Adopting a dendritic neural model for predicting stock price index movement

Financial time series forecasting has been an attractive application of machine learning techniques because an advanced forecasting method can help to accurately predict price changes in markets and make good trading profits. In this study, an emerging machine learning approach, named the dendritic neuron model (DNM), is innovatively applied to forecast financial time series. To pursue better prediction performance, a novel scale-free differential evolution (SFDE) is defined as the training algorithm of the DNM, which can well control the balance between exploration and exploitation. In addition, the maximum Lyapunov exponent is used to detect the chaotic property of financial time series; then, the series is reconstructed into a phase space with high dimension before the prediction, where the time delay of the phase space is calculated by a mutual information method and the embedding dimension is separately determined by a false nearest neighbors approach. In our experiments, eight benchmark stock price indices selected from developed markets and emerging markets are used to validate the effectiveness and efficiency of the proposed forecasting model. Overall, the experimental results illustrate that the DNM trained by the SFDE algorithm yields better forecasting performances than other prevailing models and that it can be considered a reliable and satisfactory forecasting tool for predicting price changes in financial markets for practical applications.