Investigation of financial market prediction by recurrent neural network

By Aleksandras Vytautas Rutkauskas, Algirdas Antano Maknickas. 2011

33 citations recorded by the source corpus.

Subfields: 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 Time-Series & Forecasting

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

Citations in the corpus

• Financial Time Series Forecasting with Deep Learning : A Systematic Literature Review: 2005-2019 (2019, methodology): “The authors of [178] also used EVOLINO RNN + orthogonal input data for predicting USD/JPY and XAU/USD prices for different periods.”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[141] used Evolino RNN for portfolio selection and return prediction accordingly.”
• On stock return prediction with LSTM networks (2017, background): “However most research uses neural nets of feedforward architecture Maknickiene et al. (2011) is one example where they use recurrent networks for financial analysis.”