Intell. Syst. Account. Finance Manag.最新文献_第10页

Stock Price Prediction Using Prior Knowledge and Neural Networks 基于先验知识和神经网络的股票价格预测

Intell. Syst. Account. Finance Manag. Pub Date : 1997-03-01 DOI: 10.1002/(SICI)1099-1174(199703)6:1%3C11::AID-ISAF115%3E3.0.CO;2-3

K. Kohara, T. Ishikawa, Y. Fukuhara, Yukihiro Nakamura

引用次数: 160

Predicting Bond Ratings Using Neural Networks: A Comparison with Logistic Regression 用神经网络预测债券评级:与逻辑回归的比较

Intell. Syst. Account. Finance Manag. Pub Date : 1997-03-01 DOI: 10.1002/(SICI)1099-1174(199703)6:1%3C59::AID-ISAF116%3E3.0.CO;2-H

J. Maher, Tarun K. Sen

{"title":"Predicting Bond Ratings Using Neural Networks: A Comparison with Logistic Regression","authors":"J. Maher, Tarun K. Sen","doi":"10.1002/(SICI)1099-1174(199703)6:1%3C59::AID-ISAF116%3E3.0.CO;2-H","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199703)6:1%3C59::AID-ISAF116%3E3.0.CO;2-H","url":null,"abstract":"Bond rating agencies examine the financial outlook of a company and the characteristics of a bond issue and assign a rating that indicates an independent assessment of the degree of default risk associated with the firm’s bonds. Predicting this bond rating has been of interest to potential investors as well as to the firm. Prior research in this area has primarily relied upon traditional statistical methods to develop models with reasonably good prediction accuracy. This article utilizes a neural network approach to modeling the bond rating process in an attempt to increase the overall prediction accuracy of the models. A comparison is made to a more traditional logistic regression approach to classification prediction. The results indicate that the neural networks-based model performs significantly better than the logistic regression model for classifying a holdout sample of newly issued bonds in the 1990–92 period. A potential drawback to a neural network approach is a tendency to overfit the data which could negatively affect the model’s generalizability. This study carefully controls for overfitting and obtains significant improvement in bond rating prediction compared to the logistic regression approach. © 1997 by John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"21 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128017849","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: 86

Adaptive Methods in Macroeconomic Forecasting 宏观经济预测中的自适应方法

Intell. Syst. Account. Finance Manag. Pub Date : 1997-03-01 DOI: 10.1002/(SICI)1099-1174(199703)6:1%3C1::AID-ISAF118%3E3.0.CO;2-2

C. Haefke, M. Natter, T. Soni, H. Otruba

引用次数: 3

Knowledge-Based Systems in Retail Banking: A Survey of Current Practice 零售银行的知识系统:当前实践的调查

Intell. Syst. Account. Finance Manag. Pub Date : 1997-03-01 DOI: 10.1002/(SICI)1099-1174(199703)6:1%3C73::AID-ISAF117%3E3.0.CO;2-E

Y. Shao, Alan Wilson, C. Oppenheim

引用次数: 4

Ordinal Pairwise Partitioning (OPP) Approach to Neural Networks Training in Bond rating 债券评级中神经网络训练的有序成对划分方法

Intell. Syst. Account. Finance Manag. Pub Date : 1997-03-01 DOI: 10.1002/(SICI)1099-1174(199703)6:1%3C23::AID-ISAF113%3E3.0.CO;2-4

Young-sig Kwon, Ingoo Han, K. Lee

{"title":"Ordinal Pairwise Partitioning (OPP) Approach to Neural Networks Training in Bond rating","authors":"Young-sig Kwon, Ingoo Han, K. Lee","doi":"10.1002/(SICI)1099-1174(199703)6:1%3C23::AID-ISAF113%3E3.0.CO;2-4","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199703)6:1%3C23::AID-ISAF113%3E3.0.CO;2-4","url":null,"abstract":"Statistical classification methods such as multivariate discriminant analysis have been widely used in bond rating classification in spite of the limitations of the methodology. Recently, neural networks have emerged as new methods for business classification. This approach to neural networks training is to categorize a new instance as one of the predefined bond classes. Such a conventional approach has limitations in dealing with the ordinal nature of bond rating. In addition, most of the prior studies have used sample data which are evenly divided among the classes. However, the natural population in real application is usually unevenly divided among the classes. Under such circumstances, it is hard to achieve good predictive performance. As the number of classes to be recognized increases, the predictive performance decreases. In this article, to increase the predictive performance in real-world bond rating, we propose the ordinal pairwise partitioning (OPP) approach to backpropagation neural networks training. The main idea of the OPP approach is to partition the data set in the ordinal and pairwise manner according to the output classes. Then, each backpropagation neural networks model is trained by using each partitioned data set and is separately used for classification. Experimental results show that the predictive performance of the proposed OPP approach can be significantly enhanced, when compared to the conventional neural networks modeling approach as well as multivariate discriminant analysis. The OPP approach has two computation methods, and we discuss under which circumstances one method performs better than the other. We also show the generalizability of the OPP approach. © 1997 by John Wiley & Sons, Ltd.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"15 3 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1997-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123706093","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: 77

Organizing a Model Base of Linear Programming Models Using Analogical Processes 利用类比过程组织线性规划模型库

Intell. Syst. Account. Finance Manag. Pub Date : 1996-12-01 DOI: 10.1002/(SICI)1099-1174(199612)5:4%3C217::AID-ISAF111%3E3.0.CO;2-9

Ruth Schwartz, F. Murphy

引用次数: 1

A Case-Based Reasoning System for Indirect Bank Lending 基于案例的银行间接贷款推理系统

Intell. Syst. Account. Finance Manag. Pub Date : 1996-12-01 DOI: 10.1002/(SICI)1099-1174(199612)5:4%3C229::AID-ISAF110%3E3.0.CO;2-7

Atish P. Sinha, Mark A. Richardson

引用次数: 10

The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks 利用财务报表信息预测盈余:基于Logit模型和人工神经网络的经验证据

Intell. Syst. Account. Finance Manag. Pub Date : 1996-12-01 DOI: 10.1002/(SICI)1099-1174(199612)5:4%3C199::AID-ISAF114%3E3.0.CO;2-C

A. Charitou, C. Charalambous

{"title":"The Prediction of Earnings Using Financial Statement Information: Empirical Evidence With Logit Models and Artificial Neural Networks","authors":"A. Charitou, C. Charalambous","doi":"10.1002/(SICI)1099-1174(199612)5:4%3C199::AID-ISAF114%3E3.0.CO;2-C","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199612)5:4%3C199::AID-ISAF114%3E3.0.CO;2-C","url":null,"abstract":"In the past three decades, earnings have been one of the most researched variables in accounting. Empirical research provided substantial evidence on its usefulness in the capital markets but evidence in predicting earnings has been limited, yielding inconclusive results. The purpose of this study is to validate and extend prior research in predicting earnings by examining aggregate and industry-specific data. A sample of 10,509 firm-year observations included in the Compustat database for the period 1982–91 is used in the study. The stepwise logistic regression results of the present study indicated that nine earnings and non-earnings variables can be used to predict earnings. These predictor variables are not identical to those reported in prior studies. These results are also extended to the manufacturing industry. Two new variables are identified to be significant in this industry. Moreover, an Artificial Neural Network (ANN) approach is employed to complement the logistic regression results. The ANN model's performance is at least as high as the logistic regression model's predictive ability. © 1996 Wiley Periodicals, Inc.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"21 6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126942278","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: 16

Automated Integration of Enterprise Accounting Models Throughout the Systems Development Life Cycle 企业会计模型在整个系统开发生命周期的自动化集成

Intell. Syst. Account. Finance Manag. Pub Date : 1996-09-01 DOI: 10.1002/(SICI)1099-1174(199609)5:3%3C113::AID-ISAF106%3E3.0.CO;2-Y

G. Geerts, W. McCarthy, Stephen R. Rockwell

引用次数: 15

Belief Networks for Expert System Development in Auditing

Intell. Syst. Account. Finance Manag. Pub Date : 1996-09-01 DOI: 10.1002/(SICI)1099-1174(199609)5:3%3C147::AID-ISAF108%3E3.0.CO;2-F

S. Sarkar, R. Sriram, Shibu Joykutty

{"title":"Belief Networks for Expert System Development in Auditing","authors":"S. Sarkar, R. Sriram, Shibu Joykutty","doi":"10.1002/(SICI)1099-1174(199609)5:3%3C147::AID-ISAF108%3E3.0.CO;2-F","DOIUrl":"https://doi.org/10.1002/(SICI)1099-1174(199609)5:3%3C147::AID-ISAF108%3E3.0.CO;2-F","url":null,"abstract":"This study examines the use of a belief network based expert system for an auditing task—financial distress evaluation for banks. A belief network uses probability measures to store important dependencies across variables of interest in a problem domain, and makes inferences based on observed evidence using probability calculus. This paper discusses how belief network structures can be constructed, and used to assist auditor's in making appropriate recommendations regarding the financial health of a bank under audit. The ability of a belief network to make reliable predictions depends on how well the network structure reflects the underlying dependencies across variables in the problem domain (e.g. financial ratios and the financial health of a bank). The first part of this study illustrates how a computer program developed by the authors can be used to generate and evaluate different feasible belief network structures based on historical data. The program uses an information-theoretic measure to compare the alternative structures. The ability of the program to identify existing dependencies across variables is demonstrated by using it to reconstruct a known network structure from simulated data. Next, the program is used on a database of twelve important bank financial ratios over a three-year period. The predictive ratios identified by the program reflect important areas of a bank's health, such as loan quality, efficiency, profitability and capital adequacy. Finally, a belief revision mechanism is encoded for the belief network structure identified earlier, and is used to illustrate how it can assist auditors in making recommendations about financial health based on a bank's critical financial ratios. The probability estimates provided by the system are validated using data on banks not used in the network design stage, and are found to be reliable. © 1996 Wiley Periodicals, Inc.","PeriodicalId":153549,"journal":{"name":"Intell. Syst. Account. Finance Manag.","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"1996-09-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129158850","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: 8