Evaluating Neural Network Predictors by Bootstrapping
AbstractWe present a new method, inspired by the bootstrap, whose goal it is to determine the quality and reliability of a neural network predictor. Our method leads to more robust forecasting along with a large amount of statistical information on forecast performance that we exploit. We exhibit the method in the context of multi-variate time series prediction on financial data from the New York Stock Exchange. It turns out that the variation due to different resamplings (i.e., splits between training, cross-validation, and test sets) is significantly larger than the variation due to different network conditions (such as architecture and initial weights). Furthermore, this method allows us to forecast a probability distribution, as opposed to the traditional case of just a single value at each time step. We demonstrate this on a strictly held-out test set that includes the 1987 stock market crash. We also compare the performance of the class of neural networks to identically bootstrapped linear models.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by EconWPA in its series Finance with number 9411002.
Date of creation: 14 Nov 1994
Date of revision:
Contact details of provider:
Web page: http://18.104.22.168
Other versions of this item:
- Andreas S. WEIGEND & Blake LeBARON, 1994. "Evaluating Neural Network Predictors by Bootstrapping," SFB 373 Discussion Papers 1994,35, Humboldt University of Berlin, Interdisciplinary Research Project 373: Quantification and Simulation of Economic Processes.
- G - Financial Economics
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Gallant, A Ronald & Rossi, Peter E & Tauchen, George, 1993. "Nonlinear Dynamic Structures," Econometrica, Econometric Society, vol. 61(4), pages 871-907, July.
- Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
- Estanislao Arana & Pedro Delicado & Luis Martí, 1999. "Validation procedures in radiological diagnostic models. Neural network and logistic regression," Economics Working Papers 414, Department of Economics and Business, Universitat Pompeu Fabra.
- D. Guegan & L. Mercier, 2005. "Prediction in chaotic time series: methods and comparisons with an application to financial intra-day data," The European Journal of Finance, Taylor & Francis Journals, vol. 11(2), pages 137-150.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (EconWPA).
If references are entirely missing, you can add them using this form.