Hierarchical structure of the German stock market

Optimality problem of network topology in stocks market analysis

“…From the literature, we learn that until the present day, the standard practice to find MST is by using Kruskal's algorithm [3][4][5]7,8,12,[26][27][28][29][30][31][32] or Prim's algorithm [6,7,18,26,27,29,32]. Interestingly, those algorithms give the same result [29].…”

“…This is one of the advantages of Kruskal's algorithm that is usually used in stocks network analysis based on MST such as presented in the literature, for example, Refs. [3,4,7,12,13,21,30,34,40]. Based on that property, the merits of the proposed method can be seen in Section 4 where the promising results are presented and discussed.…”

Toward a generic representation of random variables for machine learning

“…We can notice its hierarchical structure already described in many papers, e.g. (Mantegna, 1999), (Brida and Risso, 2010), focusing on stock markets. There is information in distribution and in correlation, thus taking into account both information, i.e.…”

The network of the Italian stock market during the 2008–2011 financial crises

“…The New York Stock Exchange, for its large size and importance among world capital markets, has attracted the attention of several researchers (Heimo et al, 2007;Onnela et al, 2003;Tumminello et al 2005;Brida and Risso, 2010a;Gan and Djauhari, 2015). Further papers have applied network analysis to study different stock market economies (Huang et al, 2009;Tabak et al, 2010;Gałązka, 2011;Coronnello et al, 2005;Zhuang et al, 2008;Brida and Risso, 2010b;. More recently, researchers have directed their attention to analyzing a stock market's network at the time of financial market turbulence, such as that observed during the 2008-2009 US subprime financial crisis (Majapa and Gossel, 2016;Nobi et al, 2014;Khashanah and Miao, 2011;Wiliński et al, 2013).…”