Forecasting with artificial neural networks: the state of the art

Neural networks are being used in areas of prediction and classification, the areas where statistical methods have traditionally been used. Both the traditional statistical methods and neural networks are looked upon as competing model-building techniques in literature. This paper carries out a comprehensive review of articles that involve a comparative study of feed forward neural networks and statistical techniques used for prediction and classification problems in various areas of applications. Tabular presentations highlighting the important features of these articles are also provided. This study aims to give useful insight into the capabilities of neural networks and statistical methods used in different kinds of applications.

Review: Neural networks and statistical techniques: A review of applications

Neural networks are used in business organizations for providing intelligent decision support. The ABI/Inform bibliographic database has over 1 000 publications on business-related research in neur...

A bibliometric analysis

The prediction of corporate bankruptcies is an important and widely studied topic since it can have significant impact on bank lending decisions and profitability. This work presents two contributions. First we review the topic of bankruptcy prediction, with emphasis on neural-network (NN) models. Second, we develop an NN bankruptcy prediction model. Inspired by one of the traditional credit risk models developed by Merton (1974), we propose novel indicators for the NN system. We show that the use of these indicators in addition to traditional financial ratio indicators provides a significant improvement in the (out-of-sample) prediction accuracy (from 81.46% to 85.5% for a three-year-ahead forecast).

/pdf/bankruptcy-prediction-for-credit-risk-using-neural-networks-2mx7c9zrvh.pdf

Bankruptcy prediction for credit risk using neural networks: A survey and new results

During the last decade, neural networks have established themselves as a theoretically sound alternative to traditional statistical models, and a large body of research on their application to business has been produced. The comprehensive range of business and financial applications is such that a focus is required for an in-depth analysis, therefore this review addresses applications related to management, marketing and decision making. Also, given that previous reviews have dealt with earlier publications, the time span of the review is limited to the period 1992–1998. The presentation is centred on summary tables with links between them. These tables classify the studies according to their application areas, the main contributions rendered by the use of neural networks, and the alleged advantages and disadvantages of this, as well as the journal of publication. Further information on the neural network models, other statistical methods against which they have been compared, and features of the analysed data are also provided. The more controversial issues concerning real-world applications of neural networks are discussed as a part of a critical analysis. Many of the studies are shown to be first attempts to apply these new techniques to established areas of research, whereas only a few tackle real-world cases. Although still regarded as a novel methodology, neural networks are shown to have matured to the point of offering real practical benefits in many of their applications.

Neural networks in business: a survey of applications (1992–1998)

A survey of journal articles on neural network business applications published between 1988 and 1995 indicates that an increasing amount of neural network research is being conducted for a diverse range of business activities. The classification of literature by (1) year of publication, (2) application area, (3) problem domain, (4) decision process phase, (5) level of management, (6) level of task interdependence, (7) means of development, (8) corporate/academic interaction in development, (9) technology integration, (10) comparative study, (11) major contribution, and (12) journal provides some insights into the trends in neural networks research. The implications for neural networks developers/researchers and suggestions on future research areas are discussed.

Neural network applications in business: a review and analysis of the literature (1988-95)

The purpose of this research is to present a comprehensive bibliography of neural network application research in business. One hundred and twenty-seven neural network application papers and reports are identified through exhaustive literature searches. A classification of these articles by application area reveals that neural networks are being used for a diverse range of corporate functional activities, particularly in the areas of production/operations and finance. Information on the neural network development language/tool, the learning paradigm and the computer operating environment as reported in each article is included. Those journals which have published the most neural network business applications are also presented.

A bibliography of neural network business applications research: 1988–September 1994

The efficacy of matching information systems development methodologies with application characteristics—an empirical study

In recent years, many Colleges and Universities in the USA have been facing a serious financial crisis since many state governments have reduced their support for higher education. There is no doubt that one of the solutions to this crisis depends on the successful implementation of University fund raising programs. Identifying the potential donors is an important part of this process. The objective of this research was to develop a cascade-correlation neural network to predict the types of people who would most likely be potential donors. A comparison of the classification accuracy between neural networks and multiple discriminant analyses (MDA) was also conducted. Our results indicated that neural networks could perform as well as MDA in overall accuracy. Furthermore, neural networks could predict with a lot more accuracy the actual donor (Type I hit) than MDA. Our study is the first published case study on the use of artificial neural networks for University fund raising.

The use of cascade-correlation neural networks in University fund raising

Three models of virtual organizations are (1) telecommuting companies, (2) companies that outsource all non-core competencies, and (3) completely virtual companies. Information technology plays a critical role in supporting all of these models. This paper provides valuable insights in the use of information technology to support the human resource management needs in these types of organizations.

/pdf/information-technology-in-virtual-organizations-a-needs-3ff9l0mfws.pdf

Thomas A. Bodnovich

Papers

Neural network applications in business: a review and analysis of the literature (1988-95)

A bibliography of neural network business applications research: 1988–September 1994

The efficacy of matching information systems development methodologies with application characteristics—an empirical study

The use of cascade-correlation neural networks in University fund raising

Information Technology in Virtual Organizations: A Needs Assessment from the Perspective of Human Resource Management