Stock market index prediction using deep neural network ensemble

By Bing Yang, Zi-Jia Gong, Wenqi Yang. 2017

53 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Management Science and Operations Research, Signal Processing, Time-Series & Forecasting

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. Omer Berat Sezer
2. M. Ugur Gudelek
3. A. Ozbayoglu

In Management Science and Operations Research

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

In Signal Processing

1. A. Ozbayoglu
2. Omer Berat Sezer
3. M. Ugur Gudelek

In Time-Series & Forecasting

1. M. Ugur Gudelek
2. Omer Berat Sezer
3. A. Ozbayoglu
4. Weiwei Jiang
5. Longbing Cao

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [142] created an ensemble network of several Backpropagation and ADAM models for trend prediction.”
• Applications of deep learning in stock market prediction: recent progress (2020, unknown): “Principal component analysis (PCA) is a commonly used transformation technique that uses Singular Value Decomposition of the input data to project it to a lower dimensional space and has been used in Gao & Chai (2018); Zhang et al. (2019b); Zhong & Enke (2017); Wang & Wang (2015); Singh & Srivastava”