An electronic marketplace based on reputation and learning
Journal of Theoretical and Applied Electronic Commerce Research
A software architecture framework for on-line option pricing
The Journal of Supercomputing
A software architecture framework for on-line option pricing
ISPA'06 Proceedings of the 4th international conference on Parallel and Distributed Processing and Applications
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The idea of a machine that can learn from its own interactions with the world has been one ofthe driving forces behind artificial intelligence research since its inception (Turing, 1950). Themotivation of this paper is to present and demonstrate the merits of a machine to assist a non-expert decision maker, in applying stock market hedging strategies that are typically usedby experts. The machine, called a Virtual Decision Maker (VDM), provides the processing power to deal with the very high granularity of such strategies while offering higher flexibility in choosing the trading frequency. More specifically, the VDM is a network of cooperating intelligent agents' technologies that can exploit automated on-line trading services at any time and any place without the physical presence of the decision maker. At present, the VDM is developed in an Excel-VB environment with agents that cooperate to (i) import the required stock market real time data, (ii) identify the opportunity of making a trade, (iii) formulate an appropriate strategy and (iv) execute of the corresponding order on the fly. The design of theVDM takes as its main premise the technological advantage of reduced reaction time, as opposed to attempting to anticipate a given security's movement. Results indicate in a disturbing manner that, given expert-validated knowledge, decision-making by cooperating and negotiation intelligent agents could lead to higher returns than commonly used indexes. In the conclusion, the idea of full automation is discussed in relation to the decision maker's behavioural and the cognitive issues.