Neural Networks to Predict Financial Time Series in a Minority Game

Dipartimento di Scienze Economiche, Matematiche e Statistiche
Università degli Studi di Foggia
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Neural Networks to Predict Financial Time Series
in a Minority Game Context
Luca Grilli e Angelo Sfrecola
Quaderno n. 14/2005
“Esemplare fuori commercio per il deposito legale agli effetti della legge 15 aprile 2004 n. 106”
Quaderno riprodotto al
Dipartimento di Scienze Economiche, Matematiche e Statistiche
nel mese di giugno 2005 e
depositato ai sensi di legge
Authors only are responsible for the content of this preprint.
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Dipartimento di Scienze Economiche, Matematiche e Statistiche, Largo Papa Giovanni Paolo II, 1,
71100 Foggia (Italy), Phone +39 0881-75.37.30, Fax +39 0881-77.56.16
Neural Networks to Predict Financial Time Series
in a Minority Game Context∗
Luca Grilli†
Angelo Sfrecola‡
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.
Subject Classification: 91A80, 91A26, 92B20, 62M45.
JEL Classification: C45, C70, C22, G14.
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∗ Research
supported by Local Project 2004 (EX 40%) Università di Foggia
author) Dipartimento di Scienze Economiche, Matematiche e Statistiche, Università degli Studi di Foggia, Via IV Novembre 1, I-71100 Foggia, Italy e-mail:
[email protected]
‡ Research financially supported by Dipartimento di Scienze Economiche, Matematiche e
Statistiche, Università degli Studi di Foggia
† (Corresponding
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