|PDF Title||:||Neural Networks in Finance|
|Author||:||Paul D. McNelis|
|Total Page||:||261 Pages|
|PDF Size||:||3.37 MB|
|PDF Link||:||Read and Download|
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“When evaluating the regression, we first want to know how well a model fits the actual data used to obtain the estimates of the coefficients. In the neural network literature, this is known as supervised training. We supervise the network, insofar as we evaluate it by how well it fits actual data.
The first overall statistic is a measure of goodness of fit. The Hannan- Quinn information is a method for handicapping this measure for competing models that have different numbers of parameters. The other statistics relate to properties of the regression residuals.
If the model is indeed a good fit, and thus well specified, then there should be nothing further to learn from the residuals. The residuals should simply represent “white noise,” or uncorrelated meaningless information like listening to a fan or air conditioner, which we readily and easily ignore.”
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