Using Deep Learning for price prediction by exploiting stationary limit order book features

The recent surge in Deep Learning (DL) research of the past decade has successfully provided solutions to many difficult problems. The field of quantitative analysis has been slowly adapting the new methods to its problems, but due to problems such as the non-stationary nature of financial data, significant challenges must be overcome before DL is fully utilized. In this work a new method to construct stationary features, that allows DL models to be applied effectively, is proposed. These features are thoroughly tested on the task of predicting mid price movements of the Limit Order Book. Several DL models are evaluated, such as recurrent Long Short Term Memory (LSTM) networks and Convolutional Neural Networks (CNN). Finally a novel model that combines the ability of CNNs to extract useful features and the ability of LSTMs' to analyze time series, is proposed and evaluated. The combined model is able to outperform the individual LSTM and CNN models in the prediction horizons that are tested

Hybrid wavelet adaptive neuro-fuzzy tool supporting competitiveness and efficiency of predicting the stock markets of the Visegrad Four countries

The stock market is influenced by many factors and predicting its development is a difficult and complicated task. Successful and accurate stock market prediction is absolutely essential for countries’ economies as it helps to create a competitive advantage for listed companies through economies of scale. The aim of the paper is to decompose and denoise stock time series using wavelet analysis, detect a smoothed trend and predict future development using an adaptive neuro-fuzzy model. This hybrid fusion model is also referred to as the WANFIS model. The application of the WANFIS model is carried out on less developed stock markets, specifically on the official stock market indices of the Visegrad countries, namely the Czech Republic, Slovak Republic, Poland and Hungary. Recently, wavelet analysis has been among the most promising mathematical tools, which can be used to easily decompose continuous signals or time series in the time and frequency domains. The results show that the proposed WANFIS hybrid model demonstrates a more accurate prediction of the development of stock indices than individual models alone. Experimental results show that the fusion model provides a promising and effective tool for predicting even less liquid and less efficient stock markets, such as those in the V4 countries. A useful and accurate prediction alternative proven in emerging stock markets is offered