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Found 4 papers, 1 papers with code
Deep Attentive Learning for Stock Movement Prediction From Social Media Text and Company Correlations
In the financial domain, risk modeling and profit generation heavily rely on the sophisticated and intricate stock movement prediction task.
Ranked #1 on
Stock Market Prediction
on stocknet
(using extra training data)
Quantitative Day Trading from Natural Language using Reinforcement Learning
It is challenging to design profitable and practical trading strategies, as stock price movements are highly stochastic, and the market is heavily influenced by chaotic data across sources like news and social media.
FAST: Financial News and Tweet Based Time Aware Network for Stock Trading
Designing profitable trading strategies is complex as stock movements are highly stochastic; the market is influenced by large volumes of noisy data across diverse information sources like news and social media.
GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion
Parliamentary debates present a valuable language resource for analyzing comprehensive options in electing representatives under a functional, free society.
