Abstract
Deep neural networks have achieved promising results in stock trend prediction. However, most of these models have two common drawbacks, including (i) current methods are not sensitive enough to abrupt changes of stock trend, and (ii) forecasting results are not interpretable for humans. To address these two problems, we propose a novel Knowledge-Driven Temporal Convolutional Network (KDTCN) for stock trend prediction and explanation. Firstly, we extract structured events from financial news, and utilize external knowledge from knowledge graph to obtain event embeddings. Then, we combine event embeddings and price values together to forecast stock trend. We evaluate the prediction accuracy to show how knowledge-driven events work on abrupt changes. We also visualize the effect of events and linkage among events based on knowledge graph, to explain why knowledge-driven events are common sources of abrupt changes. Experiments demonstrate that KDTCN can (i) react to abrupt changes much faster and outperform state-of-the-art methods on stock datasets, as well as (ii) facilitate the explanation of prediction particularly with abrupt changes.
Original language | English |
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Title of host publication | WWW '19: Companion Proceedings of The 2019 World Wide Web Conference |
Place of Publication | New York, NY, USA |
Publisher | ACM Association for Computing Machinery |
Pages | 678–685 |
Number of pages | 8 |
ISBN (Print) | 9781450366755 |
DOIs | |
Publication status | Published - 13 May 2019 |
Event | The Web Conference 2019 - San Francisco, United States Duration: 13 May 2019 → 17 May 2019 https://www2019.thewebconf.org/ |
Conference
Conference | The Web Conference 2019 |
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Country/Territory | United States |
City | San Francisco |
Period | 13/05/19 → 17/05/19 |
Internet address |
Keywords / Materials (for Non-textual outputs)
- stock trend prediction
- predictive analytics
- unstructured
- explanation
- event extraction
- structured
- Knowledge-driven