Paper Info
Title
Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network
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.
Year
DOI
Venue
2019
10.1145/3308560.3317701
Companion Proceedings of The 2019 World Wide Web Conference
Keywords
Field
DocType
Knowledge-driven, event extraction, explanation, predictive analytics, stock trend prediction, structured, unstructured
Data mining,Knowledge graph,Financial news,Computer science,Predictive analytics,Software,Stock trend prediction,Deep neural networks
Conference
ISBN
Citations 
PageRank 
978-1-4503-6675-5
7
0.60
References 
Authors
0
6
Name
Order
Citations
PageRank
Shumin Deng13210.61
Ningyu Zhang26318.56
wen zhang3387.69
J Chen413930.64
J. Z. Pan5254.59
Huanhuan Chen6731101.79