Neural network applications in business: A review and analysis of the literature (1988–1995)

Wong, Bo K.; Bodnovich, Thomas A.; Selvi, Yakup

doi:10.1016/s0167-9236(96)00070-x

Cited by 181 publications

(78 citation statements)

References 171 publications

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“…Training is a process in which the weighting of inter-layer connections is adjusted based on the training using classified data that are already known to achieve a more accurate classification of data with unknown categories. The majority of ANN are based on the multilayer feed-forward error back propagation algorithm [35], which is also the calculation method adopted in this study.…”

Section: Machine Learning Techniquesmentioning

confidence: 99%

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan

Shao

2015

Int. J. Emerg. Technol. Learn.

125

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Abstract-The dropout high rate is a serious problem in Elearning programs. Thus, it is a concern of education administrators and researchers. Predicting the dropout potential of students is a workable solution for preventing dropouts. Based on the analysis of related literature, this study selected students' personal characteristics and academic performance as input attributions. Prediction models were developed using Artificial Neural Network (ANN), Decision Tree (DT) and Bayesian Networks (BNs). A large sample of 62,375 students was utilized in the procedures of model training and testing. The results of each model were presented in a confusion matrix and were analyzed by calculating the rates of accuracy, precision, recall, and F-measure. The results suggested all of the three machine learning methods were effective for student dropout prediction, but DT presented a better performance. Finally, some suggestions were made for future research.

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Section: Machine Learning Techniquesmentioning

confidence: 99%

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Tan

Shao

2015

Int. J. Emerg. Technol. Learn.

125

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“…This survey includes a section on bankruptcy prediction. Also, the survey of Wong et al [56] on NNs in business applications includes some references on the bankruptcy prediction problem. Dimitras et al [15] provide a survey on the classical empirical approaches.…”

Section: B Neural-network (Nn) Approachesmentioning

confidence: 99%

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

Atiya

2001

IEEE Trans. Neural Netw.

565

243

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Abstract-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, 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).Index Terms-Asset-based model, bankruptcy prediction, corporate distress, corporate failure prediction, credit risk, default prediction, financial ratios, financial statement data, multilayer networks.

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“…According to a survey research conducted by Wong et al [21], more than 127 neural network business applications had been published in international journals up to September 1994. The number rose to 213 after a year [22]. This paper describes the application of neural networks in foreign exchange rates forecasting between American Dollar and "ve other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar.…”

Section: Introductionmentioning

confidence: 99%

A case study on using neural networks to perform technical forecasting of forex

2000

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This paper reports empirical evidence that a neural network model is applicable to the prediction of foreign exchange rates. Time series data and technical indicators, such as moving average, are fed to neural networks to capture the underlying`rulesa of the movement in currency exchange rates. The exchange rates between American Dollar and "ve other major currencies, Japanese Yen, Deutsch Mark, British Pound, Swiss Franc and Australian Dollar are forecast by the trained neural networks. The traditional rescaled range analysis is used to test the`e$ciencya of each market before using historical data to train the neural networks. The results presented here show that without the use of extensive market data or knowledge, useful prediction can be made and signi"cant paper pro"ts can be achieved for out-of-sample data with simple technical indicators. A further research on exchange rates between Swiss Franc and American Dollar is also conducted. However, the experiments show that with e$cient market it is not easy to make pro"ts using technical indicators or time series input neural networks. This article also discusses several issues on the frequency of sampling, choice of network architecture, forecasting periods, and measures for evaluating the model's predictive power. After presenting the experimental results, a discussion on future research concludes the paper.

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Neural network applications in business: A review and analysis of the literature (1988–1995)

Cited by 181 publications

References 171 publications

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

Prediction of Student Dropout in E-Learning Program Through the Use of Machine Learning Method

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

A case study on using neural networks to perform technical forecasting of forex

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