A comparison of selected artificial neural networks that help auditors evaluate client financial viability

Article Abstract:

Three artificial neural network approaches were compared for performance in assisting auditors to evaluate client financial viability.

Research, Auditing

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Artificial neural network models for pricing initial public offerings

Article Abstract:

Neural networks have been widely used in the management of financial services. However, one area that remains unexplored by neural networks is the pricing of initial public offerings. Application of neural networks to determine pricing of new issues shows their significant economic gains over traditional pricing models.

Models, Information management, Going public (Securities), Initial public offerings, Stock price forecasting

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The efficacy of neural networks in predicting returns on stock and bond indices

Article Abstract:

Neural network models may be used as an effective alternative in evaluating the presence of nonlinear relationship in large stocks, corporate bonds, small stocks, intermediate government bonds and other financial variables. Results revealed that neural networks are more reliable means of gauging linearity relationships compared to conventional Generalized Autoregressive Conditional Heteroskedasticity (GARCH) models, particularly if financial performance measures are used. However, neural forecasts generate insignificant results when small stocks and intermediate government bonds are considered.

Evaluation, Stocks, Bonds, Bonds (Securities)

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