Forecasting stock market indexes using principle component analysis and stochastic time effective neural networks

By Jie Wang, Jun Wang. 2015

148 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Economics and Econometrics, Management Science and Operations Research

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Weiwei Jiang
2. Saman Haratizadeh
3. Ehsan Hoseinzade

In Economics and Econometrics

1. Weiwei Jiang
2. Saman Haratizadeh
3. Ehsan Hoseinzade

In Management Science and Operations Research

1. Weiwei Jiang
2. Saman Haratizadeh
3. Ehsan Hoseinzade

Citations in the corpus

• Applications of deep learning in stock market prediction: recent progress (2020, methodology): “…(IDLNN) where each layer is composed of a set of increasingdecreasing-linear processing units (de A. Arajo et al., 2019), stochastic time effective function neural network (STEFNN) (Wang & Wang, 2015), radial basis function network (RBFN) that uses radial basis functions as activation functions.”
• CNNpred: CNN-based stock market prediction using a diverse set of variables (2019, methodology): “Artificial neural networks (ANN) and support vector machine (SVM) are the most common algorithms that have been utilized for this purpose (Guresen et al., 2011; Kara et al., 2011; Wang & Wang, 2015).”