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A Hidden Markov model approach to classify and predict the sign of financial local trends

Bicego, Manuele and Grosso, Enrico and Otranto, Edoardo (2008) A Hidden Markov model approach to classify and predict the sign of financial local trends. In: Structural, Syntactic, and Statistical Pattern Recognition, Joint IAPR International Workshop, SSPR & SPR 2008: proceedings, 4-6 December 2008, Orlando, USA. Berlin - Heidelberg, Springer. p. 852-861. (Lecture Notes in Computer Science, 5342/2008). ISBN 978-3-540-89688-3. Conference or Workshop Item.

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DOI: 10.1007/978-3-540-89689-0_89

Abstract

In the field of financial time series analysis it is widely accepted that the returns (price variations) are unpredictable in the long period [1]; nevertheless, this unappealing constraint could be somehow relaxed if sufficiently short time intervals are considered. In this paper this alternative scenario is investigated with a novel methodology, aimed at analyzing short (local) financial trends for predicting their sign (increase or decrease). This peculiar problem needs specific models – different from standard techniques used for estimating the volatility or the returns – able to capture the asymmetries between increase and decrease periods in the short time. This is achieved by modeling directly the signs of the local trends using two separate Hidden Markov models, one for positive and one for negative trends. The approach has been tested with different financial indexes, with encouraging results also in comparison with standard methods.

Item Type:Conference or Workshop Item (Paper)
ID Code:2238
Status:Published
Uncontrolled Keywords:Trends for predicting, financial indexes, Markov model trends for predicting, financial indexes, Markov model
Subjects:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Area 09 - Ingegneria industriale e dell'informazione > ING-INF/05 Sistemi di elaborazione delle informazioni
Divisions:001 Università di Sassari > 01 Dipartimenti > Economia, impresa, regolamentazione
Publisher:Springer
ISBN:978-3-540-89688-3
Deposited On:18 Aug 2009 10:07

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