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2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.最新文献

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Estimating the number of mutual fund styles using the generalized style classification approach and the GAP statistic 用广义风格分类方法和GAP统计估计共同基金风格的数量
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196272
P. Lajbcygier, M. Ong
{"title":"Estimating the number of mutual fund styles using the generalized style classification approach and the GAP statistic","authors":"P. Lajbcygier, M. Ong","doi":"10.1109/CIFER.2003.1196272","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196272","url":null,"abstract":"A growing empirical literature has shown that funds managers can he classified by their investment style and that this classification is useful in predicting performance. Brown and Goetzmann (1997) created a generalized style classification (GSC) technology that has been used in various studies to decide what funds managers belong to what styles. The GSC technology relies on k-means clustering to group fund manager returns into styles. Since the GSC technology relies on k-means clustering, the number of styles must be known a priori. Conventional techniques can be used to estimate the number of groups/style but they are dependent on various unrealistic assumptions. A new method to estimate the number of styles, first proposed by Tibshirani, Walther, and Hastie, (2000), known as the Gap statistic is adapted to the GSC technology. The approach is used to verify the number of styles in Japanese mutual funds data.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"44 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127008832","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: 7
Evolutionary reinforcement learning in FX order book and order flow analysis FX订单簿和订单流分析中的进化强化学习
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196282
R. G. Bates, M. Dempster, Yazann S. Romahi
{"title":"Evolutionary reinforcement learning in FX order book and order flow analysis","authors":"R. G. Bates, M. Dempster, Yazann S. Romahi","doi":"10.1109/CIFER.2003.1196282","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196282","url":null,"abstract":"As macroeconomic fundamentals based modelling of FX time series have been shown not to fit the empirical evidence at horizons of less than one year, interest has moved towards microstructure-based approaches. Order flow data has recently been receiving an increasing amount of attention in equity market analyses and thus increasingly in foreign exchange as well. In this paper, order flow data is coupled with order book derived indicators and we explore whether pattern recognition techniques derived from computational learning can be applied to successfully infer trading strategies on the underlying timeseries. Due to the limited amount of data available the results are preliminary. However, the approach demonstrates promise and it is shown that using order flow and order book data is usually superior to trading on technical signals alone.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130809218","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: 24
Bankruptcy prediction for credit risk using an auto-associative neural network in Korean firms 基于自关联神经网络的韩国企业信用风险破产预测
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196237
Jinwoo Baek, Sungzoon Cho
{"title":"Bankruptcy prediction for credit risk using an auto-associative neural network in Korean firms","authors":"Jinwoo Baek, Sungzoon Cho","doi":"10.1109/CIFER.2003.1196237","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196237","url":null,"abstract":"Empirical bankruptcy prediction models have been proposed and widely used in the last decades or so. Historic solvent and default firm data are collected and labeled appropriately. Statistical and neural network models are then \"trained\" to fit these data. A major problem is the imbalance of data, i.e. much more solvent data than default data. We propose an auto-associative neural network (AANN) that learns the identity mapping of input. By training the network with only solvent data, we built a bankruptcy predictor with better accuracies than conventional 2-class neural network based predictor.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"32 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131077053","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: 48
The predictive power of dividend yields analyzed by methods preserving time-dependent structures 用保留时相关结构的方法分析股利收益率的预测能力
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196239
J. Walde
{"title":"The predictive power of dividend yields analyzed by methods preserving time-dependent structures","authors":"J. Walde","doi":"10.1109/CIFER.2003.1196239","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196239","url":null,"abstract":"This paper analyzes the predictability of dividend-price-ratio for long horizon stock returns with the help of methods that preserve the time dependence of the underlying problem. Bootstrapping and subsampling approaches are employed to handle serial correlation in the data and to achieve reliable t-statistics. Optimization procedures were used to obtain the optimal moving-block-size and subsampling-size respectively. Furthermore to get valid results, it is necessary to calculate the calibration function and hence the corresponding nominal significance level to a requested level. Both moving-block bootstrapping with the optimal block size and the calibrated significance level as well as the optimized subsampling technique strongly reject the hypothesis of predictability in dividend-price-ratio for long horizon returns of the equities at NYSE.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128356929","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
Including life-time and options in residual income indicators 在剩余收益指标中包括终身和期权
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196238
F. Neri, M. Noro, E. Piccirillo
{"title":"Including life-time and options in residual income indicators","authors":"F. Neri, M. Noro, E. Piccirillo","doi":"10.1109/CIFER.2003.1196238","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196238","url":null,"abstract":"We present a proposal for a novel residual income financial indicator. The indicator is based on the standard financial definition of operating cash flow, includes the new dimensions of life-cycle of the business, and puts a numerical value to the choices (options) available to the management during the business life. A hypothetical business example is also described and commented.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"11 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122350542","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
Statistical models for time sequences data mining 时间序列数据挖掘的统计模型
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196281
Jessica K. Ting, M. Ng, H. Rong, J. Huang
{"title":"Statistical models for time sequences data mining","authors":"Jessica K. Ting, M. Ng, H. Rong, J. Huang","doi":"10.1109/CIFER.2003.1196281","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196281","url":null,"abstract":"In this paper, we present an adaptive modelling technique for studying past behaviors of objects and predicting the near future events. Our approach is to define a sliding window (of different window sizes) over a time sequence and build autoregression models from subsequences in different windows. The models are representations of past behaviors of the sequence objects. We can use the AR coefficients as features to index subsequences to facilitate the query of subsequences with similar behaviors. We can use a clustering algorithm to group time sequences on their similarity in the feature space. We can also use the AR models for prediction within different windows. Our experiments show that the adaptive model can give better prediction than non-adaptive models.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"46 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122937416","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
Statistical arbitrage trading with wavelets and artificial neural networks 基于小波和人工神经网络的统计套利交易
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196339
C. Zapart
{"title":"Statistical arbitrage trading with wavelets and artificial neural networks","authors":"C. Zapart","doi":"10.1109/CIFER.2003.1196339","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196339","url":null,"abstract":"The paper outlines the use of an alternative option pricing scheme to perform statistical arbitrage in derivative markets. The method links a binomial tree to an innovative stochastic volatility model that is based on wavelets and artificial neural networks. Wavelets provide a convenient signal/noise decomposition of volatility in a non-linear feature space. Neural networks are used to infer future volatility levels from the wavelets feature space in an iterative manner. The bootstrap method provides 95% confidence intervals for the option prices. When used to set up delta-hedged arbitrage trades in the US equity options market, the proposed approach generates substantial profits.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"17 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131249690","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
Support vector machines for company failure prediction 支持向量机用于公司故障预测
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196241
Z. Yang
{"title":"Support vector machines for company failure prediction","authors":"Z. Yang","doi":"10.1109/CIFER.2003.1196241","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196241","url":null,"abstract":"This paper applies support vector machines (SM), a new powerful learning algorithm, to company failure prediction based on 2048 UK construction companies. The study shows that the SVM model outperforms linear statistical models and other neural network models.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"125 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124203745","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
Estimation of default probability by three-factor structural model 用三因素结构模型估计违约概率
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196235
C. Hui, Edward Chi-Fai Lo, N. Huang
{"title":"Estimation of default probability by three-factor structural model","authors":"C. Hui, Edward Chi-Fai Lo, N. Huang","doi":"10.1109/CIFER.2003.1196235","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196235","url":null,"abstract":"This paper develops a three-factor structural model for estimating probability of default. The model incorporates the stochastic asset value of a corporate, liability and risk-free interest rate with time-dependent model parameters. A corporate defaults when its leverage ratio increases above a predefined default-triggering level. Using average market data for corporates with different external credit ratings, the three-factor model is capable of producing the term structures of probability of default for rated corporates, that are broadly matched with the average default rates of the corresponding ratings reported by Standard & Poor's. The three-factor model can be applied to the estimation of probability of default under the internal ratings-based approach proposed in the New Basel Capital Accord.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115721733","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
Mean square optimal hedges using higher order moments 使用高阶矩的均方最优对冲
2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings. Pub Date : 2003-03-20 DOI: 10.1109/CIFER.2003.1196252
Yuji Yamada, J. Primbs
{"title":"Mean square optimal hedges using higher order moments","authors":"Yuji Yamada, J. Primbs","doi":"10.1109/CIFER.2003.1196252","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196252","url":null,"abstract":"The authors pose and solve a mean square optimal hedging problem that takes higher order moments (or cumulants) into account. They first provide a discrete stochastic dynamics model using a general multinomial lattice, where the first m cumulants are matched over each time step. They then analyze the effect of higher order moments in the underlying asset process on the price of derivative securities. The relationship between the term structure of the volatility smile and smirk and higher order cumulants is illustrated through numerical experiments.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"26 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125849844","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: 6
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