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FinGAT: Financial Graph Attention Networks for Recommending Top-K Profitable Stocks
Yi Ling Hsu
, Yu Che Tsai
,
Cheng Te Li
資訊工程學系
研究成果
:
Article
›
同行評審
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引文 斯高帕斯（Scopus）
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Keyphrases
Financial Knowledge Graph
100%
Graph Attention Network
100%
Stock Prices
50%
Fully Connected Graph
33%
Financial Technology
33%
Latent Interaction
33%
Information Sharing
16%
Taiwan
16%
Sequential Pattern
16%
Stock Price Prediction
16%
State-of-the-art Techniques
16%
Modeling Time
16%
Learning Components
16%
S&P 500
16%
Deep Learning Model
16%
NASDAQ
16%
Recommendation Performance
16%
Stock Recommendations
16%
Hierarchical Learning
16%
Conventional Stocks
16%
Return Ratio
16%
Stock Time Series
16%
Stock Sectors
16%
Stock Movement
16%
Multi-task Objective
16%
Stock Analysis
16%
Engineering
Deep Learning Method
100%
Conducted Experiment
50%
State-of-the-Art Method
50%
Shared Information
50%
Target Technology
50%
Computer Science
Graph Attention Network
100%
Deep Learning Method
33%
Connected Graph
33%
Sequential Pattern
16%
Learning Component
16%
Shared Information
16%
Financial Model
16%
Target Technology
16%
Earth and Planetary Sciences
Time Series
100%
Taiwan
33%
State of the Art
33%
Economics, Econometrics and Finance
Financial Network
100%
Information Sector
25%
Medicine and Dentistry
Time Series Analysis
100%