A P-spline based clustering approach for portfolio selection

Cluster analysis; P-spline; Portfolio selection; Time series; Finance; Investments; Clustering approach; Clustering methods; Clustering techniques; Financial portfolio; Financial practitioners; Investment decisions; P-splines; Time series analysis

Abstract :

[en] In the last years, many clustering techniques dealing with time course data have been proposed due to recent interests in studying phenomena that change over time. A new clustering method suitable for time series applications has been recently proposed by exploiting the properties of the P-splines approach. This semi-parametric tool has several advantages, i.e. it facilitates the removal of noise from time series and it ensures a computational time saving. In this paper, we propose to use this clustering approach on financial data with the aim of building a financial portfolio. Our proposal works directly on time series without any pre-processing, except for the computation of the spline coefficients and, eventually, normalizing the series. We show that our strategy is useful to support the investment decisions of financial practitioners. © 2017 Elsevier Ltd

Disciplines :

Finance
Sociology & social sciences
Quantitative methods in economics & management

Author, co-author :

Iorio, C.; Department of Industrial Engineering, University of Naples Federico II, Italy

Frasso, Gianluca ; Université de Liège - ULg

D'Ambrosio, A.; Department of Economics and Statistics, University of Naples Federico II, Italy

Siciliano, R.; Department of Industrial Engineering, University of Naples Federico II, Italy

Language :

English

Title :

A P-spline based clustering approach for portfolio selection

Publication date :

2018

Journal title :

Expert Systems with Applications

ISSN :

0957-4174

eISSN :

1873-6793

Publisher :

Elsevier, Oxford, United Kingdom

Volume :

Pages :

88-103

Peer reviewed :

Peer Reviewed verified by ORBi

Available on ORBi :

since 13 October 2020

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