting the group's financing requirements, by appropriately structuring the holdings of the group and financing the subsidiaries through loans. We consider a multinational company with profits generated in a number of countries through wholly-owned subsidiaries. These profits are to be repatriated as dividend flows after interest payments have been made. We consider the problem of designing a corporate structure for such a company so that the net amount repatriated is as large as possible. We consider the problem of the source of funding and method of funding, under thin capitalisation rules, taking into account different tax systems and methods of computation of tax credit. Due to complexity, we use heuristic techniques to provide near-optimal solutions for corporate structuring. We provide examples of simulated annealing, genetic algorithms and bionomic algorithm applications to this problem. We define the structure of the neighbourhoods for the local search methods and we present crossover operators for the genetic approach.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"25 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":"133757573","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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