Publication Type

Journal Article

Version

publishedVersion

Publication Date

10-2024

Abstract

Accurately predicting financial entity performance remains a challenge due to the dynamic nature of financial markets and vast unstructured textual data. Financial knowledge graphs (FKGs) offer a structured representation for tackling this problem by representing complex financial relationships and concepts. However, constructing a comprehensive and accurate financial knowledge graph that captures the temporal dynamics of financial entities is non-trivial. We introduce FintechKG, a comprehensive financial knowledge graph developed through a three-dimensional information extraction process that incorporates commercial entities and temporal dimensions and uses a financial concept taxonomy that ensures financial domain entity and relationship extraction. We propose a temporal and relational graph convolutional network (RGCN)-based representation for FintechKG data across multiple timesteps, which captures temporal dependencies. This representation is then combined with FinBERT embeddings through a projection layer, enabling a richer feature space. To demonstrate the efficacy of FintechKG, we evaluate its performance using the example task of financial performance prediction. A logistic regression model uses these combined features and social media embeddings for performance prediction. We classify whether the revenue will increase or decrease. This approach demonstrates the effectiveness of FintechKG combined with textual information for accurate financial forecasting. Our work contributes a systematic FKG construction method and a framework that utilizes both relational and textual embeddings for improved financial performance prediction.

Keywords

knowledge graph, finance, BERT, tweets, text, LSTM, RGCN, news, NLP, commercial entities, concept entities

Discipline

Databases and Information Systems | Finance and Financial Management

Research Areas

Data Science and Engineering

Publication

Machine Learning and Knowledge Extraction

Volume

6

Issue

4

First Page

2303

Last Page

2320

Identifier

10.3390/make6040113

Publisher

MDPI

Copyright Owner and License

Author-CC-BY

Additional URL

https://doi.org/10.3390/make6040113

Download

Share

COinS