TY - JOUR
T1 - A neuro-wavelet model for the short-term forecasting of high-frequency time series of stock returns
AU - Ortega, Luis
AU - Khashanah, Khaldoun
PY - 2014/3
Y1 - 2014/3
N2 - 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.
AB - 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.
KW - high-frequency financial data
KW - neuro-wavelets
KW - recurrent neural networks
KW - time series forecasting
KW - wavelet multi-resolution decomposition
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U2 - 10.1002/for.2270
DO - 10.1002/for.2270
M3 - Article
AN - SCOPUS:84894900843
SN - 0277-6693
VL - 33
SP - 134
EP - 146
JO - Journal of Forecasting
JF - Journal of Forecasting
IS - 2
ER -