Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501848
V. Hanagandi, A. Dhar, K. Buescher
Historical information on credit card transactions can be used to generate a fraud score which can then be used to reduce credit card fraud. The report describes a fraud-nonfraud classification methodology using a radial basis function network (RBFN) with a density based clustering approach. The input data is transformed into the cardinal component space and clustering as well as RBFN modeling is done using a few cardinal components. The methodology has been tested on a fraud detection problem and the preliminary results obtained are satisfactory.
{"title":"Density-based clustering and radial basis function modeling to generate credit card fraud scores","authors":"V. Hanagandi, A. Dhar, K. Buescher","doi":"10.1109/CIFER.1996.501848","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501848","url":null,"abstract":"Historical information on credit card transactions can be used to generate a fraud score which can then be used to reduce credit card fraud. The report describes a fraud-nonfraud classification methodology using a radial basis function network (RBFN) with a density based clustering approach. The input data is transformed into the cardinal component space and clustering as well as RBFN modeling is done using a few cardinal components. The methodology has been tested on a fraud detection problem and the preliminary results obtained are satisfactory.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"44 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":"115632299","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501853
Ehsan H. Feroz, T. Kwon
In the fields of accounting and auditing, detection of firms engaged in fraudulent financial reporting has become increasingly important, due to the increased frequency of such events and the attendant costs of litigation. Conventional statistical tools such as legit and probit have not been successful in detecting such firms. We employ seven redflags which are composed of four financial redflags and three turn over redflags in order to detect targets of the Securities and Exchange Commission's (SEC) investigation of fraudulent financial reporting. Two prominent nonlinear approaches, i.e. neural network and fuzzy sets, are applied to detection of SEC investigation targets and compared with the conventional statistical methods.
{"title":"Self-organizing fuzzy and MLP approaches to detecting fraudulent financial reporting","authors":"Ehsan H. Feroz, T. Kwon","doi":"10.1109/CIFER.1996.501853","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501853","url":null,"abstract":"In the fields of accounting and auditing, detection of firms engaged in fraudulent financial reporting has become increasingly important, due to the increased frequency of such events and the attendant costs of litigation. Conventional statistical tools such as legit and probit have not been successful in detecting such firms. We employ seven redflags which are composed of four financial redflags and three turn over redflags in order to detect targets of the Securities and Exchange Commission's (SEC) investigation of fraudulent financial reporting. Two prominent nonlinear approaches, i.e. neural network and fuzzy sets, are applied to detection of SEC investigation targets and compared with the conventional statistical methods.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"89 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":"115220226","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501817
R. Freedman, R. Digiorgio
The problem that concerns us is the cost-effective computation of the expected value of a derivative security. One should not separate the method of computing the expected present value of a structured security from its ultimate computing topology. In particular, the network infrastructure is as least as important a factor in cost-effective computing as the algorithm design and its processor implementation. We investigate the network issues involved with deploying sophisticated derivative analytics on a modern computer network. We show that same technology that can be used to exploit parallelism can also be used to deploy sophisticated analytics to authorized users in a cost-effective way that is secure, easily updatable and relatively machine-independent. We put these ideas to practice by extending our derivative computation system, which was used to compare the derivative valuations on various computing network architectures. The benchmark problem computes an American "put" option under various interest rate scenarios using a combination of binomial lattice and Monte Carlo methods. We rebuilt the system as an executable derivative calculator applet. It is currently viewable on any Java-enabled Web Browser on the World Wide Web, independent of the computer processor or operating system. It also exploits parallelism: it uses any processor available on its local host to automatically speed itself up.
{"title":"New computational architectures for pricing derivatives","authors":"R. Freedman, R. Digiorgio","doi":"10.1109/CIFER.1996.501817","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501817","url":null,"abstract":"The problem that concerns us is the cost-effective computation of the expected value of a derivative security. One should not separate the method of computing the expected present value of a structured security from its ultimate computing topology. In particular, the network infrastructure is as least as important a factor in cost-effective computing as the algorithm design and its processor implementation. We investigate the network issues involved with deploying sophisticated derivative analytics on a modern computer network. We show that same technology that can be used to exploit parallelism can also be used to deploy sophisticated analytics to authorized users in a cost-effective way that is secure, easily updatable and relatively machine-independent. We put these ideas to practice by extending our derivative computation system, which was used to compare the derivative valuations on various computing network architectures. The benchmark problem computes an American \"put\" option under various interest rate scenarios using a combination of binomial lattice and Monte Carlo methods. We rebuilt the system as an executable derivative calculator applet. It is currently viewable on any Java-enabled Web Browser on the World Wide Web, independent of the computer processor or operating system. It also exploits parallelism: it uses any processor available on its local host to automatically speed itself up.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"44 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":"129599144","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501856
D. Minkov
The rapid economic growth of countries from the South East Asian (SEA) region creates good investment opportunities. A simple way to invest in the SEA region is to play the stock exchange indices by investing in an investment company with several funds of countries from the same region. The significant swings in the indices of many of these countries, and the online information on the Internet concerning the movements of the indices allow the employment of aggressive investment strategies. In the long term, an annual increment of 30-60% can be achieved, depending on the investment strategy used.
{"title":"Optimisation of an investment in South East Asian country funds investment company","authors":"D. Minkov","doi":"10.1109/CIFER.1996.501856","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501856","url":null,"abstract":"The rapid economic growth of countries from the South East Asian (SEA) region creates good investment opportunities. A simple way to invest in the SEA region is to play the stock exchange indices by investing in an investment company with several funds of countries from the same region. The significant swings in the indices of many of these countries, and the online information on the Internet concerning the movements of the indices allow the employment of aggressive investment strategies. In the long term, an annual increment of 30-60% can be achieved, depending on the investment strategy used.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"24 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":"114677929","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501839
S. Chou, Chau-Chen Yang, Chi-Huang Chan, F. Lai
We propose an intelligent stock trading decision support system that can forecast the buying and selling signals according to the prediction of short-term and long-term trends using rule-based neural networks. A rule-based neural network allows us to use domain knowledge in the form of inference rules to set up the initial structure of the neural network, and to extract refined domain knowledge from the trained network. With this information, users can understand why and how a decision is made by the system without the need to trust the output of the network blindly. The performance of the proposed system was evaluated by trading the TSEWPI (Taiwan Stock Exchange Weighted Price Index) from 1992 to 1995, and the result was encouraging.
{"title":"A rule-based neural stock trading decision support system","authors":"S. Chou, Chau-Chen Yang, Chi-Huang Chan, F. Lai","doi":"10.1109/CIFER.1996.501839","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501839","url":null,"abstract":"We propose an intelligent stock trading decision support system that can forecast the buying and selling signals according to the prediction of short-term and long-term trends using rule-based neural networks. A rule-based neural network allows us to use domain knowledge in the form of inference rules to set up the initial structure of the neural network, and to extract refined domain knowledge from the trained network. With this information, users can understand why and how a decision is made by the system without the need to trust the output of the network blindly. The performance of the proposed system was evaluated by trading the TSEWPI (Taiwan Stock Exchange Weighted Price Index) from 1992 to 1995, and the result was encouraging.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"7 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":"133017246","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501822
Monzurul Hoque
The central focus of this paper is to conduct an elaborate search for chaos in the national stock market index prices. We looked at 810 days of index prices by employing all the techniques discussed in the finance literature. The structures sorted out in this paper were calendar and non-calendar factors. We provide conclusive evidence for the presence of chaos in national stock index prices data. Most importantly, this provides a rational impetus for future growth in international portfolio investments in a highly integrated world. Furthermore, future exploration of the underlying structure confirmed by the presence of chaos may lead to the clarification of unexplained changes in national stock index prices.
{"title":"Impetus for future growth in the globalization of stock investments: an evidence from joint time series and chaos analyses","authors":"Monzurul Hoque","doi":"10.1109/CIFER.1996.501822","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501822","url":null,"abstract":"The central focus of this paper is to conduct an elaborate search for chaos in the national stock market index prices. We looked at 810 days of index prices by employing all the techniques discussed in the finance literature. The structures sorted out in this paper were calendar and non-calendar factors. We provide conclusive evidence for the presence of chaos in national stock index prices data. Most importantly, this provides a rational impetus for future growth in international portfolio investments in a highly integrated world. Furthermore, future exploration of the underlying structure confirmed by the presence of chaos may lead to the clarification of unexplained changes in national stock index prices.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"247 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":"132683311","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501825
Dirk Ormoneit, R. Neuneier
We compare the performance of multilayer perceptrons and density estimating neural networks in the task of forecasting the return and the volatility of the DAX index. We claim that for nontrivial target distributions, density estimating networks should lead to improved predictions. The reason is that the latter are capable of embodying more complex probability models for the target noise. We discuss appropriate distribution assumptions for the important cases of outliers and non constant variances, and give interpretations of the new estimates in regression theory.
{"title":"Experiments in predicting the German stock index DAX with density estimating neural networks","authors":"Dirk Ormoneit, R. Neuneier","doi":"10.1109/CIFER.1996.501825","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501825","url":null,"abstract":"We compare the performance of multilayer perceptrons and density estimating neural networks in the task of forecasting the return and the volatility of the DAX index. We claim that for nontrivial target distributions, density estimating networks should lead to improved predictions. The reason is that the latter are capable of embodying more complex probability models for the target noise. We discuss appropriate distribution assumptions for the important cases of outliers and non constant variances, and give interpretations of the new estimates in regression theory.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"111 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":"123440409","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}
Pub Date : 1996-03-24DOI: 10.1109/CIFER.1996.501820
Shu-Heng Chen, C. Yeh
Applies the genetic programming (GP) based notion of unpredictability to the testing of the efficient market hypothesis (EMH). This paper extends the study of Chen and Yeh (1995) by testing the EMH with a small, medium and large sample of the S&P 500 stock index. It is found that, in terms of the prediction performance, the probability /spl pi//sub 2/(n) that GP can beat the random walk tends to have a negative relation to the size of the in-sample dataset. For example, when the sample size n is 50, 200 and 2000, then /spl pi//sub 2/(n) is 0.5, 0.2 and 0, respectively. This therefore suggests that, while nonlinear regularities could exist, they might exist in a very short span. As a consequence, the search costs of discovering them might be too high to make the exploitation of these regularities profitable; hence, the EMH is sustained.
{"title":"Bridging the gap between nonlinearity tests and the efficient market hypothesis by genetic programming","authors":"Shu-Heng Chen, C. Yeh","doi":"10.1109/CIFER.1996.501820","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501820","url":null,"abstract":"Applies the genetic programming (GP) based notion of unpredictability to the testing of the efficient market hypothesis (EMH). This paper extends the study of Chen and Yeh (1995) by testing the EMH with a small, medium and large sample of the S&P 500 stock index. It is found that, in terms of the prediction performance, the probability /spl pi//sub 2/(n) that GP can beat the random walk tends to have a negative relation to the size of the in-sample dataset. For example, when the sample size n is 50, 200 and 2000, then /spl pi//sub 2/(n) is 0.5, 0.2 and 0, respectively. This therefore suggests that, while nonlinear regularities could exist, they might exist in a very short span. As a consequence, the search costs of discovering them might be too high to make the exploitation of these regularities profitable; hence, the EMH is sustained.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"SE-1 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":"126572640","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}
Pub Date : 1996-03-01DOI: 10.1109/CIFER.1996.501845
B. Dumas, Jeff Fleming, R. Whaley
Black and Scholes (1973) implied volatilities tend to be systematically related to the option's exercise price and time to expiration. Derman and Kani (1994), Dupire (1994), and Rubinstein (1994) attribute this behavior to the fact that the Black/Scholes constant volatility assumption is violated in practice. These authors hypothesize that the volatility of the underlying asset's return is a deterministic function of the asset price and time and develop the deterministic volatility function (DVF) option valuation model, which has the potential of fitting the observed cross-section of option prices exactly. Using a sample of S and P 500 index options during the period June 1988 through December 1993, we evaluate the economic significance of the implied deterministic volatility function by examining the predictive and hedging performance of the DVF option valuation model.
{"title":"Implied volatility functions: empirical tests","authors":"B. Dumas, Jeff Fleming, R. Whaley","doi":"10.1109/CIFER.1996.501845","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501845","url":null,"abstract":"Black and Scholes (1973) implied volatilities tend to be systematically related to the option's exercise price and time to expiration. Derman and Kani (1994), Dupire (1994), and Rubinstein (1994) attribute this behavior to the fact that the Black/Scholes constant volatility assumption is violated in practice. These authors hypothesize that the volatility of the underlying asset's return is a deterministic function of the asset price and time and develop the deterministic volatility function (DVF) option valuation model, which has the potential of fitting the observed cross-section of option prices exactly. Using a sample of S and P 500 index options during the period June 1988 through December 1993, we evaluate the economic significance of the implied deterministic volatility function by examining the predictive and hedging performance of the DVF option valuation model.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127020588","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}
Pub Date : 1995-12-01DOI: 10.1109/CIFER.1996.501842
R. Bhar, C. Chiarella
Hedging interest rate exposures using interest rate futures contracts requires some knowledge of the volatility function of the interest rates. Use of historical data as well as interest rate options like caps and swaptions to estimate this volatility function, have been proposed in the literature. The interest rate futures price is modelled within an arbitrage-free framework for a volatility function which includes a stochastic variable, the instantaneous spot interest rate. The resulting system is expressed in a state space form which is solved using an extended Kalman filter. The technique is applied to short-term interest rate futures contracts trading on the Sydney Futures Exchange as well as on the Tokyo International Financial Futures Exchange. The residual diagnostics indicate suitability of the model and the bootstrap resampling technique is used to obtain small sample properties of the parameters of the volatility function.
{"title":"Interest rate futures: estimation of volatility parameters in an arbitrage-free framework","authors":"R. Bhar, C. Chiarella","doi":"10.1109/CIFER.1996.501842","DOIUrl":"https://doi.org/10.1109/CIFER.1996.501842","url":null,"abstract":"Hedging interest rate exposures using interest rate futures contracts requires some knowledge of the volatility function of the interest rates. Use of historical data as well as interest rate options like caps and swaptions to estimate this volatility function, have been proposed in the literature. The interest rate futures price is modelled within an arbitrage-free framework for a volatility function which includes a stochastic variable, the instantaneous spot interest rate. The resulting system is expressed in a state space form which is solved using an extended Kalman filter. The technique is applied to short-term interest rate futures contracts trading on the Sydney Futures Exchange as well as on the Tokyo International Financial Futures Exchange. The residual diagnostics indicate suitability of the model and the bootstrap resampling technique is used to obtain small sample properties of the parameters of the volatility function.","PeriodicalId":378565,"journal":{"name":"IEEE/IAFE 1996 Conference on Computational Intelligence for Financial Engineering (CIFEr)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1995-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"131607245","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}
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