Cluster analysis for portfolio optimization

Tola, Vincenzo; Lillo, Fabrizio; Gallegati, Mauro; Mantegna, Rosario N.

doi:10.1016/j.jedc.2007.01.034

Cited by 230 publications

(163 citation statements)

References 45 publications

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“…Hierarchical clustering procedures have been shown to be effective in extracting financial information from the correlation matrix of stock returns (Mantegna 1999). Finally, it should be noted that hierarchical clustering methods have already been considered in portfolio optimization (Tola et al 2008).…”

Section: Agglomerative Hierarchical Clustering Estimatorsmentioning

confidence: 99%

When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators

Pantaleo

Tumminello

Lillo

et al. 2011

Quantitative Finance

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The use of improved covariance matrix estimators as an alternative to the sample estimator is considered an important approach for enhancing portfolio optimization. Here we empirically compare the performance of nine improved covariance estimation procedures using daily returns of 90 highly capitalized US stocks for the period 1997-2007. We find that the usefulness of covariance matrix estimators strongly depends on the ratio between the estimation period T and the number of stocks N, on the presence or absence of short selling, and on the performance metric considered. When short selling is allowed, several estimation methods achieve a realized risk that is significantly smaller than that obtained with the sample covariance method. This is particularly true when T/N is close to one. Moreover, many estimators reduce the fraction of negative portfolio weights, while little improvement is achieved in the degree of diversification. On the contrary, when short selling is not allowed and T > N, the considered methods are unable to outperform the sample covariance in terms of realized risk, but can give much more diversified portfolios than that obtained with the sample covariance. When T < N, the use of the sample covariance matrix and of the pseudo-inverse gives portfolios with very poor performance.Portfolio optimization, Correlation structures, Statistical methods, Econophysics,

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Section: Agglomerative Hierarchical Clustering Estimatorsmentioning

confidence: 99%

When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators

Pantaleo

Tumminello

Lillo

et al. 2011

Quantitative Finance

Self Cite

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“…The disadvantage of external schedulers is that it may be very hard to generalize execution patterns for irregular or speculative parallelism. In this case, which occurs in various situations ranging from medical image processing to portfolio optimization [50], a development of a specialized embedded scheduler may be necessary.…”

Section: User-level Schedulingmentioning

confidence: 99%

Scheduling for Responsive Grids

Germain-Renaud

Loomis²,

Mościcki

et al. 2007

J Grid Computing

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Abstract. Grids are facing the challenge of seamless integration of the grid power into everyday use. One critical component for this integration is responsiveness, the capacity to support on-demand computing and interactivity. Grid scheduling is involved at two levels in order to provide responsiveness: the policy level and the implementation level. The main contributions of this paper are as follows. First, we present a detailed analysis of the performance of the EGEE grid with respect to responsiveness. Second, we examine two user-level schedulers located between the general scheduling layer and the application layer. These are the DIANE (DIstributed ANalysis Environment) framework, a general-purpose overlay system, and a specialized, embedded scheduler for gPTM3D, an interactive medical image analysis application. Finally, we define and demonstrate a virtualization scheme, which achieves guaranteed turnaround time, schedulability analysis, and provides the basis for differentiated services. Both methods target a brokering-based system organized as a federation of batch-scheduled clusters, and an EGEE implementation is described.

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“…Besides clustering has found application in finance, e.g. (Tola et al, 2008), which gives us a framework for benchmarking on real data.…”

Section: Motivation and Goal Of Studymentioning

confidence: 99%

“…The correlation matrix is then filtered thanks to a clustering of the correlation-network (Di Matteo et al, 2010) built using similarity and dissimilarity matrices which are derived from the correlation one by convenient ad hoc transformations. Clustering these correlation-based networks (Onnela et al, 2004) aims at filtering the correlation matrix for standard portfolio optimization (Tola et al, 2008). Yet, adopting similar approaches only allow to retrieve information given by assets co-movements and nothing about the specificities of their returns behaviour, whereas we may also want to distinguish assets by their returns distribution.…”

Section: Performance Of Clustering Using Gnprmentioning

confidence: 99%

Toward a generic representation of random variables for machine learning

Marti¹,

Very

Donnat³

2016

Pattern Recognition Letters

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This paper presents a pre-processing and a distance which improve the performance of machine learning algorithms working on independent and identically distributed stochastic processes. We introduce a novel non-parametric approach to represent random variables which splits apart dependency and distribution without losing any information. We also propound an associated metric leveraging this representation and its statistical estimate. Besides experiments on synthetic datasets, the benefits of our contribution is illustrated through the example of clustering financial time series, for instance prices from the credit default swaps market. Results are available on the website www.datagrapple.com and an IPython Notebook tutorial is available at www.datagrapple.com/Tech for reproducible research.

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Cluster analysis for portfolio optimization

Cited by 230 publications

References 45 publications

When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators

When do improved covariance matrix estimators enhance portfolio optimization? An empirical comparative study of nine estimators

Scheduling for Responsive Grids

Toward a generic representation of random variables for machine learning

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