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

Nonparametric estimation of state-price densities implicit in financial asset prices 隐含在金融资产价格中的国家价格密度的非参数估计

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-11-01 DOI: 10.1109/CIFER.1995.495227

Yacine Aït-Sahalia, A. Lo

引用次数: 739

Price behavior and Hurst exponents of tick-by-tick interbank foreign exchange rates 银行间外汇汇率变动的价格行为和赫斯特指数

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495228

J. Moody, Lizhong Wu

引用次数: 9

Robust neural networks 鲁棒神经网络

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495226

R. Martin

{"title":"Robust neural networks","authors":"R. Martin","doi":"10.1109/CIFER.1995.495226","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495226","url":null,"abstract":"Neural networks are being increasingly used for modeling, analysis and prediction of financial data, particularly financial time series. Whatever the network architecture, the method for fitting a regression or prediction type network is almost always the method of least squares (LS), i.e., the minimization of the sum of squared errors (or prediction residuals). Unfortunately, the LS method is not robust: the estimated model can be highly effected by outliers of various kinds. In the financial time series context, the outliers might occur in isolation or in short patches. In the time series context, level shifts also cause havoc with LS fitting of neural networks. Contrary to some popular impressions, use of a neural network is not a cure-all for dealing with outliers and level shifts. We provide an introduction to statistical notions of robustness, and demonstrate the non-robustness of LS fitting of neural networks with some concrete examples where the neural network fitting is exceedingly bad due to the presence of outliers or level shifts. Then we discuss how to robustify the fitting of neural networks in both regression and time series prediction contexts. The robust methods are illustrated with several examples where the robust approach yields considerable improvement over LS fitting of neural networks.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"82 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":"132078471","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

Multiresolution methods for financial time series prediction 金融时间序列预测的多分辨率方法

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495258

V. Bjorn

{"title":"Multiresolution methods for financial time series prediction","authors":"V. Bjorn","doi":"10.1109/CIFER.1995.495258","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495258","url":null,"abstract":"Summary form only given. Fractional Brownian motion (fBm), a 1/f, fractal process, has long been considered a plausible model for financial time series. A fractal structure of the market, indicating the presence of correlations across time, hints at the possibility of some predictability. Recent advances in time/frequency localized transforms by the applied mathematics and electrical engineering communities provide us with powerful new methods for the analysis of this type of process. In fact, it has been proven by Wornell that the wavelet transform is an optimal (KL) transform for fBm processes. With this result, we consider using the wavelet decomposition to analyze financial time series. Specifically, the discrete wavelet transform can be used to decompose a signal into several scales, while maintaining time localization of events in each scale. In terms of financial time series, we can conceptually think of each of these scales as the contribution to the price movement from the information and traders associated with a given investment horizon, for instance, long term traders, such as institutional investors, basing their trades on long term information, form the low-frequency component of the market. Once we have extracted out these scales, we can view each as a stationary time series, which can be modeled, analyzed and predicted individually, either independently, or in conjunction with other scales and data that is relevant to that scale. For the case of prediction, the forecasts from each scale can be fused together, with traditional techniques such as hard coded decision rules, or with a neural network, to arrive at tomorrow's direction and/or price.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"158 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":"115554719","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: 22

Artificial market making with neural nets: an application to options 用神经网络人工做市:期权的应用

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495270

H. Englisch, S. Mayhew

引用次数: 1

A methodology for stock market analysis utilizing rough set theory 基于粗糙集理论的股票市场分析方法

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495230

R. Golan, W. Ziarko

{"title":"A methodology for stock market analysis utilizing rough set theory","authors":"R. Golan, W. Ziarko","doi":"10.1109/CIFER.1995.495230","DOIUrl":"https://doi.org/10.1109/CIFER.1995.495230","url":null,"abstract":"Quants are aiding brokers and investment managers for stock market analysis and prediction. The Quant's black magic stems from many of the evolving artificial intelligence (AI) techniques. Extensive literature exists describing attempts to use AI techniques, and in particular neural networks, for analyzing stock market variations. The main problem with neural networks, however is the tremendous difficulty in interpreting the results. The neural nets approach is a black box approach in which no new knowledge regarding the nature of the interactions between the market indicators and the stock market fluctuations is extracted from the market data. Consequently, there is a need to develop methodologies and tools which would help in increasing the degree of understanding of market processes and, at the same time, would allow for relatively accurate predictions. The methods stemming from the research on knowledge discovery in databases (KDD) seem to provide a good mix of predictive and knowledge acquisition capabilities for the purpose of market prediction and market data analysis. This paper describes the methodology of rough sets while citing two applications which apply rough set theory (BST) for stock market analysis using Datalogic/R+. This is based on the variable precision model of rough sets (VPRS) to acquire new knowledge from market data.","PeriodicalId":374172,"journal":{"name":"Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"9 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":"116848709","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: 58

Intraday volatility forecasting for option pricing using a neural network approach 基于神经网络的期权定价日内波动率预测

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495229

F. G. Miranda, A. Burgess

引用次数: 2

Predicting exchange rates using a fuzzy learning system 使用模糊学习系统预测汇率

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495260

Tao Li, L. Fang, D. Guo, S. Klasa

引用次数: 1

Neural networks and multivariate currency forecasting 神经网络与多元货币预测

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495259

N. Kahhwa, Gan Woon Seng

引用次数: 1

Portfolio choice through convex optimization 基于凸优化的投资组合选择

Proceedings of 1995 Conference on Computational Intelligence for Financial Engineering (CIFEr) Pub Date : 1995-04-09 DOI: 10.1109/CIFER.1995.495267

P. Henrotte, H. Lebret

引用次数: 0