Mesoscopic structure of the stock market and portfolio optimization

Arbeitspapier

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

Object Details

• Language: Englisch

References and Relationships

• Bibliographic citation: Series: LEM Working Paper Series ; No. 2021/45

Classification and Topics

• Classification: Wirtschaft ; Mathematical Methods ; Network Formation and Analysis: Theory ; Portfolio Choice; Investment Decisions
• Subject: Random matrix theory ; Community detection ; Mesoscopic structures ; Portfolio optimization

Contributors, Places and Time

• Event: Geistige Schöpfung
• (who): Zema, Sebastiano Michele ; Fagiolo, Giorgio ; Squartini, Tiziano ; Garlaschelli, Diego
• Event: Veröffentlichung
• (who): Scuola Superiore Sant'Anna, Laboratory of Economics and Management (LEM)
• (where): Pisa
• (when): 2021

Further information

• Handle: http://hdl.handle.net/10419/259540
• Last update: 10.03.2025, 11:45 AM CET