Intell. Syst. Account. Finance Manag.最新文献

{"title":"Decision making using time-dependent knowledge: knowledge augmentation using qualitative reasoning","authors":"S. Yu, Sang-Chan Park, Jyun-Cheng Wang","doi":"10.1002/isaf.194","DOIUrl":"https://doi.org/10.1002/isaf.194","url":null,"abstract":"In this paper we propose a method to enhance the performance of knowledge-based decision-support systems, knowledge of which is volatile and incomplete by nature in a dynamically changing situation, by providing meta-knowledge augmented by the Qualitative Reasoning (QR) approach. The proposed system intends to overcome the potential problem of completeness of the knowledge base. Using the deep meta-knowledge incorporated into the QR module, along with the knowledge we gain from applying inductive learning, we then identify the ongoing process and amplify the effects of each pending process to the attribute values. In doing so, we apply the QR models to enhance or reveal the patterns which are otherwise less obvious. The enhanced patterns can eventually be used to improve the classification of the data samples. The success factor hinges on the completeness of the QR process knowledge base. With enough processes taking place, the influences of each process will lead prediction in a direction that can reflect more of the current trend. The preliminary results are successful and shed light on the smooth introduction of Qualitative Reasoning to the business domain from the physical laboratory application. © 2001 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"132 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114506023","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":"Clustering technique for risk classification and prediction of claim costs in the automobile insurance industry","authors":"A. C. Yeo, K. Smith‐Miles, R. Willis, M. Brooks","doi":"10.1002/isaf.196","DOIUrl":"https://doi.org/10.1002/isaf.196","url":null,"abstract":"This paper considers the problem of predicting claim costs in the automobile insurance industry. The first stage involves classifying policy holders according to their perceived risk, followed by modelling the claim costs within each risk group. Two methods are compared for the risk classification stage: a data-driven approach based on hierarchical clustering, and a previously published heuristic method that groups policy holders according to pre-defined factors. Regression is used to model the expected claim costs within a risk group. A case study is presented utilizing real data, and both risk classification methods are compared according to a variety of accuracy measures. The results of the case study show the benefits of employing a data-driven approach. © 2001 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"161 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125888938","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":"Decision-making capabilities of a hybrid system applied to the auditor's going-concern assessment","authors":"M. Lenard, Pervaiz Alam, David E. Booth, G. Madey","doi":"10.1002/isaf.190","DOIUrl":"https://doi.org/10.1002/isaf.190","url":null,"abstract":"The purpose of this study is to evaluate a hybrid system as a decision support model to assist with the auditor's going-concern assessment. The going-concern assessment is often an unstructured decision that involves the use of both qualitative and quantitative information. An expert system that predicts the going-concern decision has been developed in consultation with partners at three of the Big Five accounting firms. This system is combined with a statistical model that predicts bankruptcy, as a component of the auditor's decision, to form a hybrid system. The hybrid system, because it combines the use of quantitative and qualitative information, has the potential for better prediction accuracy than either the expert system or statistical model predicting separately. In addition, testing of the system provides some insight into the characteristics of firms that experience problems, but do not necessarily receive a going-concern modification. Further investigation into those firms that have problems could reveal factors that may be incorporated into decision support systems for auditors, in order to improve accuracy and reliability of these decision tools. © 2001 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"45 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2001-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132043234","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":"Developing a bankruptcy prediction model via rough sets theory","authors":"Thomas E. McKee","doi":"10.1002/1099-1174(200009)9:3%3C159::AID-ISAF184%3E3.0.CO;2-C","DOIUrl":"https://doi.org/10.1002/1099-1174(200009)9:3%3C159::AID-ISAF184%3E3.0.CO;2-C","url":null,"abstract":"The high individual and social costs encountered in corporate bankruptcies make this decision problem very important to parties such as auditors, management, government policy makers, and investors. Bankruptcy is a worldwide problem and the number of bankruptcies can be considered an index of the robustness of individual country economies. The costs associated with this problem have led to special disclosure responsibilities for both management and auditors. Bankruptcy prediction is a problematic issue for all parties associated with corporate reporting since the development of a cause–effect relationship between the many attributes that may cause or be related to bankruptcy and the actual occurrence of bankruptcy is difficult. An approach that has been proposed for dealing with this type of prediction problem is rough sets theory. Rough sets theory involves a calculus of partitions. A rough sets theory based model has the following advantages: (1) the rough sets data analysis process results in the information contained in a large number of cases being reduced to a model containing a generalized description of knowledge, (2) the model is a set of easily understandable decision rules which do not normally need interpretation, (3) each decision rule is supported by a set of real examples, (4) additional information like probabilities in statistics or grade of membership in fuzzy set theory is not required. In keeping with the philosophy of building on prior research, variables identified in prior recursive partitioning research were used to develop a rough sets bankruptcy prediction model. The model was 93% accurate in predicting bankruptcy on a 100-company developmental sample and 88% accurate on the overall separate 100-company holdout sample. This was superior to the original recursive partitioning model which was only 65% accurate on the same data set. The current research findings are also compared, both in terms of predictive results and variables identified, to three prior rough sets empirical bankruptcy prediction studies. The model produced by the current research had a significantly higher prediction accuracy on its validation sample and employed fewer variables. This research significantly extends prior rough sets bankruptcy prediction research by using a larger sample size and data from U.S. public companies. Implications for both bankruptcy prediction and future research are explored. Copyright © 2000 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123527392","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":"Artificial neural networks in accounting and finance: modeling issues","authors":"J. Coakley, Carol E. Brown","doi":"10.1002/1099-1174(200006)9:2%3C119::AID-ISAF182%3E3.0.CO;2-Y","DOIUrl":"https://doi.org/10.1002/1099-1174(200006)9:2%3C119::AID-ISAF182%3E3.0.CO;2-Y","url":null,"abstract":"This article reviews the literature on artificial neural networks (ANNs) applied to accounting and finance problems and summarizes the ‘suggestions’ from this literature. The first section reviews the basic foundation of ANNs to provide a common basis for further elaboration and suggests criteria that should be used to determine whether the use of an ANN is appropriate. The second section of the paper discusses development of ANN models including: selection of the learning algorithm, choice of the error and transfer functions, specification of the architecture, preparation of the data to match the architecture, and training of the network The final section presents some general guidelines and a brief summary of research progress and open research questions. Copyright © 2000 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116140287","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":"Management of reengineering knowledge: AI-based approaches","authors":"D. O’Leary","doi":"10.1002/1099-1174(200006)9:2%3C107::AID-ISAF179%3E3.0.CO;2-J","DOIUrl":"https://doi.org/10.1002/1099-1174(200006)9:2%3C107::AID-ISAF179%3E3.0.CO;2-J","url":null,"abstract":"Knowledge about \"best practices\" for reengineering can be critical to a firm’s ability to evolve and respond to competition. As a result, this paper addresses the issue of how to manage reengineering knowledge. Multiple forms of knowledge representation are adapted to address two primary issues: When and what should a firm reengineer? Four different knowledge-based models and prototypes are developed to illustrate particular types of reengineering knowledge. The prototypes are used to draw inferences about issues in knowledge management and to illustrate feasibility. Distribution of best practices reengineering knowledge can then be accomplished using knowledge servers or making software and knowledge bases available to download off the world wide web.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"7 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126654174","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 methodology for modeling influence diagrams: a case-based reasoning approach","authors":"Jae Kwang Lee, Jae Kyeong Kim, S. Kim","doi":"10.1002/(SICI)1099-1174(200003)9:1%3C55::AID-ISAF180%3E3.0.CO;2-5","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(200003)9:1%3C55::AID-ISAF180%3E3.0.CO;2-5","url":null,"abstract":"In this paper, a case-based reasoning approach to build an influence diagram is described. Building an influence diagram in decision analysis is known to be a most complicated and burdensome process. To overcome such a difficulty, decision class analysis is suggested, which treats a set of decisions having some degree of similarity as a single unit. This research suggests a case-based reasoning approach as a methodology to analyze a class of decisions. The candidate influence diagrams are retrieved from a set of similar influence diagrams, a case base. They are combined and modified by the node classification tree and DM’s preference for the given decision problem. For such a purpose, the case representation and retrieval process is explained with the adaptation process. We suggest using two measure, the fitness and garbage ratio for the case retrieval process. The basic concept of decision class analysis and case-based reasoning is very similar so the case-based reasoning approach is believed to be a better methodology to implement a decision class analysis. Copyright © 2000 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"82 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121886486","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":"Restructuring the credit process: behaviour scoring for german corporates","authors":"Sebastian Fritz, Detlef Hosemann","doi":"10.1002/(SICI)1099-1174(200003)9:1%3C9::AID-ISAF168%3E3.0.CO;2-Q","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(200003)9:1%3C9::AID-ISAF168%3E3.0.CO;2-Q","url":null,"abstract":"An instrument for automated monthly credit standing analysis based on data of the corporates current accounts is presented. Different methods of statistics and machine learning are used to develop scoring models for the supervision of debtors. The following methods were selected for model developement: \u0000 \u0000 \u0000 \u0000Linear Discriminant Analysis \u0000 \u0000 \u0000 \u0000 \u0000Pattern Recognition (k-nearest-neighbours) \u0000 \u0000 \u0000 \u0000 \u0000Genetic Algorithms \u0000 \u0000 \u0000 \u0000 \u0000Neural Networks \u0000 \u0000 \u0000 \u0000 \u0000Decision Trees \u0000 \u0000 \u0000 \u0000 \u0000The developed models were compared not only concerning their classification results but also concerning score distribution, transparency and IT-realisation. Copyright © 2000 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116756114","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":"Multivariate volatility analysis of VW stock prices","authors":"H. Herwartz, H. Lütkepohl","doi":"10.1002/(SICI)1099-1174(200003)9:1%3C35::AID-ISAF176%3E3.0.CO;2-V","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(200003)9:1%3C35::AID-ISAF176%3E3.0.CO;2-V","url":null,"abstract":"VW common and preference stock prices are modelled and analyzed using models which allow for multivariate conditional heteroscedasticity. The relationship between the conditional variances of the variables is investigated by suitable impulse responses or conditional moment profiles. It is found that there is a clear asymmetry in the volatility of the series which react quite differently to positive and negative shocks in the market. Also some differences in the reactions of preference and common stocks are uncovered. No significant evidence is found for size effects, that is, the way the variables respond to unexpected shocks in the market depends more on the sign of the shocks than on their size. Copyright © 2000 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"79 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132969913","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":"Credit scoring and reject inference with mixture models","authors":"A. Feelders","doi":"10.1002/(SICI)1099-1174(200003)9:1%3C1::AID-ISAF177%3E3.0.CO;2-%23","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(200003)9:1%3C1::AID-ISAF177%3E3.0.CO;2-%23","url":null,"abstract":"Reject inference is the process of estimating the risk of defaulting for loan applicants that are rejected under the current acceptance policy. We propose a new reject inference method based on mixture modeling, that allows the meaningful inclusion of the rejects in the estimation process. We describe how such a model can be estimated using the EM algorithm. An experimental study shows that inclusion of the rejects can lead to a substantial improvement of the resulting classification rule. Copyright © 1999 John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"70 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2000-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133013880","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}