Abstract
Stock indices related to specific economic sectors play a major role in portfolio diversification. We observe some flaws in the traditional sector classification and propose a latent class approach in order to correctly classify the stock companies into homogenous groups under risk-return profile. Furthermore we provide synthetic price index numbers for each traditional and new sector by evaluating the effect of different weighting structures on the risk-return profile. We obtain new sector indices which are consistent with the standard portfolio theory and lead to an improvement of sector portfolio diversification. Our results allow to introduce a methodological dimension into both the sector definition and the sector synthesis.
This is a preview of subscription content, log in via an institution.
Buying options
Tax calculation will be finalised at checkout
Purchases are for personal use only
Learn about institutional subscriptionsNotes
- 1.
We report Eq. (1) in a simplified form, where does not explicitly appear the probability of each traditional sector \( \pi_{c} \); the complete expression for \( \pi_{xmspc} \) is: \( \pi_{xmspc}= \pi_{c}\pi_{x|c}\pi_{m|x}\pi_{s|x}\pi_{p|x}\).
References
Costa, M., & De Angelis, L. (2008, June 11–13). Sector classification in stock markets: A latent class approach. In Book of Short Papers, Meeting of the Classification and Data Analysis Group of the Italian Statistical Society, Caserta. Heidelberg : Springer.
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977). Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society B, 39, 1–38.
Hagenaars, J. A. (1990). Categorical longitudinal data - loglinear analysis of panel, trend and cohort data. Sage : Newbury Park.
Lazarsfeld, P. F. (1950). The logical and mathematical foundation of latent structure analysis. In S. Stouffer et al. (Ed.), Measurement and prediction. New York : Wiley.
Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston: Houghton Mill.
Lisi, F., & Mortandello, F. (2004). Numeri indici di borsa: flottante e volatility. Statistica Applicata, 1, 17–37.
Lisi F., & Otranto, E. (2008). Clustering mutual funds by return and risk levels (Working Paper CRENoS 200813) Sardinia: Centre for North South Economic Research, University of Cagliari and Sassari.
Magidson, J., & Vermunt, J. K. (2001). Latent class factor and cluster models, Bi-plots and related graphics displays. Sociological Methodology, 31, 223–264.
Magidson, J., & Vermunt, J. K. (2004). Latent class models. In D. Kaplan (Ed.), The sage handbook of quantitative methodology for the social sciences. Thousand Oaks: Sage Publications.
Moustaki, I., & Papageorgiu, I. (2005). Latent class models for mixed variables with applications in archaeometry. Computational Statistics and Data Analysis, 48, 659–675.
Otranto, E. (2008). Clustering heteroskedastic time series by model-based procedures. Computational Statistics and Data Analysis, 52, 4685–4698.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2010 Springer-Verlag Berlin Heidelberg
About this paper
Cite this paper
Costa, M., De Angelis, L. (2010). Sector Price Indexes in Financial Markets: Methodological Issues. In: Biggeri, L., Ferrari, G. (eds) Price Indexes in Time and Space. Contributions to Statistics. Physica-Verlag HD. https://doi.org/10.1007/978-3-7908-2140-6_14
Download citation
DOI: https://doi.org/10.1007/978-3-7908-2140-6_14
Published:
Publisher Name: Physica-Verlag HD
Print ISBN: 978-3-7908-2139-0
Online ISBN: 978-3-7908-2140-6
eBook Packages: Mathematics and StatisticsMathematics and Statistics (R0)