A neuro-wavelet model for the short-term forecasting of high-frequency time series of stock returns

Luis Ortega, Khaldoun Khashanah

Research output: Contribution to journalArticlepeer-review

46 Scopus citations

Abstract

We propose a wavelet neural network (neuro-wavelet) model for the short-term forecast of stock returns from high-frequency financial data. The proposed hybrid model combines the capability of wavelets and neural networks to capture non-stationary nonlinear attributes embedded in financial time series. A comparison study was performed on the predictive power of two econometric models and four recurrent neural network topologies. Several statistical measures were applied to the predictions and standard errors to evaluate the performance of all models. A Jordan net that used as input the coefficients resulting from a non-decimated wavelet-based multi-resolution decomposition of an exogenous signal showed a consistent superior forecasting performance. Reasonable forecasting accuracy for the one-, three- and five step-ahead horizons was achieved by the proposed model. The procedure used to build the neuro-wavelet model is reusable and can be applied to any high-frequency financial series to specify the model characteristics associated with that particular series.

Original languageEnglish
Pages (from-to)134-146
Number of pages13
JournalJournal of Forecasting
Volume33
Issue number2
DOIs
StatePublished - Mar 2014

Keywords

  • high-frequency financial data
  • neuro-wavelets
  • recurrent neural networks
  • time series forecasting
  • wavelet multi-resolution decomposition

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