Up: 3:3-4

The design and performance of the adaptive stock market index

Lior Zatlavi; Dror Y. Kenett; Eshel Ben-Jacob

Algorithmic Finance (2014), 3:3-4, 189-207
DOI: 10.3233/AF-140039

Published: Abstract, PDF.
Archived: SSRN.


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.

Managing Editor

Philip Maymin

University of Bridgeport

Deputy Managing Editor

Jayaram Muthuswamy

Kent State University

Advisory Board

Kenneth J. Arrow

Stanford University

Herman Chernoff

Harvard University

David S. Johnson

AT&T Labs Research

Leonid Levin

Boston University

Myron Scholes

Stanford University

Michael Sipser

Massachusetts Institute of Technology

Richard Thaler

University of Chicago

Stephen Wolfram

Wolfram Research

Editorial Board

Associate Editors

Peter Bossaerts

California Institute of Technology

Emanuel Derman

Columbia University

Ming-Yang Kao

Northwestern University

Pete Kyle

University of Maryland

David Leinweber

Lawrence Berkeley National Laboratory

Richard J. Lipton

Georgia Tech

Avi Silberschatz

Yale University

Robert Webb

University of Virginia

Affiliate Editors

Giovanni Barone-Adesi

University of Lugano

Bruce Lehmann

University of California, San Diego

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Online ISSN: 2157-6203
Print ISSN: 2158-5571