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Neural Networks to Predict Financial Time Series in a Minority Game Context

  • Luca Grilli

    ()

  • Angelo Sfrecola

In this paper we consider financial time series from U.S. Fixed Income Market, S&P500, Exchange Market and Oil Market. It is well known that financial time series reveal some anomalies as regards the Efficient Market Hypotesis and some scaling behavior is evident such as fat tails and clustered volatility. This suggests to consider financial time serie as "pseudo"-random time series. For this kind of time series the power of prediction of neural networks has been shown to be appreciable. We first consider the financial time serie from the Minority Game point of view and than we apply a neural network with learning algorithm in order to analyze its prediction power. We show that Fixed Income Market presents many differences from other markets in terms of predictability as a measure of market efficiency.

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Paper provided by Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia in its series Quaderni DSEMS with number 14-2005.

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Date of creation: Jun 2005
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Handle: RePEc:ufg:qdsems:14-2005
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  1. D. Challet & A. Chessa & M. Marsili & Y. -C. Zhang, 2000. "From Minority Games to real markets," Papers cond-mat/0011042, arXiv.org.
  2. Challet, Damien, 2008. "Inter-pattern speculation: Beyond minority, majority and $-games," Journal of Economic Dynamics and Control, Elsevier, vol. 32(1), pages 85-100, January.
  3. Grilli, Luca, 2004. "Long-term fixed income market structure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 332(C), pages 441-447.
  4. Arthur, W Brian, 1994. "Inductive Reasoning and Bounded Rationality," American Economic Review, American Economic Association, vol. 84(2), pages 406-11, May.
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