Limited profit in predictable stock markets

Roland Rothenstein and Klaus Pawelzik

Physica A: Statistical Mechanics and its Applications, 2005, vol. 348, issue C, 419-427

Abstract: It has been assumed that arbitrage profits are not possible in efficient markets, because future prices are not predictable. Here, we show that predictability alone is not a sufficient measure of market efficiency because of the influence an order has on its dynamics. We instead propose to measure inefficiencies of markets in terms of the maximal profit an ideal trader who can perfectly predict the future behavior of the market can take out from a market. In a stock market model with an evolutionary selection of agents this method reveals that, the mean relative amount of realizable profits P is very limited and we find that it decays with the rising number of agents. Our results show that markets may self-organize their collective dynamics such that it becomes very sensitive to profit attacks, which demonstrates that a high degree of market efficiency can coexist with predictability.

Keywords: Econophysics; Efficiency; Stock market model; Predictability (search for similar items in EconPapers)
Date: 2005
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0378437104012130
Full text for ScienceDirect subscribers only. Journal offers the option of making the article available online on Science direct for a fee of $3,000

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:eee:phsmap:v:348:y:2005:i:c:p:419-427

DOI: 10.1016/j.physa.2004.09.010

Access Statistics for this article

Physica A: Statistical Mechanics and its Applications is currently edited by K. A. Dawson, J. O. Indekeu, H.E. Stanley and C. Tsallis

More articles in Physica A: Statistical Mechanics and its Applications from Elsevier
Bibliographic data for series maintained by Catherine Liu ().