Prediction of Stock Price Movement Using Knowledge Graph Embeddings

Eric Kwamena Asibu Yartey¹

Section:Research Paper, Product Type: Journal-Paper
Vol.7 , Issue.6 , pp.12-19, Dec-2020

Online published on Dec 31, 2020

Copyright © Eric Kwamena Asibu Yartey . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Given the advancement of artificial intelligence and increased computational capabilities to handle a massive amount of web Information, Knowledge graphs (KG) and Knowledge graphs (KG) embeddings have proven useful in modelling and representing factual information in various domains such as medical diagnosis, web search, semantic parsing, speech recognition, question answering, named entity disambiguation, link prediction, recommendation system among others. While TransE model has been adopted by some researchers in the field of finance to generate knowledge graph embeddings for their proposed stock prediction models, it has also been under criticism. Onuki et al. had claimed in their research work that their proposed KGML model outperforms TransE model in terms of predictive accuracy, learning speed, and convergence speed of learning. This study evaluates the effect that learned embedding vectors from TransE and KGML models have on the performance of a TCN stock prediction model. Results have shown that KG embeddings are powerful predictors, and the overall best KG embeddings are that of the TransE model.

Key-Words / Index Term :
Knowledge Graph, Knowledge Graph Embeddings, TransE Model, KGML Model, Temporal Convolution Network (TCN), Stock Price Movement Prediction

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