Financial News Event Prediction Using Temporal Knowledge Graphs
Publicerad
Författare
Typ
Examensarbete för masterexamen
Master's Thesis
Master's Thesis
Modellbyggare
Tidskriftstitel
ISSN
Volymtitel
Utgivare
Sammanfattning
Predicting future financial events and trends is crucial for effective decision-making
in financial markets. Traditional approaches utilizing Temporal Knowledge Graphs
(TKGs) primarily rely on specific relationship forecasting within knowledge graphs,
often falling short in capturing the broader, more dynamic shifts that characterize
industry trends. This thesis explores the feasibility of using TKGs in concert with
advanced machine learning to predict such trends, shifting the focus from specific
relationship predictions to identifying broader patterns in entity categories and relationship
types. We investigated multiple techniques, including adapting existing
graph convolutional network models and implementing custom model architectures
leveraging Transformers and Multi-Head Latent Attention. Performance was evaluated
across several publicly available TKG datasets. Our findings suggest that while
defining a clear and actionable trend within a TKG presents a significant challenge,
a trend-based approach using carefully curated datasets and appropriate machinelearning
architectures has the potential to improve prediction accuracy, particularly
when combined with methods addressing model generalization and data scarcity.
Future research directions include exploring more sophisticated trend definitions,
incorporating entity-specific category information, and further optimizing model architectures
for enhanced performance and reduced computational costs.
Beskrivning
Ämne/nyckelord
Financial Event Prediction, Temporal Knowledge Graphs (TKGs), Dynamic Knowledge Graphs (DKGs), Graph Convolutional Networks (GCNs), Transformers, Multi-Head Latent Attention (MLA), Time Series Analysis.