Forecasting Economic Data with Neural Networks

Aminian, Farzan; Suarez, E. Dante; Aminian, Mehran; Walz, Daniel T.

doi:10.1007/s10614-006-9041-7

Cited by 77 publications

(52 citation statements)

References 32 publications

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“…ANNs are more accurate than statistical models such as Multivariate Discriminant Analysis (MDA) and logit models in accuracy rate (Lee, Booth, & Alam, 2005;Tam, 1991) and ANNs are free of restrictive statistical assumptions (Aminian, Suarez, Aminian, & Walz, 2006).…”

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confidence: 99%

A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

Park

Hançer

2012

Tourism Economics

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Taking financial ratios as independent variables, this study used the framework of a neural network applied to hospitality firm bankruptcy, comparing the results to those of a logit model. Based on the empirical results of the two methodologies, the neural network obtained a higher accuracy rate than the logit model in an in-sample test. However, when tested with a holdout sample for verification, both models achieved a 100% accuracy rate. The study found that 'total liabilities to total assets' was a significant variable based on the results of both the t-test and logit analysis. Since hospitality firms are known for being highly leveraged, the conclusion can be drawn that extensive debt financing, when not accompanied by the competitive market value of equity, could play a pivotal role in forcing firms to file for bankruptcy.

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confidence: 99%

A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

Park

Hançer

2012

Tourism Economics

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“…It is this way of information processing by the brain that the ANN model tends to mimic. Although ANN models are too far from the way the human brain performs, by mimicking the basic features of the biological neural networks, they have succeeded in doing certain jobs very well [13][14][15].…”

Section: Methodsmentioning

confidence: 99%

“…The main purpose of this paper is to use the ANN technique to forecast inflation in Ghana for the period 2011: 01-2011:12 using the data between 1991: 01 and 2010:12.The forecast performance of the ANNs is also compared with their counterpart traditional models, AR (12) and VAR (14). The results show that the ANNs predict accurately than the econometric models.…”

Section: Some Central Banks Including Inflation-targeting Central Banmentioning

confidence: 99%

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Inflation Forecasting in Ghana-Artificial Neural Network Model Approach

Nunoo¹,

Kings²

2015

Int J Econ Manag Sci

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“…In particular, NN exercises a parameterized nonlinear function that can approximate nonlinear systems for prediction purpose. As far as the economic applications are concerned, NN has been widely applied to financial and macro-econometric areas (Aminian, et al, 2006;Binner, Gazely and Chen, 2002). The backpropagation neural network (BPN) model has been the most popular form of NN model used for forecasting (Mausumi et al, 1994;Hill et al, 1996).…”

Section: Introductionmentioning

confidence: 99%

Forecasting Inflation Rate of India using Neural Networks

Rani¹,

Haragopal²,

Reddy³

2017

IJCA

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In this paper, Inflation constitutes one of the major economic problems in emerging market economies that requires monetary authorities to elaborate tools and policies to prevent high volatility in prices and long periods of inflation. This paper outlines to forecast monthly inflation rate of India by using neural networks on the evaluation of set of variables. The data used for estimating the models for the period July 1994 to March 2008, for demonstration purpose of the two methodologies. The two neural network models Backpropagation neural network model or Backpropagation Network model (BPN model) and the recurrent neural network model (RNN model) under static and dynamic forecasts respectively are used in this study in forecasting inflation rate. The results of the neural network models under static and dynamic forecasts are also compared with the traditional econometric model. The results shows that the RNN model under the dynamic forecast is performing better than the BPN model under static forecast and the traditional econometric model in forecasting the inflation rate.

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Forecasting Economic Data with Neural Networks

Abstract: neural networks, nonlinear regression, economic data modeling, economic data forecasting,

Cited by 77 publications

References 32 publications

A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

A Comparative Study of Logit and Artificial Neural Networks in Predicting Bankruptcy in the Hospitality Industry

Inflation Forecasting in Ghana-Artificial Neural Network Model Approach

Forecasting Inflation Rate of India using Neural Networks

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