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Neural Networks – originally inspired from Neuroscience – provide powerful models for statistical data analysis. Their most prominent feature is their ability to “learn” dependencies based on a finite number of observations.
- Computational Techniques for Modelling Learning in Economics
Computational Techniques for Modelling Learning in Economics...
- Computational Techniques for Modelling Learning in Economics
23 Μαρ 2021 · Our study uses the grey relational analysis (GRA) and artificial neural network (ANN) models for the prediction of consumer exchange-traded funds (ETFs). We apply eight variables, including the put/call ratio, the EUR/USD exchange rate, the volatility index, the Commodity Research Bureau Index (CRB), the short-term trading index, the New York ...
25 Μαρ 2016 · In this chapter, we present a practical framework for the design of a neural network for solving problems in economic regression, classification and time-series forecasting. We present examples of the use neural networks using theoretical and real data.
Neural Networks in Economics: Background, Applications and New Developments. Peter Bollmann. 1998. Neural Networks have been developed in the sixties as a device for classificationand pattern recognition.
Based on this, this paper builds an integrated model by embedding neural networks into the GARCH-MIDAS model to consider the effect of historical innovation in clean energy ETF returns on future volatility.
The chapters of this dissertation explore the theoretical and empirical potential of neural networks and deep learning as estimation techniques in economics. The rst chapter pro-vides a novel approximation result for two hidden layer neural networks that makes clear the trade-o between width and depth.
1 Οκτ 2013 · Our results show that the back propagation network model outperforms the recurrent neural network model in predicting both high technology and non-high technology ETFs.
