Gradient Boost with Convolution Neural Network for Stock Forecast

TL;DR

This paper introduces a novel stock forecasting model that combines Convolutional Neural Networks and Gradient Boosting to improve prediction accuracy amidst noisy market data.

Contribution

The paper presents a new hybrid model integrating CNN and GBoost, demonstrating superior performance over existing methods in stock market prediction.

Findings

Outperforms current popular methods on six market indexes.

Effectively handles noisy and uncertain market data.

Shows improved accuracy in stock price change forecasting.

Abstract

Market economy closely connects aspects to all walks of life. The stock forecast is one of task among studies on the market economy. However, information on markets economy contains a lot of noise and uncertainties, which lead economy forecasting to become a challenging task. Ensemble learning and deep learning are the most methods to solve the stock forecast task. In this paper, we present a model combining the advantages of two methods to forecast the change of stock price. The proposed method combines CNN and GBoost. The experimental results on six market indexes show that the proposed method has better performance against current popular methods.