Mesoscopic Structure of the Stock Market and Portfolio Optimization

Sebastiano Michele Zema, Giorgio Fagiolo (), Tiziano Squartini and Diego Garlaschelli

LEM Papers Series from Laboratory of Economics and Management (LEM), Sant'Anna School of Advanced Studies, Pisa, Italy

Abstract: The idiosyncratic (microscopic) and systemic (macroscopic) components of market structure have been shown to be responsible for the departure of the optimal mean-variance allocation from the heuristic 'equally-weighted' portfolio. In this paper, we exploit clustering techniques derived from Random Matrix Theory (RMT) to study a third, intermediate (mesoscopic) market structure that turns out to be the most stable over time and provides important practical insights from a portfolio management perspective. First, we illustrate the benefits, in terms of predicted and realized risk profiles, of constructing portfolios by filtering out both random and systemic comovements from the correlation matrix. Second, we redefine the portfolio optimization problem in terms of stock clusters that emerge after filtering. Finally, we propose a new wealth allocation scheme that attaches equal importance to stocks belonging to the same community and show that it further increases the reliability of the constructed portfolios. Results are robust across different time spans, cross-sectional dimensions and set of constraints defining the optimization problem.

Keywords: Random matrix theory; Community detection; Mesoscopic structures; Portfolio optimization. (search for similar items in EconPapers)
Date: 2021-12-07
New Economics Papers: this item is included in nep-fmk and nep-rmg
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Working Paper: Mesoscopic Structure of the Stock Market and Portfolio Optimization (2021)
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Persistent link: https://EconPapers.repec.org/RePEc:ssa:lemwps:2021/45

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