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

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.