Enhanced capital-asset pricing model for bipartite financial networks reconstruction

T. Squartini, G. Caldarelli, G. Cimini

Physical Review E 96, 32315 (2017)

#finance#financialnetworks#bipartitenetworks

Download the PDF

LQ placeholderThe challenge of statistical reconstruction is using the limited available information to predict stock holdings.

The challenge of statistical reconstruction is using the limited available information to predict stock holdings.

The spreading of financial distress in capital markets and the resulting systemic risk strongly depend on the detailed structure of financial interconnections. Yet, while financial institutions have to disclose their aggregated balance sheet data, the information on single positions is often unavailable due to privacy issues. The resulting challenge is that of using the aggregate information to statistically reconstruct financial networks and correctly predict their higher-order properties. However, standard approaches generate unrealistically dense networks, which severely underestimate systemic risk. Moreover, reconstruction techniques are generally cast for networks of bilateral exposures be- tween financial institutions (such as the interbank market), whereas, the network of their investment portfolios (i.e., the stock market) has received much less attention. Here we develop an improved reconstruction method, based on statistical mechanics concepts and tailored for bipartite market networks. Technically, our approach consists in the preliminary estimation of connection probabilities by maximum-entropy inference driven by entities capitalizations and link density, followed by a density-corrected gravity model to assign position weights. Our method is successfully tested on NASDAQ, NYSE and AMEX filing data, by correctly reproducing the network topology and providing reliable estimates of systemic risk over the market.

LQ placeholder

Degree-correlations in a bursting dynamic network model

F. Vanni, P. Barucca

Journal of Economic Interaction and Coordination

LQ placeholder

Scale of non-locality for a system of n particles

S. Talaganis, I. Teimouri

Sub. to Physical Review D

LQ placeholder

How much can we influence the rate of innovation?

T. Fink, M. Reeves

Science Advances

LQ placeholder

The statistical physics of real-world networks

G. Cimini, T. Squartini, F. Saracco, D. Garlaschelli, A. Gabrielli, G. Caldarelli

Nature Reviews Physics

LQ placeholder

PopRank: Ranking pages’ impact and users’ engagement on Facebook

A. Zaccaria, M. Vicario, W. Quattrociocchi, A. Scala, L. Pietronero

PLoS ONE

LQ placeholder

On defining the Hamiltonian beyond quantum theory

D. Branford, O. Dahlsten, A. Garner

Foundations of Physics

119 / 119 papers