Details

Autor(en) / Beteiligte

• Lin, Xiaowei
• Yang, Zehong
• Song, Yixu

Titel

Short-term stock price prediction based on echo state networks

Ist Teil von

• Expert systems with applications, 2009-04, Vol.36 (3), p.7313-7317

Ort / Verlag

Elsevier Ltd

Erscheinungsjahr

2009

Quelle

Alma/SFX Local Collection

Beschreibungen/Notizen

• Neural network has been popular in time series prediction in financial areas because of their advantages in handling nonlinear systems. This paper presents a study of using a novel recurrent neural network–echo state network (ESN) to predict the next closing price in stock markets. The Hurst exponent is applied to adaptively determine initial transient and choose sub-series with greatest predictability during training. The experiment results on nearly all stocks of S&P 500 demonstrate that ESN outperforms other conventional neural networks in most cases. Experiments also indicate that if we include principle component analysis (PCA) to filter noise in data pretreatment and choose appropriate parameters, we can effectively prevent coarse prediction performance. But in most cases PCA improves the prediction accuracy only a little.