A novel recurrent neural network-based prediction system for option trading and hedging |
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Authors: | C Quek M Pasquier N Kumar |
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Affiliation: | (1) Centre for Computational Intelligence, Nanyang Technological University, School of Computer Engineering,, Block N4-B1a-02, Nanyang Avenue, Singapore, 639798, Singapore |
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Abstract: | In order to reduce their exposure to the erratic fluctuations of the financial markets, traders are nowadays increasingly
dealing with options and other derivative securities instead of directly trading in the underlying assets. This paradigm shift
has attracted the attention of many researchers, and there has been a tremendous increase in the awareness and activities
of derivative securities. In particular there is a need to devise new techniques to address the limitations of traditional
parametric pricing methods, which rely on assumptions and approximations to capture the complex dynamics of pricing processes.
This paper proposes a novel non-parametric method using an ad-hoc recurrent neural network for estimating the future prices
of war commodities such as gold and crude oil as well as currencies, which are increasingly gaining importance in the financial
markets. The price predictions from the network, shown to be accurate and computationally efficient, are used in a hedging
system to avoid unnecessary risks. Experiments with actual gold and currency trading data show that our system using the proposed
network and strategy can construct portfolios yielding a return on investment of about 4.76%. |
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Keywords: | Computational finance Option trading Hedging system Recurrent neural network |
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