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

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 the non-stationary and non-linear attributes embedded in bulky sets of microstructure market data. The model is able to decompose and analyze the complex signal depicted by the data resulting from the interaction of short-term and long-term memory processes. 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 modeling and forecasting performance of all six models. A Jordan net that used as input the coefficients resulting from a non-decimated Haar 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 hybrid model. The model validation process included scenarios of extreme market operation conditions such as the market crash of 2008. The methodology 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.