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

Stock market prediction using different neural network classification architectures 利用不同的神经网络分类架构进行股票市场预测

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501826

K. Schierholt, C. Dagli

引用次数: 51

Path integral Monte Carlo method and maximum entropy: a complete solution for the derivative valuation problem 路径积分蒙特卡罗方法和最大熵:导数估值问题的完整解决方案

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501833

M. Makivic

{"title":"Path integral Monte Carlo method and maximum entropy: a complete solution for the derivative valuation problem","authors":"M. Makivic","doi":"10.1109/CIFER.1996.501833","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501833","url":null,"abstract":"Summary form only given. We propose a combination of the path-integral Monte Carlo method and the maximum entropy method as a comprehensive solution for the problem of pricing of derivative securities. The path-integral Monte Carlo approach relies on the probability distribution of the complete histories of the underlying security, from the present time to the contract expiration date. In our present implementation, the Metropolis algorithm is used to sample the probability distribution of histories (paths) of the underlying security. The advantage of the path integral approach is that complete information about the derivative security, including its parameter sensitivities, is obtained in a single simulation. It is also possible to obtain results for multiple values of parameters in a single simulation. The input to the path-integral Monte Carlo method is the assumed propagator for the stochastic process of the underlying. The path integral method is flexible about the input stochastic process and it can be used for both American and European contracts. Derivative valuation can be viewed as a statistical inference procedure about the underlying stochastic process. In its simplest form it reduces to the computation of implied volatility. It is known that the implied volatility matrix may contain significant variations across strike prices and contract maturities. This implies that parametrization of the underlying process via single volatility parameter is inconsistent with market data. Instead, we formulate an approach which allows one to generate a fully consistent estimate of the complete propagator for the underlying.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"310 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":"122699383","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: 2

Robust estimation analytics for financial risk management 稳健的财务风险管理估计分析

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501844

H. G. Green, R. Martin, M. A. Pearson

引用次数: 2

Automated mathematical modelling for financial time series prediction using fuzzy logic, dynamical systems and fractal theory 利用模糊逻辑、动力系统和分形理论进行金融时间序列预测的自动数学建模

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501835

O. Castillo, P. Melin

{"title":"Automated mathematical modelling for financial time series prediction using fuzzy logic, dynamical systems and fractal theory","authors":"O. Castillo, P. Melin","doi":"10.1109/CIFER.1996.501835","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501835","url":null,"abstract":"We describe a new method for performing automated mathematical modelling for financial time series prediction using fuzzy logic techniques, dynamical systems and fractal theory. The main idea is that using fuzzy logic techniques we can simulate and automate the reasoning process of human experts in mathematical modelling for financial time series prediction. Our new method for automated modelling consists of three main parts: time series analysis, developing a set of admissible models, and selecting the \"best\" model. Our method for time series analysis consists of using the fractal dimension of a set of points as a measure of the geometrical complexity of the time series. Our method for developing a set of admissible dynamical systems models is based on the use of fuzzy logic techniques to simulate the decision process of the human experts in modelling financial problems. The selection of the \"best\" model for financial time series prediction (FTSP) is done using heuristics from the experts and statistical calculations. This new method can be implemented as a computer program and can be considered an intelligent system for automated mathematical modelling for FTSP.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"56 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":"121735411","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: 39

Solving robust optimization models in finance 求解金融中的鲁棒优化模型

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501816

J. Mulvey

引用次数: 3

Adaptive Rival Penalized Competitive Learning and Combined Linear Predictor with application to financial investment 自适应对手惩罚竞争学习与组合线性预测器在金融投资中的应用

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501838

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

引用次数: 8

Problems with Monte Carlo simulation in the pricing of contingent claims 蒙特卡罗模拟在或有债权定价中的问题

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501834

J. Molle, F. Zapatero

引用次数: 0

Fuzzy set methods for uncertainty representation in risky financial decisions 风险财务决策中不确定性表示的模糊集方法

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501824

R. Yager

引用次数: 6

Fuzzy queries for top-management succession planning 高层管理人员继任计划的模糊查询

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501847

T. Sutter, G. Mollet, Markus Schröder, R. Kruse, J. Gebhardt

引用次数: 0

Models of market behavior: bringing realistic games to market 市场行为模型:将现实主义游戏推向市场

IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1996-03-24 DOI: 10.1109/CIFER.1996.501821

S. Leven

{"title":"Models of market behavior: bringing realistic games to market","authors":"S. Leven","doi":"10.1109/CIFER.1996.501821","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501821","url":null,"abstract":"Modelling markets top-down tends to eliminate the dynamic nature of valuation. As prices constitute emergent properties of market forces and these forces emerge from anticipation and interaction of agents, only by employing games based in discursive systems theory can we detect \"systems embedded in systems\". Human decision-making has long been described as the convolving of habitual, inferential and affective processes. We have designed a series of simulations that employ neural networks to model the biological processes involved in individual and interactive decision-making. We have also designed models employing these interactions in organizational and market processes. Further, we suggest that observer effects are central to the measurement process in time-series analysis, from series and component definition to experimental design through outcome interpretation. Employing a neural network tool called Differential Filtering, we have suggested that these effects can be understood and, to some extent, vitiated. Finally, we have demonstrated the ability of the brain-emulating networks to detect context and to discover texture in data series, as a solution to problems such as data fusion and data decomposition. We discuss these models in light of modern approaches to complex systems information processing.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"50 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":"124584244","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: 1