Neuro-wavelet Model for price prediction in high-frequency data in the Mexican Stock market

Abstract
With the availability of high frequency data and new techniques for the management of noise in signals, we revisit the question, can we predict financial asset prices? The present work proposes an algorithm for next-step log-return prediction. Data in frequencies from 1 to 15 minutes, for 25 high capitalization assets in the Mexican market were used. The model applied consists on a wavelet followed by a Long Short-Term Memory neural network (LSTM). Application of either wavelets or neural networks in finance are common, the novelty comes from the application of the particular architecture proposed. The results show that, on average, the proposed LSTM neuro-wavelet model outperforms both an ARIMA model and a benchmark dense neural network model. We conclude that, although further research (in other stock markets, at higher frequencies, etc.) is in order, given the ever increasing technical capacity of market participants, the inclusion of the LSTM neurowavelet model is a valuable addition to the market participant toolkit, and might pose an advantage to traditional predictive tools.