From market games to real-world markets |
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Authors: | P Jefferies ML Hart PM Hui NF Johnson |
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Institution: | (1) Department of Physics, Oxford University, Parks Rd, Oxford, OX13PU, UK, GB;(2) Physics Department, Chinese University of Hong Kong, Shatin, Hong Kong, PR China, HK |
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Abstract: | This paper uses the development of multi-agent market models to present a unified approach to the joint questions of how financial
market movements may be simulated, predicted, and hedged against. We first present the results of agent-based market simulations
in which traders equipped with simple buy/sell strategies and limited information compete in speculatory trading. We examine
the effect of different market clearing mechanisms and show that implementation of a simple Walrasian auction leads to unstable
market dynamics. We then show that a more realistic out-of-equilibrium clearing process leads to dynamics that closely resemble
real financial movements, with fat-tailed price increments, clustered volatility and high volume autocorrelation. We then
show that replacing the `synthetic' price history used by these simulations with data taken from real financial time-series
leads to the remarkable result that the agents can collectively learn to identify moments in the market where profit is attainable.
Hence on real financial data, the system as a whole can perform better than random. We then employ the formalism of Bouchaud
in conjunction with agent based models to show that in general risk cannot be eliminated from trading with these models. We
also show that, in the presence of transaction costs, the risk of option writing is greatly increased. This risk, and the
costs, can however be reduced through the use of a delta-hedging strategy with modified, time-dependent volatility structure.
Received 30 August 2000 |
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Keywords: | PACS 01 30 Cc Conference proceedings – 05 45 Tp Time series analysis – 05 65 +b Self-organized systems |
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