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Financial Time Series and Neural Networks in a Minority Game Context

Author

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  • Luca Grilli
  • Angelo Sfrecola
  • Massimo Alfonso Russo

Abstract

In this paper we consider financial time series from U.S. Fixed Income Market, S&P500, DJ Eurostoxx 50, Dow Jones,Mibtel and Nikkei 225. It is well known that financial time series reveal some anomalies regarding the EfficientMarketHypothesis and some scaling behaviour, such as fat tails and clustered volatility, is evident. This suggests that financial time series can be considered as “pseudo”-random.For this kind of time series the prediction power of neural networks has been shown to be appreciable [10]. At first, we consider the financial time series from the Minority Game point of view and then we apply a neural network with learning algorithm in order to analyse its prediction power. We prove that the Fixed Income Market shows many differences from other markets in terms of predictability as a measure of market efficiency.

Suggested Citation

  • Luca Grilli & Angelo Sfrecola & Massimo Alfonso Russo, 2010. "Financial Time Series and Neural Networks in a Minority Game Context," Quaderni DSEMS lg_maf_2009, Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia.
    • Handle: RePEc:ufg:qdsems:lg_maf_2009
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    Cited by:

    1. Luca GRILLI & Massimo Alfonso RUSSO & Roberto GISMONDI, 2012. "Methodological Proposals For A Qualitative Evaluation Of Italian Durum Wheat Varieties," Journal of Applied Economic Sciences, Spiru Haret University, Faculty of Financial Management and Accounting Craiova, vol. 7(2(20)/ Su), pages 103-122.

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