# Gradient Boost with Convolution Neural Network for Stock Forecast

arXiv: https://arxiv.org/abs/1909.09563 · HTML: https://mlsanity.com/paper/1909.09563/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Sep 2019.

## Built on by (most cited first, 1 of 1)

- [WhACC: Whisker Automatic Contact Classifier with Expert Human-Level Performance](https://mlsanity.com/paper/2501.06219/) (2025)

## Abstract (arXiv)

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.