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Chance Discovery In Stock Index Option And Futures Arbitrage

Author

Listed:
  • EDWARD TSANG

    (Department of Computer Science, University of Essex, Wivenhoe Park, Colchester, C04 3SQ, United Kingdom)

  • SHERI MARKOSE

    (Department of Economics, University of Essex, Wivenhoe Park, Colchester, C04 3SQ, United Kingdom)

  • HAKAN ER

    (Department of Business Administration, Akdeniz University, Dumlupinar Bulvari, Kampus, Antalya, 07058, Turkey)

Abstract

The prices of the option and futures of a stock both reflect the market's expectation of futures changes of the stock's price. Their prices normally align with each other within a limited window. When they do not, arbitrage opportunities arise: an investor who spots the misalignment will be able to buy (sell) options on the one hand, and sell (buy) futures on the other and make risk-free profits. Historical data suggest that option and futures prices on the LIFFE Market do not align occasionally. Arbitrage chances are rare. Besides, they last for seconds only before the market adjusts itself. The challenge is not only to discover such chances, but to discover them ahead of other arbitragers. In the past, we have introduced EDDIE as a genetic programming tool for forecasting. This paper describes EDDIE-ARB, a specialization of EDDIE, for forecasting arbitrage opportunities. As a tool, EDDIE-ARB was designed to enable economists and computer scientists to work together to identify relevant independent variables. Trained on historical data, EDDIE-ARB was capable of discovering rules with high precision. Tested on out-of-sample data, EDDIE-ARB out-performed a naiveex anterule, which reacted only when misalignments were detected. This establishes EDDIE-ARB as a promising tool for arbitrage chances discovery. It also demonstrates how EDDIE brings domain experts and computer scientists together.

Suggested Citation

  • Edward Tsang & Sheri Markose & Hakan Er, 2005. "Chance Discovery In Stock Index Option And Futures Arbitrage," New Mathematics and Natural Computation (NMNC), World Scientific Publishing Co. Pte. Ltd., vol. 1(03), pages 435-447.
    • Handle: RePEc:wsi:nmncxx:v:01:y:2005:i:03:n:s1793005705000251
    DOI: 10.1142/S1793005705000251
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    References listed on IDEAS

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    1. Zellner,Arnold, 2004. "Statistics, Econometrics and Forecasting," Cambridge Books, Cambridge University Press, number 9780521540445, September.
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