Japanese and Chinese Stock Market Behaviour in Comparison – an analysis of dynamic networks

The effect of self-organizing map architecture based on the value migration network centrality measures on stock return. Evidence from the US market

“…The main utilization of the correlation-based networks approach is to convert the multidimensional relationship matrix of the financial market into its sparse depiction. The stock correlation network is a subset of financial network that provides a deeper understanding of stock return time series [12][13][14][15][16][17][18][19][20], better predicts stock market behavior [21][22][23][24][25][26][27] and plays a significant role in portfolio optimalization [28][29][30][31][32][33][34][35], risk assessment [36][37][38][39], asset allocation [40,41]. In other words, correlation-based networks are a useful approach in economic decision-making [42] and can be regarded as the methodological basis of portfolio theory leading to efficient risk management.…”

Online network change point detection with missing values

“…Calderoni 2019), finance (e.g. Schuenemann et al 2020), economics (e.g. Wu et al 2020) and cybersecurity (e.g.…”

Sectoral Analysis of the US Stock Market through Complex Networks

“…Financial stock markets are well-defined complex systems (Mantegna 1999) consisting of many interacting elements (Mantegna and Stanley 1999;Huang et al, 2017). Complex networks are among the most widely used to investigate the crosscorrelation of the series of daily stock price returns (Esmaeilpour Moghadam et al, 2019;Schuenemann, Ribberink, and Katenka 2020;Zhuang and Xiu 2015;Chen et al, 2021). According to Onnela et al (2002), the stock correlation network can be considered as a set of nodes consisting of stocks and edges between nodes denoting relationships obtained from the correlation coefficients.…”