Predicting Stock Movements: Using Multiresolution Wavelet Reconstruction and Deep Learning in Neural Networks

Stock movement prediction is important in the financial world because investors want to observe trends in stock prices before making investment decisions. However, given the non-linear non-stationary financial time series characteristics of stock prices, this remains an extremely challenging task. A wavelet is a mathematical function used to divide a given function or continuous-time signal into different scale components. Wavelet analysis has good time-frequency local characteristics and good zooming capability for non-stationary random signals. However, the application of the wavelet theory is generally limited to a small scale. The neural networks method is a powerful tool to deal with large-scale problems. Therefore, the combination of neural networks and wavelet analysis becomes more applicable for stock behavior prediction. To rebuild the signals in multiple scales, and filter the measurement noise, a forecasting model based on a stock price time series was provided, employing multiresolution analysis (MRA). Then, the deep learning in the neural network method was used to train and test the empirical data. To explain the fundamental concepts, a conceptual analysis of similar algorithms was performed. The data set for the experiment was chosen to capture a wide range of stock movements from 1 January 2009 to 31 December 2017. Comparison analyses between the algorithms and industries were conducted to show that the method is stable and reliable. This study focused on medium-term stock predictions to predict future stock behavior over 11 days of horizons. Our test results showed a 75% hit rate, on average, for all industries, in terms of US stocks on FORTUNE Global 500. We confirmed the effectiveness of our model and method based on the findings of the empirical research. This study’s primary contribution is to demonstrate the reconstruction model of the stock time series and to perform recurrent neural networks using the deep learning method. Our findings fill an academic research gap, by demonstrating that deep learning can be used to predict stock movement