Forecasting Austrian IPOs: an application of linear and neural network error-correction models

Article Abstract:

The time series properties and the relationship between a newly built Initial Public Offering (IPO) Index and the Austrian Traded Index were examined with respect to autocorrelation, volatility and causality patterns. The econometric analysis facilitate the development of linear and multilayer feedforward neural network error-correction models for predicting the returns of Austrian IPOs. The results showed that the neural network model performed better than the buy-and-hold and moving average models of trading.

Forecasts and trends, Econometrics, Going public (Securities), Initial public offerings, Stock price forecasting, Linear models (Statistics), Prediction theory

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A robust neural network filter for electricity demand prediction

Article Abstract:

The modelling of one-hour-ahead hourly forecasting of electric power demand, for Seattle's Puget Power Demand, is addressed. Robust neural networks perform better than non-robust neural networks, as observed from a lower mean square error in about half of the out-of-sample test data. Robust neural networks are created by filtering the level shifts and outliers from the data, and the predictions are then derived on the 'clean' data.

Energy consumption, Demand functions (Economics), Demand functions, Kalman filtering

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