Estimating time-varying network effects with application to portfolio allocation

Landau, Daniel; Ramos, Gabriel Lobato

Estimating time-varying network effects with application to portfolio allocation

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Landau, D, Ramos, G. Estimating time-varying network effects with application to portfolio allocation. 2019. handle: http://hdl.handle.net/10230/43332

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Abstract

In this paper, we characterize a variety of financial markets as partially correlated networks of stock returns via the implementation of the joint sparse regression estimation techniques of Peng et al. (2009). We find that Markowitz portfolios generally dissuade the inclusion of central stocks in the network, yet the interaction of a stock's individual and systemic performance is more complex. We implement a number of investment strategies aimed at simplifying the portfolio selection process, contingent on the time-varying network dynamics. We find that applying mean-variance allocation to a restricted sample of stocks can statistically significantly enhance out-of-sample portfolio performance in comparison to a benchmark. We also find evidence that such portfolios are more resilient during periods of macroeconomic instability.