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Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)最新文献

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A multinomial characterization of feedforward neural networks 前馈神经网络的多项式表征
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495255
B. Lehmann
{"title":"A multinomial characterization of feedforward neural networks","authors":"B. Lehmann","doi":"10.1109/CIFER.1995.495255","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495255","url":null,"abstract":"The purpose of the paper is to examine neural networks in terms of a particular probability model: a multinomial distribution characterization of the conditional mean. This characterization suggests circumstances in which networks need only provide good local approximations and a new parsimonious neural network model. The paper provides an empirical application to interest rate volatility.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"20 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121990852","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: 0
A neurofuzzy arbitrage simulator for stock investing 股票投资的神经模糊套利模拟器
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495271
A. Hobbs, N. Bourbakis
{"title":"A neurofuzzy arbitrage simulator for stock investing","authors":"A. Hobbs, N. Bourbakis","doi":"10.1109/CIFER.1995.495271","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495271","url":null,"abstract":"We study the success of a neural network computer model to predict the price, of a stock, given the fluctuations in the rest of the market that day. Based on the neural net's prediction, the program then measures its success by simulating buying or selling that stock, based on whether the market's price is determined over valued or under valued. The neural net itself is a modification of a fuzzy based neural network (Kung, 1993). The program consistently averages over 20% A.P.R. and has been time tested over 6 years with several stocks.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"52 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129198616","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: 11
Estimation of dependencies based on small number of observations 基于少量观测值的依赖性估计
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495231
V. Vapnik
{"title":"Estimation of dependencies based on small number of observations","authors":"V. Vapnik","doi":"10.1109/CIFER.1995.495231","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495231","url":null,"abstract":"Summary form only given. The main conceptual problem with estimating the dependencies from empirical data arises when the number of given observations is small. To do well in this situation, one has to use an induction principle that takes into account, along with the performance on the training set, the VC-dimension of the set of functions from which the decision function is chosen. It is therefore possible to construct methods that generalize well even in a very high dimensional input space using a small number of observations. To do the best, given a small sample size, one must only try to solve the problem one really needs to solve, rather than some more general problem. Often, however, this is not easy. For many applications (including financing) it is important to estimate the values of the function at the given points of interest, rather than to estimate the function itself. It is possible to construct algorithms for direct decision making. I describe the problem of learning to make actions, whose solution is not based on the model estimating technique. The problem of learning to make actions is a generalization of the decision making problem. It appears applicable to the many financing problems.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"105 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116015665","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
An approach to social system simulation based on information fusion 基于信息融合的社会系统仿真方法
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495256
I. Kobayashi, M. Sugeno
{"title":"An approach to social system simulation based on information fusion","authors":"I. Kobayashi, M. Sugeno","doi":"10.1109/CIFER.1995.495256","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495256","url":null,"abstract":"We focus on human information processing characterized by information fusion, and propose a new simulation method for a social system. We consider the role of custom and language in human intelligence, and apply it to information processing. We use natural language as a tool for information fusion by a computer. In this context we discuss a simulation model imitating the human thinking process. As an example, we build a model to estimate the future trends of foreign exchange rates.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133585063","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: 0
Neural networks in finance: an information analysis 金融中的神经网络:一种信息分析
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495273
R. N. Kahn, A. Basu
{"title":"Neural networks in finance: an information analysis","authors":"R. N. Kahn, A. Basu","doi":"10.1109/CIFER.1995.495273","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495273","url":null,"abstract":"We classify financial applications of neural networks into two broad classes by stability and signal-to-noise ratio. We present two statistical measures typically applied to investment analysis: the information ratio (IR) and the information coefficient (IC); then we use Monte-Carlo simulations to critically examine neural net performance as a function of signal-to-noise ratio in characteristic investment domains. We thus measure the maximum noise level tolerable by neural nets during training on a representative class of investment problems.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"156 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114542634","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
Function approximation with learning networks in the financial field and its application to the interest rate sector 金融领域的学习网络函数逼近及其在利率领域的应用
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495272
G. A. Hoffmann
{"title":"Function approximation with learning networks in the financial field and its application to the interest rate sector","authors":"G. A. Hoffmann","doi":"10.1109/CIFER.1995.495272","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495272","url":null,"abstract":"Quantitative analysis in the financial markets has traditionally been dominated by linear, parametric modeling approaches. Recent theoretical and empirical results suggest that nonlinear, nonparametric, multivariable regression techniques are more powerful tools to discover and capture nontrivial relationships between variables. In this work ways of improving models and thus forecasts are explored by adapting two different ways of specifying connectionist networks: radial basis function networks (RBF) and multilayer perceptrons (MLP). By employing these techniques we gain the potential to model complex data more effectively while at the same time we largely avoid imposing any particular and possibly incorrect model assumptions. Evolution strategy and a speeded up error backpropagation are utilized to estimate model parameters. To illustrate the application potential nonlinear models for Bund yields are estimated. For comparison benchmark models using a linear multivariable and a random walk approach are also estimated.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"2 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129899981","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
Input variable selection for neural networks: application to predicting the U.S. business cycle 神经网络的输入变量选择:在预测美国经济周期中的应用
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495263
J. Utans, J. Moody, S. Rehfuss, H. Siegelmann
{"title":"Input variable selection for neural networks: application to predicting the U.S. business cycle","authors":"J. Utans, J. Moody, S. Rehfuss, H. Siegelmann","doi":"10.1109/CIFER.1995.495263","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495263","url":null,"abstract":"Selecting a \"best subset\" of input variables is a critical issue in forecasting. This is especially true when the number of available input series is large, and an exhaustive search through all combinations of variables is computationally infeasible. Inclusion of irrelevant variables not only doesn't help prediction, but can reduce forecasting accuracy through added noise or systematic bias. We demonstrate a technique called \"sensitivity-based pruning\" (SBP) that removes irrelevant input variables from a nonlinear forecasting or regression model. The technique makes use of a saliency measure computed for each input variable and uses estimates of prediction risk for determining the number of input variables to prune. We present preliminary results of the SBP technique applied to neural network predictors of a key business cycle measure, the US Index of Industrial Production.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-04-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128358228","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: 61
Trend visualization 视觉化趋势
Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1900-01-01 DOI: 10.1109/CIFER.1995.495268
Steve W. Piche
{"title":"Trend visualization","authors":"Steve W. Piche","doi":"10.1109/CIFER.1995.495268","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495268","url":null,"abstract":"Double moving averages are commonly used to identify trends within capital markets. In this paper, a novel analysis technique is presented which is based upon plotting the returns associated with thousands of different double moving average trading rules for a time series. The resulting plots are useful for gaining an understanding of the trends contained within the time series. The analysis technique, which is referred to as trend visualization, is also useful for selecting the appropriate parameters for double moving average trading systems. In this paper, the technique is used to investigate trends within the foreign currency spot market.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1900-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131159743","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
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