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Please use this identifier to cite or link to this item:
http://hdl.handle.net/2451/14769
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| Title: | A Bootstrap Evaluation of the Effect of Data Splitting on Financial Time Series |
| Authors: | LeBaron, Blake
Weigend, Andreas S. |
| Keywords: | Model evaluation
Model uncertainty
Bootstrap
Resampling
Financial forecasting
Time series prediction
Linear bias of early stopping
Superposition of forecasts
Model merging |
| Issue Date: | 1997 |
| Publisher: | Stern School of Business, New York University |
| Series/Report no.: | IS-97-013 |
| Abstract: | This article exposes problems of the commonly used technique of
splitting the available data into training, validation, and test sets
that are held fixed, warns about drawing too strong conclusions from
such static splits, and shows potential pitfalls of ignoring variability
across splits. Using a bootstrap or resampling method, we compare the
uncertainty in the solution stemming from the data splitting with neural
network specific uncertainties (parameter initialization, choice of
number of hidden units, etc.). We present two results on data from the
New York Stock Exchange. First, the variation due to different
resamplings is significantly larger than the variation due to different
network conditions. This result implies that it is important to not
over-interpret a model (or an ensemble of models) estimated on one
specific split of the data. Second, on each split, the neural network
solution with early stopping is very close to a linear model; no
significant nonlinearities are extracted. |
| URI: | http://hdl.handle.net/2451/14769 |
| Appears in Collections: | IOMS: Information Systems Working Papers
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