Towards understanding the predictability of stock markets from the perspective of computational complexity

James Aspnes*, David F. Fischer, Michael J. Fischer, Ming Yang Kao, Alok Kumar

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations

Abstract

This paper initiates a study into the century-old issue of market predictability from the perspective of computational complexity. We develop a simple agent-based model for a stock market where the agents are traders equipped with simple trading strategies, and their trades together determine the stock prices. Computer simulations show that a basic case of this model is already capable of generating price graphs which are visually similar to the recent price movements of high tech stocks. In the general model, we prove that if there are a large number of traders but they employ a relatively small number of strategies, then there is a polynomial-time algorithm for predicting future price movements with high accuracy. On the other hand, if the number of strategies is large, market prediction becomes complete in two new computational complexity classes CPP and BCPP, where P NP [&Ogr;(log n)] e BCPP e CPP = PP. These computational completeness results open up a novel possibility that the price graph of a actual stock could be sufficiently deterministic for various prediction goals but appear random to all polynomial-time prediction algorithms.

Original languageEnglish (US)
Title of host publicationProceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms
Pages745-754
Number of pages10
StatePublished - Dec 1 2001
Event2001 Operating Section Proceedings, American Gas Association - Dallas, TX, United States
Duration: Apr 30 2001May 1 2001

Other

Other2001 Operating Section Proceedings, American Gas Association
CountryUnited States
CityDallas, TX
Period4/30/015/1/01

Keywords

  • Algorithms
  • Management
  • Measurement
  • Performance
  • Theory
  • Verification

ASJC Scopus subject areas

  • Software
  • Mathematics(all)

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  • Cite this

    Aspnes, J., Fischer, D. F., Fischer, M. J., Kao, M. Y., & Kumar, A. (2001). Towards understanding the predictability of stock markets from the perspective of computational complexity. In Proceedings of the 12th Annual ACM-SIAM Symposium on Discrete Algorithms (pp. 745-754)