A cerebellar associative memory approach to option pricing and arbitrage trading

Authors:
S. D. Teddy;E. M. -K. Lai;C. Quek
Affiliations:
Data Mining Department, Institute for Infocomm Research (I2R), 21 Heng Mui Keng Terrace, Singapore;Institute of Information Sciences and Technology, Massey University, Wellington, New Zealand;Center for Computational Intelligence, Block N4 #2A-32, School of Computer Engineering, Nanyang Technological University, Nanyang Avenue, Singapore
Venue:
Neurocomputing
Year:
2008

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Abstract

Option pricing is a process to obtain the theoretical fair value of an option based on the factors affecting its price. The classical approaches to option pricing include the Black-Scholes pricing formula and the binomial pricing model. These techniques, however, employ complex and rigid statistical formulations that are not easily comprehensible to novice investors. More recently, non-parametric and computational methods of option valuation that are able to construct a model of the pricing formula from historical data have been proposed in the literature. However, most of these models functioned as black-boxes and may not be able to efficiently and accurately capture the complex market dynamics and characteristics of the option data. This paper proposes a novel brain-inspired cerebellar associative memory model for pricing American-style call options on British pound vs. US dollar currency futures. The proposed model, named PSECMAC, constitutes 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 an encouraging return on investment of 23.1% for some of the traded options.