IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)最新文献_第2页

Max-min optimal investing 最大最小最优投资

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501836

E. Ordentlich, T. Cover

引用次数: 0

The gene expression messy genetic algorithm for financial applications 将基因表达凌乱的遗传算法用于金融应用

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501840

H. Kargupta, K. Buescher

引用次数: 13

Modelling stock return sensitivities to economic factors with the Kalman filter and neural networks 用卡尔曼滤波和神经网络模拟股票收益对经济因素的敏感性

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501827

Y. Bentz, L. Boone, J. Connor

引用次数: 7

Building long/short portfolios using rule induction 使用规则归纳构建多头/空头投资组合

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501837

George H. John, Peter Miller

{"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}

引用次数: 9

Application of fuzzy regression models to predict exchange rates for composite currencies 应用模糊回归模型预测复合货币汇率

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501851

S. Ghoshray

{"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}

引用次数: 5

Intelligent hybrid system for data mining 数据挖掘智能混合系统

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501832

M. Hambaba

{"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}

引用次数: 9

Trading mechanisms and return volatility: empirical investigation on Shanghai Stock Exchange based on a neural network model 交易机制与收益波动:基于神经网络模型的上海证券交易所实证研究

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501850

Helen Z. H. Lai, Yiu-ming Cheung, L. Xu

引用次数: 3

The applicability of information criteria for neural network architecture selection 信息准则在神经网络结构选择中的适用性

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501855

C. Haefke, C. Helmenstein

引用次数: 1

Analyzing shocks on the interest rate structure with Kohonen map 用Kohonen图分析利率结构的冲击

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501841

M. Cottrell, E. D. Bodt, P. Grégoire

引用次数: 3

Heuristic techniques in tax structuring for multinationals 跨国公司税收结构的启发式技术

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501852

D. Fatouros, G. Salkin, Nicos Christofides

{"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}

引用次数: 3