Integrating Fundamental and Technical Analysis of Stock Market through Multi-layer Perceptron

We use Multi-layer Perceptron and propose a hybrid model of fundamental and technical analysis by utilizing stock prices (from 2012–06 to 2017–12) and financial ratios of Technology companies listed on Nasdaq. Our model uses data discretization and feature selection preprocesses. The best results are obtained through topology optimizations using a three-hidden layer MLP. We examine the predictability of our hybrid model through a training/test split and cross-validation. It is found that the hybrid model successfully predicts the future stock movements. Our model results in the greatest average directional accuracy (65.87%) compared to the results obtained from the fundamental and technical analysis in isolation. The numerical results provide enough evidence to conclude that the market is not perfectly efficient.