---
title: "Deep Learning for Event-Driven Stock Prediction"
authors:
  - "Xiao Ding"
  - "Yue Zhang"
  - "Ting Liu"
  - "Junwen Duan"
doi: ""
url: "https://ijcai.org/proceedings/2015/329"
pdf: "https://ijcai.org/proceedings/2015/0329.pdf"
venue: "Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence"
year: 2015
pages: "2327-2332"
track: "Main Track"
keywords:
---

# Deep Learning for Event-Driven Stock Prediction

Xiao Ding, Yue Zhang, Ting Liu, Junwen Duan

*Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence*, pages 2327-2332.

## Abstract

We propose a deep learning method for event-driven stock market prediction. First, events are extracted from news text, and represented as dense vectors, trained using a novel neural tensor network. Second, a deep convolutional neural network is used to model both short-term and long-term influences of events on stock price movements. Experimental results show that our model can achieve nearly 6% improvements on S&P 500 index prediction and individual stock prediction, respectively, compared to state-of-the-art baseline methods. In addition, market simulation results show that our system is more capable of making profits than previously reported systems trained on S&P 500 stock historical data.