A case study on using neural networks to perform technical forecasting of forex

By Jingtao Yao, Chew Lim Tan. Neurocomputing, 2000

416 citations recorded by the source corpus.

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

View Paper: https://www.semanticscholar.org/paper/ab51276c81bdf798625910e6c0fe12817c0d42d7

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. Kin Keung Lai
2. Shouyang Wang
3. Eugen Diaconescu
4. Yoshiteru Nakamori
5. Matthieu Garcin

In Economics and Econometrics

1. Kin Keung Lai
2. Eugen Diaconescu
3. Shouyang Wang
4. Matthieu Garcin
5. Anthony Grace
6. Lean Yu
7. Ling Tang

In Management Science and Operations Research

1. Eugen Diaconescu
2. Yoshiteru Nakamori
3. Kin Keung Lai
4. Shouyang Wang
5. Matthieu Garcin
6. Anthony Grace

Citations in the corpus

• 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).”
• The use of NARX neural networks to predict chaotic time series (2008, background): “The most common operations are removing known systematicies as linear or nonlinear trends (constantly rising or descending of the average value), seasonality (periodic patterns due to a periodic influencing factor)[2][9][12].”
• Forecasting Foreign Exchange Rates With Artificial Neural Networks: A Review (2004, background): “Other earlier examples using ANN in exchange rates application include Zhang61 and Yao et al.56 Considerable research effort has gone into ANNs for forecasting exchange rates.”
• Utilizing artificial neural networks and genetic algorithms to build an algo-trading model for intra-day foreign exchange speculation (2013, background): “Yao [12] use MA5, MA10, MA20, MA60, andMA120 to feed a Feed Forward Neural Network that forecasts several currency rates.”
• Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates (2017, background): “But existing papers on the subject focus on the estimate of a piecewise fBm for different market phases without smoothing [2, 31], on describing the long-memory feature of FX time series [15, 46], on estimating a mBm with random Hurst exponents [11] or on the multifractal spectrum [16, 38].”