Profit opportunities, crash prediction and risk minimization in artificial and real-world markets
AbstractThis 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.
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Bibliographic InfoPaper provided by Society for Computational Economics in its series Computing in Economics and Finance 2001 with number 86.
Date of creation: 01 Apr 2001
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Web page: http://www.econometricsociety.org/conference/SCE2001/SCE2001.html
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complexity; non-equilibrium; agents;
Find related papers by JEL classification:
- G10 - Financial Economics - - General Financial Markets - - - General (includes Measurement and Data)
- C45 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Neural Networks and Related Topics
- G00 - Financial Economics - - General - - - General
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