The design and performance of the adaptive stock market index

Lior Zatlavi, Dror Y. Kenett () and Eshel Ben-Jacob ()
Additional contact information
Lior Zatlavi: School of Electrical Engineering, Faculty of Engineering, Tel-Aviv University, Postal: School of Electrical Engineering, Faculty of Engineering, Tel-Aviv University, Tel-Aviv, Israel
Dror Y. Kenett: Center for Polymer Studies and Department of Physics, Postal: Center for Polymer Studies and Department of Physics, Boston University, Boston, MA, USA
Eshel Ben-Jacob: School of Physics and Astronomy, Tel-Aviv University, Postal: School of Physics and Astronomy, Tel-Aviv University, Tel-Aviv, Israel

Algorithmic Finance, 2014, vol. 3, issue 3-4, 189-207

Abstract: The stock market index is one of the main tools used by investors and financial managers to describe the market and compare the returns on specific investments. Common approaches to index calculation rely on a company's market value generating a weighted average as the index. This work presents new methods of computing adaptive stock market indices based on dynamical properties of the underlying index constituents, and introduces measures to evaluate their performance. The premise behind this work is that the influence of each stock on other stocks should be a major factor in determining the weight given to each stock in the index composition. The methodologies presented here provide the means to construct a dynamic adaptive index, which can be used as a benchmark for the underlying dynamics of the market. We investigate the components of the S&P500 index, and the components of the TA25 index, representing a large (NYSE) and a small (TASE) developed market, respectively. We focus our study on periods before and during the 2008 Sub-prime mortgage crisis. Our results provide evidence that the adaptive-indices provide an effective tool for policy and decision makers to monitor the stability and dynamics of the markets, and identify bubble formation and their ensuing collapse.

Keywords: Financial markets; prtial correlation; sock influence; adaptive-indices (search for similar items in EconPapers)
JEL-codes: C00 (search for similar items in EconPapers)
Date: 2014
References: Add references at CitEc
Citations: View citations in EconPapers (1)

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ris:iosalg:0031

Access Statistics for this article

Algorithmic Finance is currently edited by Phil Maymin

More articles in Algorithmic Finance from IOS Press
Bibliographic data for series maintained by Saskia van Wijngaarden ().