A hierarchical Deep neural network design for stock returns prediction

By Oussama Lachiheb, M. Gouider. 2018

26 citations recorded by the source corpus.

Abstract We present in this paper a hierarchical Deep Neural Network for stock returns prediction. This DNN is trained in a high frequency context, we use 5 minutes returns of TUNINDEX stocks in a period of 4 years. The designed network aims to predict the next 5 minutes return of a given stock. The predictive power of our network is improved by the hierarchical design and stocks classification while the training process is simplified by dimensionality reduction techniques. Experimental study shows an accuracy up to 71% and a considerable improvement comparing to recent related works.

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

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

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): “[143] TUNINDEX 2013-2017 Log returns of index data 5min DNN with hierarchical input Accuracy, MSE Java”