Cluster analysis for portfolio optimization

By Vincenzo Tola, Fabrizio Lillo, Mauro Gallegati, Rosario Nunzio Mantegna. 2005

274 citations recorded by the source corpus.

We consider the problem of the statistical uncertainty of the correlation matrix in the optimization of a financial portfolio. We show that the use of clustering algorithms can improve the reliability of the portfolio in terms of the ratio between predicted and realized risk. Bootstrap analysis indicates that this improvement is obtained in a wide range of the parameters N (number of assets) and T (investment horizon). The predicted and realized risk level and the relative portfolio composition of the selected portfolio for a given value of the portfolio return are also investigated for each considered filtering method.

Subfields: Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/391565af499ed5a6888ee723276d4f859589808e

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Citations in the corpus

Deep Stock Representation Learning: From Candlestick Charts to Investment Decisions (2017, background): “This is important to provide risk reduction by selecting a well diversified portfolio [16, 17].”
Correlation, Hierarchies, and Networks in Financial Markets (2008, unknown): “…increments of stock return time series (Micciche et al., 2003), where the estimation of statistical reliable properties of the correlation matrix is crucial for several financial decision processes such as asset allocation, portfolio optimization (Tola et al., 2008), derivative pricing, etc.”
The Joint Distribution of Stock Returns is Not Elliptical (2010, unknown): “This topic has been the focus of intense research in the recent years, some inspired by Random Matrix Theory (for a review see [13,38]) or clustering ideas [32,42,43].”
Topological properties of stock market networks: The case of Brazil (2010, background): “Several papers have shown that the use of these tools may help in the design of portfolio strategies and risk assessment [21].”
Do High-Frequency Data Improve High-Dimensional Portfolio Allocations? (2013, methodology): “Eigenvalue cleaning is a regularization technique proposed by Laloux et al. (1999) and further developed by Tola et al. (2008) that draws upon random matrix theory to determine the distribution of the eigenvalues of a correlation matrix estimate R depending on the ratio of n observations and m…”