research-article

Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network

Authors:

Huajun ChenAuthors Info & Claims

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

Pages 678 - 685

https://doi.org/10.1145/3308560.3317701

Published: 13 May 2019 Publication History

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.

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Cited By

Bao WCao YYang YChe HHuang JWen S(2025)Data-driven stock forecasting models based on neural networks: A reviewInformation Fusion10.1016/j.inffus.2024.102616113(102616)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102616
Wang KXu YLuo S(2024)TIGER: Training Inductive Graph Neural Network for Large-Scale Knowledge Graph ReasoningProceedings of the VLDB Endowment10.14778/3675034.367503917:10(2459-2472)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675039
Du KXing FMao RCambria E(2024)Financial Sentiment Analysis: Techniques and ApplicationsACM Computing Surveys10.1145/364945156:9(1-42)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1145/3649451
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Index Terms

Knowledge-Driven Stock Trend Prediction and Explanation via Temporal Convolutional Network
1. Computing methodologies
  1. Machine learning
    1. Machine learning approaches
      1. Neural networks
2. Information systems
  1. Information systems applications
    1. Data mining

Index terms have been assigned to the content through auto-classification.

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cover image ACM Other conferences

WWW '19: Companion Proceedings of The 2019 World Wide Web Conference

May 2019

1331 pages

ISBN:9781450366755

DOI:10.1145/3308560

Editors:
Ling Liu
Georgia Tech, USA
,
Ryen White
Microsoft Research, USA

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 13 May 2019

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WWW '19: The Web Conference

May 13 - 17, 2019

San Francisco, USA

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Cited By

Bao WCao YYang YChe HHuang JWen S(2025)Data-driven stock forecasting models based on neural networks: A reviewInformation Fusion10.1016/j.inffus.2024.102616113(102616)Online publication date: Jan-2025
https://doi.org/10.1016/j.inffus.2024.102616
Wang KXu YLuo S(2024)TIGER: Training Inductive Graph Neural Network for Large-Scale Knowledge Graph ReasoningProceedings of the VLDB Endowment10.14778/3675034.367503917:10(2459-2472)Online publication date: 1-Jun-2024
https://dl.acm.org/doi/10.14778/3675034.3675039
Du KXing FMao RCambria E(2024)Financial Sentiment Analysis: Techniques and ApplicationsACM Computing Surveys10.1145/364945156:9(1-42)Online publication date: 24-Apr-2024
https://dl.acm.org/doi/10.1145/3649451
Shi RHuang HYin KZhou WJin HBaeza-Yates RBonchi F(2024)Orthogonality Matters: Invariant Time Series Representation for Out-of-distribution ClassificationProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671768(2674-2685)Online publication date: 25-Aug-2024
https://dl.acm.org/doi/10.1145/3637528.3671768
Wang TDeng LWang CLian JYan YYuan NZhang QXiong HBaeza-Yates RBonchi F(2024)COMET: NFT Price Prediction with Wallet ProfilingProceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining10.1145/3637528.3671621(5893-5904)Online publication date: 25-Aug-2024
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Li XShang JZheng LWang QLiu DLiu XLi FCao WSun H(2024)IMTCN: An Interpretable Flight Safety Analysis and Prediction Model Based on Multi-Scale Temporal Convolutional NetworksIEEE Transactions on Intelligent Transportation Systems10.1109/TITS.2023.330898825:1(289-302)Online publication date: Jan-2024
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You ZZhang PZheng JCartlidge J(2024)Multi-Relational Graph Diffusion Neural Network with Parallel Retention for Stock Trends ClassificationICASSP 2024 - 2024 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)10.1109/ICASSP48485.2024.10447394(6545-6549)Online publication date: 14-Apr-2024
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Chen JXie LLin WWu YXu H(2024)Multi-Granularity Spatio-Temporal Correlation Networks for Stock Trend PredictionIEEE Access10.1109/ACCESS.2024.339377412(67219-67232)Online publication date: 2024
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Liu RLiu HHuang HSong BWu Q(2024)Multimodal multiscale dynamic graph convolution networks for stock price predictionPattern Recognition10.1016/j.patcog.2023.110211149(110211)Online publication date: May-2024
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Bi ZCheng SChen JLiang XXiong FZhang N(2024)RelphormerNeurocomputing10.1016/j.neucom.2023.127044566:COnline publication date: 21-Jan-2024
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