Network Origins of Portfolio Risk
This paper shows that idiosyncratic shocks, in the presence of asymmetric propagation
structures, diminish investors' diversification benefits.
In Search of Anomalies
This paper develops a systematic method to search for asset pricing anomalies in the context of technical analysis.
A Network Approach to Portfolio Selection
(with Gustavo Peralta)
Published in the Journal of Empirical Finance
Abstract: In this study, a financial market is conceived as a network where the securities are nodes and the links account for returns’ correlations. We theoretically prove the negative relationship between the centrality of assets in this financial market network and their optimal weights under the Markowitz framework. Therefore, optimal portfolios overweight low-central securities to avoid the large variances that result when highly influential stocks are included in the investor’s opportunity set. Next, we empirically investigate the major financial and market determinants of stock's centralities. The evidence indicates that highly central nodes tend to coincide with older, larger-cap, cheaper and financially riskier securities. Finally, we explore by means of in-sample and out-of-sample analysis the extent to which the structure of the stock market network can be employed to improve the portfolio selection process. We propose a network-based investment strategy that outperforms well-known benchmarks while presenting positive and significant Carhart alphas. The major contribution of the paper is to employ the financial market network as a useful device to improve the portfolio selection process by targeting a group of assets according to their centrality.
Hedging Network Structure and Portfolio Diversification
(with Silvia Mayoral and David Moreno)
Abstract: We introduce hedging network with nodes corresponding to stocks and links accounting for hedging relations. This network structure and the nature of its links are found to be influential in determining portfolio diversification benefits. Comparing portfolio variance convergence in different stylized networks, we find asymmetrical structures to be moderately performing well in both cases of positive and negative hedging relationships while in symmetrical structures, the performance advantage is attributed to type and magnitude of hedging relations. Furthermore, we propose a centrality measure in hedging network to find stocks most favorable to a diversified portfolio. Employing this centrality measure, we build well-diversified portfolios with low number of stocks that perform better than overall diversification both in-sample and out-of-sample.
Network Centrality, Failure Prediction and Systemic Risk
Published in the Journal of Network Theory in Finance
Abstract: A financial market can be expressed in a network structure where the stocks resides as nodes and the links account for returns correlation. Centrality measure in the financial network structure captures firms’ embeddedness and connectivity in the capital market structure. This paper investigates firms’ centrality in the financial network as an explanatory variable in corporate failure prediction and also as a systemic risk measure. First, when analyzing the CDS spreads, I find peripheral firms in the network to have higher average CDS spreads and higher propensity to CDS jump events. Second, centrality is found to increase the explanatory power of default prediction models and moreover, it is negatively related to failure and bankruptcy probability. This implies that peripheral firms in the network are more likely to fail. Finally, examining the out-of-sample performance of centrality as a systemic risk measure, I find that centrality distinguish correctly the firms that suffered a higher loss during the 2007/2008 crisis period.