Enhancing Exchange-Traded Fund Price Predictions: Insights from Information-Theoretic Networks and Node Embeddings

Autor:	Insu Choi, Woo Chang Kim
Jazyk:	angličtina
Rok vydání:	2024
Předmět:	mutual information transfer entropy machine learning node embedding centrality measure explainable artificial intelligence (xAI) Science Astrophysics QB460-466 Physics QC1-999
Zdroj:	Entropy, Vol 26, Iss 1, p 70 (2024)
Druh dokumentu:	article
ISSN:	26010070 1099-4300
DOI:	10.3390/e26010070
Popis:	This study presents a novel approach to predicting price fluctuations for U.S. sector index ETFs. By leveraging information-theoretic measures like mutual information and transfer entropy, we constructed threshold networks highlighting nonlinear dependencies between log returns and trading volume rate changes. We derived centrality measures and node embeddings from these networks, offering unique insights into the ETFs’ dynamics. By integrating these features into gradient-boosting algorithm-based models, we significantly enhanced the predictive accuracy. Our approach offers improved forecast performance for U.S. sector index futures and adds a layer of explainability to the existing literature.
Databáze:	Directory of Open Access Journals
Externí odkaz:	https://doaj.org/article/59fa23f4efa946a1a0b87108a9afbf5c Zobrazit plný text záznamu View record in DOAJ Plný text ve formátu PDF Plný text ve formátu HTML
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