Predicting S&P500 Index Using Artificial Neural Network

Abstract : This paper studies artificial neural network algorithm as a means of modelling and forecasting the financial market data. Such method bypasses traditional statistical method to deal with financial time series data. A recurrent neural network model, Elman network, is implemented to incorporate autocorrelation in time series data. A 3-parameter model is chosen to fit and forecast S&P 500 index. The experimental data is from 2000–2007, to screen out the abnormal market environment after 2008 financial crisis.
Type de document :
Communication dans un congrès
Daoliang Li; Zhenbo Li. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. IFIP Advances in Information and Communication Technology, AICT-478 (Part I), pp.173-189, 2016, Computer and Computing Technologies in Agriculture IX. 〈1010.1007/978-3-319-48357-3_17〉
Liste complète des métadonnées

Littérature citée [11 références]  Voir  Masquer  Télécharger

https://hal.inria.fr/hal-01557817
Contributeur : Hal Ifip <>
Soumis le : jeudi 6 juillet 2017 - 15:50:02
Dernière modification le : jeudi 6 juillet 2017 - 15:54:12
Document(s) archivé(s) le : mercredi 24 janvier 2018 - 01:45:28

Fichier

 Accès restreint
Fichier visible le : 2019-01-01

Connectez-vous pour demander l'accès au fichier

Licence


Distributed under a Creative Commons Paternité 4.0 International License

Identifiants

Citation

Shanghong Li, Jiayu Zhang, Yan Qi. Predicting S&P500 Index Using Artificial Neural Network. Daoliang Li; Zhenbo Li. 9th International Conference on Computer and Computing Technologies in Agriculture (CCTA), Sep 2015, Beijing, China. IFIP Advances in Information and Communication Technology, AICT-478 (Part I), pp.173-189, 2016, Computer and Computing Technologies in Agriculture IX. 〈1010.1007/978-3-319-48357-3_17〉. 〈hal-01557817〉

Partager

Métriques

Consultations de la notice

55