{"type":"ScholarlyArticle","title":"Deep Learning for Event-Driven Stock Prediction","authors":[{"name":"Xiao Ding","slug":"xiao-ding","given":"Xiao","family":"Ding","affiliation":""},{"name":"Yue Zhang","slug":"yue-zhang","given":"Yue","family":"Zhang","affiliation":""},{"name":"Ting Liu","slug":"ting-liu","given":"Ting","family":"Liu","affiliation":""},{"name":"Junwen Duan","slug":"junwen-duan","given":"Junwen","family":"Duan","affiliation":""}],"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.","doi":null,"url":"https://ijcai.org/proceedings/2015/329","pdf":"https://ijcai.org/proceedings/2015/0329.pdf","markdown":"https://ijcai.org/proceedings/2015/329.md","bibtex":"https://ijcai.org/proceedings/2015/bibtex/329","volume":{"slug":"2015","title":"Proceedings of the Twenty-Fourth International Joint Conference on Artificial Intelligence","short_title":"IJCAI 2015","url":"https://ijcai.org/proceedings/2015/","year":2015,"location":"Buenos Aires, Argentina","dates":"25–31 July 2015","editors":"Qiang Yang and Michael Wooldridge","isbn":"978-1-57735-738-4","publisher":"International Joint Conferences on Artificial Intelligence","doi_prefix":""},"section":"Web Mining","track":"Main Track","number":329,"pages":{"first":2327,"last":2332},"keywords":[],"videos":[],"references":[],"license_note":"Copyright International Joint Conferences on Artificial Intelligence."}