{"title":"Evolved hybrid auction mechanisms in non-ZIP trader marketplaces","authors":"D. Cliff, Vibhu Walia, A. Byde","doi":"10.1109/CIFER.2003.1196257","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196257","url":null,"abstract":"A previous paper by D. Cliff (see ibid., 2002) demonstrated that a genetic algorithm could be used to automatically discover new optimal auction mechanisms for automated electronic marketplaces populated by software-agent traders. Significantly, the new auction mechanisms are often unlike traditional mechanisms designed by humans for human traders; rather, they are peculiar hybrid mixtures of established styles of mechanism. This previous work used software agents running the ZIP trader algorithm (recently shown to outperform human traders). We provide the first demonstration that qualitatively similar results (i.e., non-standard hybrid mechanism designs being optimal) are also given when similar experiments are performed using a different trader algorithm, namely Gode & Sunder's (1993) ZI-C traders. Thus, the paper is the first to offer significant evidence that evolved hybrid auction mechanisms may be found that out-perform traditional market mechanisms for many styles of trader-agent.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"142 6","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2003-03-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120989813","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}
{"title":"A characterization of long-short trading strategies based on cointegration","authors":"Y. Kawasaki, S. Tachiki, H. Udaka, T. Hirano","doi":"10.1109/CIFER.2003.1196289","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196289","url":null,"abstract":"Forecasting stock price in level is very difficult because such a series is often described as an integrated process. In spite of that, a linear combination of two stock prices can be stationary around a fixed mean. Then taking both long and short position can lead to profit. This article aims to characterize the long-short strategies based on cointegration by investigating their risk-return properties.","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":"126109073","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}
{"title":"Trend time series modeling and forecasting with neural networks","authors":"M. Qi, G. Zhang","doi":"10.1109/CIFER.2003.1196279","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196279","url":null,"abstract":"Despite its great importance, there has been no general consensus on how to model the trends in time series data. Compared to traditional approaches, neural networks have shown some promise in time series forecasting. This paper investigates how to best model trend time series using neural networks. Four strategies (raw data, raw data with time index, detrending, and differencing) are used to model various simulated trend patterns (linear, nonlinear, deterministic, stochastic, and breaking trend). We find that with neural networks differencing often gives meritorious results regardless of the underlying DGPs. This finding is also confirmed by the real GNP series.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"19 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":"114477878","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}
{"title":"Microeconomic modeling of financial time series with long term memory","authors":"R. Cerqueti, G. Rotundo","doi":"10.1109/CIFER.2003.1196260","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196260","url":null,"abstract":"In this paper we fix a microeconomic model of exchange rates and we give the explicit relation between model's parameters and its long memory properties. This avoids long numerical calibration procedures and allows to build the model with the parameters suitable for the required long memory degree.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"9 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":"121200471","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}
T. V. Gestel, B. Baesens, J. Suykens, M. Espinoza, Dirk-Emma Baestaens, J. Vanthienen, B. Moor
{"title":"Bankruptcy prediction with least squares support vector machine classifiers","authors":"T. V. Gestel, B. Baesens, J. Suykens, M. Espinoza, Dirk-Emma Baestaens, J. Vanthienen, B. Moor","doi":"10.1109/CIFER.2003.1196234","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196234","url":null,"abstract":"Classification algorithms like linear discriminant analysis and logistic regression are popular linear techniques for modelling and predicting corporate distress. These techniques aim at finding an optimal linear combination of explanatory input variables, such as, e.g., solvency and liquidity ratios, in order to analyse, model and predict corporate default risk. Recently, performant kernel based nonlinear classification techniques, like support vector machines, least squares support vector machines and kernel fisher discriminant analysis, have been developed. Basically, these methods map the inputs first in a nonlinear way to a high dimensional kernel-induced feature space, in which a linear classifier is constructed in the second step. Practical expressions are obtained in the so-called dual space by application of Mercer's theorem. In this paper, we explain the relations between linear and nonlinear kernel based classification and illustrate their performance on predicting bankruptcy of mid-cap firms in Belgium and the Netherlands.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"23 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":"130007042","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}
Tian-Shyr Dai, I-Yuan Chen, Yuh-Yuan Fang, Y. Lyuu
{"title":"Analytics and algorithms for geometric average trigger reset options","authors":"Tian-Shyr Dai, I-Yuan Chen, Yuh-Yuan Fang, Y. Lyuu","doi":"10.1109/CIFER.2003.1196242","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196242","url":null,"abstract":"The geometric average trigger reset option resets the strike price based on the geometric average of the underlying asset's prices over a monitoring window. This paper derives an analytic formula and two numerical methods for pricing this option with multiple resets. The analytic formula in fact is a corollary of a general formula that holds for a large class of path-dependent options: It prices any option whose payoff function can be written as e/sup b/spl middot/X/1/sub {X/spl isin/A}/. For general American-style reset options, an O(n/sup 4/h/sup 2/)-time algorithm on n-period binomial lattice is presented. A much more efficient O(n/sup 3/ hm)-time algorithm prices European-style reset options. Monte Carlo simulation suggests that the European-style geometric average trigger reset option and the arithmetic version have similar option values. This implies that results in this paper give tight prices for the difficult arithmetic version.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"10 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":"124117654","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}
M. Magdon-Ismail, A. Atiya, Amrit Pratap, Y. Abu-Mostafa
{"title":"The maximum drawdown of the Brownian motion","authors":"M. Magdon-Ismail, A. Atiya, Amrit Pratap, Y. Abu-Mostafa","doi":"10.1109/CIFER.2003.1196267","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196267","url":null,"abstract":"The MDD is defined as the maximum loss incurred from peak to bottom during a specified period of time. It is often preferred over some of the other risk measures because of the tight relationship between large drawdowns and fund redemptions. Also, a large drawdown can even indicate the start of a deterioration of an otherwise successful trading system, for example due to a market regime switch. Overall, the MDD is a very important risk measure. To be able to use it more insightfully, its analytical properties have to be understood. As a step towards this direction, we have presented in this article some analytic results that we have developed. We hope more and more results will come out from the research community analyzing this important measure.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"54 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":"123638728","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}
{"title":"Trend detection using auto-associative neural networks: Intraday KOSPI 200 futures","authors":"Jun-Myung Lee, Sungzoon Cho, Jinwoo Baek","doi":"10.1109/CIFER.2003.1196290","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196290","url":null,"abstract":"This paper reports the results of a new neural network based trend detector. An auto-associative neural network was trained with the \"trend\" data obtained from the intra-day KOSPI 200 future price. It was then used to predict a trend. Simple investment strategies based on the detector achieved a one-year return of 31.2 points with no leverage.","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":"129666163","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}
{"title":"Efficient estimation of the Hurst parameter in high frequency financial data with seasonalities using wavelets","authors":"Erhan Bayraktar, H. Poor, R. Sircar","doi":"10.1109/CIFER.2003.1196276","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196276","url":null,"abstract":"S&P 500 Index data taken at one-minute intervals over the course of 11.5 years (January 1989- May 2000) is analyzed, and in particular the Hurst parameter over segments of stationarity (the time period over which the Hurst parameter is almost constant) is estimated. (The segments of stationarity are a byproduct of our analysis, no prior assumption about it is made.) An asymptotically efficient estimator using the log-scale spectrum is employed. This estimator is robust to additive non-stationarities, and it is shown to be robust to multiplicative non-stationarities, i.e. seasonalities, as well. Analyzing cumulative sums of returns, rather than the returns themselves, is essential in removing the effect of seasonalities. It is shown that it is necessary to use wavelets with at least two vanishing moments for the analysis in order to achieve this robustness. This analysis shows that the market has become more efficient since 1997.","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":"125501383","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}
{"title":"Optimal calling policies in convertible bonds","authors":"K. Lau, Y. Kwok","doi":"10.1109/CIFER.2003.1196249","DOIUrl":"https://doi.org/10.1109/CIFER.2003.1196249","url":null,"abstract":"Effective numerical algorithms are developed to evaluate the impact of the soft call and hard call constraints, notice period requirement and other factors on the optimal issuer's calling policy in convertible bonds. Our results show that the critical stock price at which the issuer should optimally call the convertible bond depends quite sensibly on these constraints and requirements.","PeriodicalId":257544,"journal":{"name":"2003 IEEE International Conference on Computational Intelligence for Financial Engineering, 2003. Proceedings.","volume":"5 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":"126309413","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}