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

Luca Grilli and Angelo Sfrecola

Quaderni DSEMS from Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia

Abstract: 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.

Keywords: Minority Game; Learning Algorithms; Neural Networks; Financial Time Series; Efficient Market Hypotesis (search for similar items in EconPapers)
JEL-codes: C22 C45 C70 G14 (search for similar items in EconPapers)
Date: 2005-06
New Economics Papers: this item is included in nep-cmp, nep-ets, nep-fin and nep-gth
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