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Financial time series and neural networks in a minority game context

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

Listed:
  • Luca Grilli

    (University of Foggia, Dipartimento di Scienze Economiche, Matematiche e Statistiche)

  • Massimo Alfonso Russo

    (University of Foggia, Dipartimento di Scienze Economiche, Matematiche e Statistiche)

  • Angelo Sfrecola

    (University of Foggia, Dipartimento di Scienze Economiche, Matematiche e Statistiche)

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 Efficient Market Hypothesis 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 & Massimo Alfonso Russo & Angelo Sfrecola, 2010. "Financial time series and neural networks in a minority game context," Springer Books, in: Marco Corazza & Claudio Pizzi (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 153-162, Springer.
    • Handle: RePEc:spr:sprchp:978-88-470-1481-7_16
    DOI: 10.1007/978-88-470-1481-7_16
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