Comparison of Variant Principal Component Analysis Using New RNN-based Framework for Stock Prediction

Abstract

Due to the high complexity and dimensionality of stock price data, high volatility and uncertainty cause an over-fitting problem in predicting stock price. In this paper, we compare different combinations of variant Principal Component Analysis (PCA) methods and Recurrent Neural Network (RNN) based frameworks that can be applied to extract high-level features from a rich set of initial variables for producing 2-year ahead forecasts of the daily stock price of IBM and Wells Fargo. The objective of our comparative study is to find which combination performs best in forecasting different stock price data. The experimental results show that the best combination for IBM is 2-Directional 2-Dimensional PCA (2d2d-PCA) with Long Short-Term Memory (LSTM), which achieves accuracy of 91.77% based on R-Square (R²) and for Wells Fargo is 2d2d-PCA with Gated Recurrent Unit (GRU), which achieves accuracy of 92.47%. Our comparative study indicates that the best combination of dimension reduction method and deep learning model is unfixed for predicting different stock price data. In addition, it is confirmed that GRU is an efficient model in predicting stock price; and 2d2d-PCA performs as well as PCA with respect to other dimension reduction methods with less error.