Abstract
In the last decade much interest has been shown in the possibility of using sophisticated forecasting techniques as the basis of trading systems that will beat the market. Recent empirical evidence has indicated that financial markets can exhibit some degree of predictable behaviour (as described in Chapter 3). These results are justified on the basis that markets are only truly efficient, or unpredictable, with respect to information or modelling techniques that are commonly available to other market participants. Sophisticated modelling techniques in effect generate new information with respect to which markets are not necessarily efficient. In principle, this effect is encapsulated in the relative efficient market hypothesis (Lo and MacKinlay, 1999).
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
Author information
Authors and Affiliations
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2002 Springer-Verlag London
About this chapter
Cite this chapter
Towers, N., Burgess, A.N. (2002). Learning Trading Strategies for Imperfect Markets. In: Shadbolt, J., Taylor, J.G. (eds) Neural Networks and the Financial Markets. Perspectives in Neural Computing. Springer, London. https://doi.org/10.1007/978-1-4471-0151-2_12
Download citation
DOI: https://doi.org/10.1007/978-1-4471-0151-2_12
Publisher Name: Springer, London
Print ISBN: 978-1-85233-531-1
Online ISBN: 978-1-4471-0151-2
eBook Packages: Springer Book Archive