Financial volatility trading using recurrent neural networks

By Peter Tino, Christian Schittenkopf, Georg Dorffner. 2001

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

In Economics and Econometrics

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

In Management Science and Operations Research

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): “[160] DAX, FTSE100, call/put options 1991-1998 Price data * * MM, RNN Ewa-measure, iv, daily profits’ mean and std -”
• Deep Learning for Financial Applications : A Survey (2020, methodology): “[67] used The Deutscher Aktienindex (DAX), London Financial Times Stock Exchange Index (FTSE)100, call and put options prices to predict the changes with Markov models and used the financial time series data to predict volatility changes with RNN.”
• Using Recurrent Neural Networks To Forecasting of Forex (2003, background): “(Jingtao Yao et al., 2000; Castiglione, 2001; Kuperin et al., 2001; Lee et al., 1997; Tino et al., 2001; McCluskey, 1993).”