Profit opportunities, crash prediction and risk minimization in artificial and real-world markets
This paper reports on the use of multi-agent games to model financial markets. Our research employs multi-agent games to address three questions which are of great practical importance in quantitative finance: how profit opportunities may be identified, large price movements predicted, and inherent risk exposure minimized. The present paper focuses on the aspect of prediction. In particular, we report a technique for predicting future movements of financial time-series using multi-agent games. A third-party game is trained on a black-box time-series, and is then run into the future to extract next-step and multi-step predictions. Such predictions have potential use not only for speculative gain, but also as the basis for improved risk management and portfolio optimization strategies.
To our knowledge, this item is not available for
download. To find whether it is available, there are three
1. Check below under "Related research" whether another version of this item is available online.
2. Check on the provider's web page whether it is in fact available.
3. Perform a search for a similarly titled item that would be available.
|Date of creation:||01 Apr 2001|
|Contact details of provider:|| Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html|
More information through EDIRC
When requesting a correction, please mention this item's handle: RePEc:sce:scecf1:86. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Christopher F. Baum)
If references are entirely missing, you can add them using this form.