Papers with stock market prediction

2 papers
Group, Extract and Aggregate: Summarizing a Large Amount of Finance News for Forex Movement Prediction (D19-51)

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Challenge: Existing studies on forex prediction ignore related text completely and focus on forex trade data only, which loses important semantic information.
Approach: They propose a BERT-based Hierarchical Aggregation Model to summarize forex news . they group news from different aspects and extract the most crucial news in each group .
Outcome: The proposed model outperforms baseline methods and grouping methods and summarizes the influence patterns for forex trading.
Event Representation Learning Enhanced with External Commonsense Knowledge (D19-1)

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Challenge: Existing methods to learn event representations from text lack commonsense knowledge about the intents and emotions of event participants.
Approach: They propose to leverage external commonsense knowledge about the intent and sentiment of the event to learn distributed representations for structured events from text.
Outcome: The proposed model improves on hard similarity tasks and yields more precise inferences on subsequent events under given contexts.

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