Pearson Correlation Coefficient-Based Performance Enhancement of Broad Learning System for Stock Price Prediction

Abstract

Accurate prediction of a stock price is a challenging task due to the complexity, chaos, and non-linearity nature of financial systems. In this brief, we proposed a multi-indicator feature selection method for stock price prediction based on Pearson correlation coefficient (PCC) and Broad Learning System (BLS), named the PCC-BLS framework. Firstly, PCC was used to select the input features from 35 features, including original stock price, technical indicators, and financial indicators. Secondly, these screened input features were used for rapid information feature extraction and training a BLS. Four stocks recorded on the Shanghai Stock Exchange or Shenzhen Stock Exchange were adopted to evaluate the performance of the proposed method. In addition, we compared the forecasting results with ten machine learning methods, including Support Vector Regression (SVR), Adaptive Boosting (Adaboost), Bootstrap aggregating (Bagging), Random Forest (RF), Gradient Boosting Decision Tree (GBDT), Multi-layer Perceptron (MLP), Convolutional Neural Network (CNN), and Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU) and Broad Learning System (BLS). Among all algorithms used in this brief, the proposed model showed the best performance with the highest model fitting ability.