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A Brain-Inspired Cerebellar Associative Memory Approach to Option Pricing and Arbitrage Trading

  • S. D. Teddy
  • E. M. -K. Lai
  • C. Quek
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4234)

Abstract

Option pricing is a process to obtain the theoretical fair value of an option based on the factors affecting its price. Currently, the nonparametric and computational methods of option valuation are able to construct a model of the pricing formula from historical data. However, these models are generally based on a global learning paradigm, which may not be able to efficiently and accurately capture the dynamics and time-varying characteristics of the option data. This paper proposes a novel brain-inspired cerebellar associative memory model for pricing American-style option on currency futures. The proposed model, called PSECMAC, constitute a local learning model that is inspired by the neurophysiological aspects of the human cerebellum. The PSECMAC-based option pricing model is subsequently applied in a mis-priced option arbitrage trading system. Simulation results show a return on investment as high as 23.1% for a relatively risk-free investment.

Keywords

Option Price Call Option Trading System Strike Price Underlying Asset 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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References

  1. 1.
    Chance, D.M.: An Introduction to Derivatives & Risk Management, 6th edn. Thomson (2004)Google Scholar
  2. 2.
    Nielsen, L.T.: Pricing and Hedging of Derivative Securities – Textbook in continuous-time finance theory. Oxford University Press, Oxford (1999)Google Scholar
  3. 3.
    Black, F., Scholes, N.: The pricing of options and corporate liabilities. Journal of Political Economy 81, 637–659 (1973)CrossRefGoogle Scholar
  4. 4.
    Rendleman Jr., R.J., Bartter, B.J.: Two-state option pricing. Journal of Finance 34, 1093–1110 (1979)CrossRefGoogle Scholar
  5. 5.
    Radzikowski, P.: Non-parametric methods of option pricing. In: Proc. of Informs-Korms (Seoul 2000 conference), pp. 474–480 (2000)Google Scholar
  6. 6.
    Amilon, H.: A neural network versus black-scholes: A comparison of pricing and hedging performances. Scandinavian Working Papers in Economics, Lund University series, Department of economics, Lund, Sweden (2001)Google Scholar
  7. 7.
    Anders, U., Korn, O., Schmitt, C.: Improving the pricing of options - a neural network approach. Journal of Forecasting 17(5–6), 369–388 (1998)CrossRefGoogle Scholar
  8. 8.
    Qi, M., Maddala, G.S.: Option-pricing using artificial neural networks: the case of s&p500 index call options. Neural Networks in Financial Engineering, 78–92 (1995)Google Scholar
  9. 9.
    Hutchinson, J., Lo, A., Poggio, T.: A nonparametric approach to pricing and hedging derivative securities via learning networks. Journal of Finance 49, 851–889 (1994)CrossRefGoogle Scholar
  10. 10.
    Keber, C.: Option pricing with the genetic programming approach. Journal of Computational Intelligence in Finance 7(6), 26–36 (1999)Google Scholar
  11. 11.
    Ait-Sahalia, Y., Lo, A.W.: Nonparametric estimation of state-price densities implicit in financial asset price. LFE-1024-95, MIT-Sloan School of Management (1995)Google Scholar
  12. 12.
    Tung, W.L., Quek, C.: GenSo-OPATS: A brain-inspired dynamically evolving option pricing model and arbitrage trading system. In: Proc. IEEE CEC 2005, Edinburgh, Scotland, vol. 3, pp. 2429–2436 (2005)Google Scholar
  13. 13.
    Huang, K., Yang, H., King, I., Lyu, M.: Local learning vs. global learning: An introduction to maxi-min margin machine. In: Wang, L. (ed.) Support Vector Machines: Theory and Applications, vol. 177, pp. 113–132. Springer, Heidelberg (2005)Google Scholar
  14. 14.
    Bottou, L., Vapnik, V.: Local learning algorithms. Neural Computation 4, 888–900 (1992)CrossRefGoogle Scholar
  15. 15.
    Kandel, E.R., Schwartz, J.H., Jessell, T.M.: Principles of Neural Science, 4th edn. McGraw-Hill, New York (2000)Google Scholar
  16. 16.
    Middleton, F.A., Strick, P.L.: The cerebellum: An overview. Trends in Cognitive Sciences 27(9), 305–306 (1998)CrossRefGoogle Scholar
  17. 17.
    Albus, J.S.: Marr and Albus theories of the cerebellum two early models of associative memory. In: Proc. IEEE Compcon (1989)Google Scholar
  18. 18.
    Albus, J.S.: A new approach to manipulator control: The Cerebellar Model Articulation Controller (CMAC). J. Dyn. Syst. Meas. Control, Trans. ASME, 220–227 (1975)Google Scholar
  19. 19.
    Albus, J.S.: Data storage in Cerebellar Model Articullation Controller (CMAC). J. Dyn. Syst. Meas. Control, Trans. ASME, 228–233 (1975)Google Scholar
  20. 20.
    Yamamoto, T., Kaneda, M.: Intelligent controller using CMACs with self-organized structure and its application for a process system. IEICE Trans. Fundamentals 82(5), 856–860 (1999)Google Scholar
  21. 21.
    Commuri, S., Jagannathan, S., Lewis, F.L.: CMAC neural network control of robot manipulators. J. Robot Syst. 14(6), 465–482 (1997)CrossRefMATHGoogle Scholar
  22. 22.
    Ang, K., Quek, C.: Stock trading using PSEC and RSPOP: A novel evolving rough set-based neuro-fuzzy approach. IEEE Congress on Evolutionary Computation (2005)Google Scholar
  23. 23.
    Federmeier, K.D., Kleim, J.A., Greenough, W.T.: Learning-induces multiple synapse formation in rat cerebellar cortex. Neuroscience Letters 332, 180–184 (2002)CrossRefGoogle Scholar
  24. 24.
    Teddy, S.D., Quek, C., Lai, E.M.K.: Psecmac: A brain-inspired multi resolution cerebellar learning memory model. Neural Computation (under review, 2006)Google Scholar
  25. 25.
    Widrow, B., Stearns, S.D.: Adaptive Signal Processing. Prentice-Hall, Englewood Cliffs (1985)MATHGoogle Scholar
  26. 26.
    Chicago Mercantile Exchange, U. Online, http://www.cme.com
  27. 27.
    Gencay, R.: The predictability of security returns with simple trading rules. Journal of Empirical Finance 5(4), 347–359 (1998)CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2006

Authors and Affiliations

  • S. D. Teddy
    • 1
  • E. M. -K. Lai
    • 1
  • C. Quek
    • 1
  1. 1.Centre for Computational Intelligence, School of Computer EngineeringNanyang Technological UniversitySingapore

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