Reframing the S&P 500 network of stocks along the 21st century

Abstract

Since the beginning of the new millennium, stock markets went through every state from long-time troughs, trade suspensions to all-time highs. The literature on asset pricing hence assumes random processes to be underlying the movement of stock returns. Observed procyclicality and time-varying correlation of stock returns tried to give the apparently random behavior some sort of structure. However, common misperceptions about the co-movement of asset prices in the years preceding the Great Recession and the Global Commodity Crisis, is said to have even fueled the crisis' economic impact. Here we show how a varying macroeconomic environment influences stocks' clustering into communities. From a sample of 296 stocks of the S&P 500 index, distinct periods in between 2004 and 2011 are used to develop networks of stocks. The Minimal Spanning Tree analysis of those time-varying networks of stocks demonstrates that the crises of drove the market to clustered community structures in both periods, helping to restore the stock market's ceased order of the pre-crises era. However, a comparison of the emergent clusters with the General Industry Classification Standard conveys the impression that industry sectors do not play a major role in that order.

The Dynamics of Exchange Traded Funds: A Geometrical and Topological Approach

“…2 shows the decrease of the top-30 eigenvalues of both surrogate and actual data, now computed for data set of 85 ETF securities. By examining the decrease of the largest 30 eigenvalues, we are informed that, as in the multiple studies using stocks ( [23], [6], [24] and [15]) the number of relevant dimensions shaping the market structure is six.…”

Indicators of economic crises: a data-driven clustering approach

“…For example, Demirer et al 2017create a directed network by filling the entry A zn of the adjacency matrix A with the proportion that element n contributes to the forecast error variance of element z. Another metric was introduced by Mantegna (1999) and became a popular tool for assessing the topology of financial markets (Araújo and Louçã 2007;Spelta and Araújo 2012;Araújo and Göbel 2019). Transforming the correlation coefficient in such a way that it complies with all three axioms of a distance metric, Mantegna's (1999) measure is well suited for dealing with high-frequency stock market data.…”

“…Another popular filtering technique is the Minimal Spanning Tree (MST), initially introduced by Mantegna's study on the topology of a number of stock market indices (Mantegna 1999) and adopted in subsequent papers (e.g. Spelta and Araújo (2012); Araújo and Göbel (2019)). The N −1 links, spanning the MST, create a connected network, which does not allow for disconnected componentsislands or cliques -which is, however, a desired outcome, i.e.…”