Analysing and forecasting co-movement between innovative and traditional financial assets based on complex network and machine learning

Yang Zhou, Chi Xie, Gang-Jin Wang, You Zhu and Gazi Uddin

Research in International Business and Finance, 2023, vol. 64, issue C

Abstract: We study the co-movement between innovative financial assets (i.e., FinTech-related stocks, green bonds and cryptocurrencies) and traditional assets. We construct a co-movement mode transmission network and discuss the network topology during the pre-COVID-19 and COVID-19 periods. We extract network topology information to predict the co-movement mode by machine learning algorithms. We further propose dynamic trading strategies based on the co-movement mode prediction. The empirical results show that (i) the evolution of co-movement is dominated by some key modes, and the mode transmission relies on intermediate modes and shows certain periodicity; (ii) the co-movement relationships are influenced by the ongoing COVID-19 outbreak; and (iii) the novel approach, which combines complex network and machine learning, is superior in co-movement mode prediction and can effectively bring diversification benefits. Our work provides valuable insights for market participants.

Keywords: Co-movement; Innovative financial assets; Complex network; Machine learning; Prediction (search for similar items in EconPapers)
JEL-codes: G11 G15 G17 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:riibaf:v:64:y:2023:i:c:s027553192200232x

DOI: 10.1016/j.ribaf.2022.101846

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