NDLI: A novel recurrent neural network-based prediction system for option trading and hedging

Content Provider	Springer Nature Link
Author	Quek, C. Pasquier, M. Kumar, N.
Copyright Year	2007
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%.
Starting Page	138
Ending Page	151
Page Count	14
File Format	PDF
ISSN	0924669X
Journal	Applied Intelligence
Volume Number	29
Issue Number	2
e-ISSN	15737497
Language	English
Publisher	Springer US
Publisher Date	2007-06-09
Publisher Place	Boston
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Computational finance Option trading Hedging system Recurrent neural network Manufacturing, Machines, Tools Mechanical Engineering Artificial Intelligence (incl. Robotics)
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence

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