Financial Time Series and Neural Networks in a Minority Game Context

Luca Grilli, Angelo Sfrecola () and Massimo Alfonso Russo ()

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, 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.

Keywords: minority Game; learning algorithms; neural networks; financial time series; Efficient Market Hypothesis. (search for similar items in EconPapers)
Date: 2010-04
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Published in Mathematical and Statistical Methods for Actuarial Sciences and Finance, Corazza, Marco; Claudio, Pizzi (Eds.), ISBN: 978-88-470-1480-0, Springer, 2010.

There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:ufg:qdsems:lg_maf_2009

Access Statistics for this paper

More papers in Quaderni DSEMS from Dipartimento di Scienze Economiche, Matematiche e Statistiche, Universita' di Foggia Largo Papa Giovanni Paolo II, 1 -71100- Foggia (I). Contact information at EDIRC.
Bibliographic data for series maintained by Luca Grilli ().