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

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.

I. Introduction

Forecasting stock prices is of significant importance for analyzing financial systems, as the stock market reflects the economic situation of a country, even the world. Accurate prediction of the stock market allows investors and other stakeholders to understand the movement of the financial market. Effective trading strategies are then adopted to achieve greater profits as well as returns with less risk [1]. In general, the stock market is often analyzed by researchers as a complex system [2]–[4]. However, the non-linearity, high noise and volatility of the stock market make the prediction of financial systems take higher risks than to other complex systems [5]. In addition, external factors such as national macro adjustments, changes from the political situation and investor psychology make the prediction of the financial system extremely difficult [6], [7]. Data-driven methods show great potential for the analysis and control of such complex systems [8], [9].