Intraday Ultra-Short-Term Forecasting of Foreign Exchange Rates using an Ensemble of Neural Networks based on Conventional Technical Indicators

2020 ◽  
Author(s):  
Theodoros Zafeiriou ◽  
Dimitris Kalles
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Foreign Exchange Rates ◽  
Short Term ◽  
Technical Indicators ◽  
Short Term Forecasting

SHORT-TERM TREND PREDICTION OF FOREIGN EXCHANGE RATES WITH A NEURAL-NETWORK BASED ENSEMBLE OF FINANCIAL TECHNICAL INDICATORS

2013 ◽  
Vol 22 (03) ◽  
pp. 1350016 ◽  
Author(s):  
THEODOROS ZAFEIRIOU ◽  
DIMITRIS KALLES
Keyword(s):  
Neural Network ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Short Term ◽  
Trend Prediction ◽  
Technical Indicators ◽  
The Neural Network ◽  
Exchange Markets ◽  
Short Term Forecasting ◽  
One Year

This paper is about designing, developing and training a neural network for short-term forecasting of buy-sell trends in foreign exchange markets. We use a set of established financial technical indicators as inputs to the neural network and we develop the architecture to predict a trend and then train the network based on the accuracy of the prediction. We perform extensive real time testing with the closing prices (one per minute) of the USD/EUR exchange rates for a one-year period. The overall approach delivers a system that predicts trends substantially better than individual technical indicators.

Forecasting Short-Term Exchange Rates

2011 ◽  
pp. 195-212
Author(s):  
Leong-Kwan Li ◽  
Wan-Kai Pang ◽  
Wing-Tong Yu ◽  
Marvin D. Troutt
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Recurrent Neural Networks ◽  
Foreign Exchange Rates ◽  
Short Term ◽  
Financial Strategy ◽  
Term Trend ◽  
Exchange Markets ◽  
Short Term Trend

Movements in foreign exchange rates are the results of collective human decisions, which are the results of the dynamics of their neurons. In this chapter, we demonstrate how to model these types of market behaviors by recurrent neural networks (RNN). The RNN approach can help us to forecast the short-term trend of foreign exchange rates. The application of forecasting techniques in the foreign exchange markets has become an important task in financial strategy. Our empirical results show that a discrete-time RNN performs better than the traditional methods in forecasting short-term foreign exchange rates.

scholarly journals Modeling of short-term dynamics of foreign exchange rates using deep neural networks

2020 ◽  
Vol 3-4 (276-277) ◽  
pp. 153-163
Author(s):  
Vasily Derbentsev ◽  
◽  
Vitalii Bezkorovainyi ◽  
Andrey Ovcharenko ◽  
◽  
...  
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Deep Neural Networks ◽  
Foreign Exchange Rates ◽  
Short Term

Neural Networks for Technical Forecasting of Foreign Exchange Rates

2002 ◽  
pp. 189-204
Author(s):  
Jing Tao Yao ◽  
Chew Lim Tan
Keyword(s):  
Neural Networks ◽  
Exchange Rates ◽  
Foreign Exchange ◽  
Time Series Data ◽  
Series Data ◽  
Technical Indicators ◽  
Currency Exchange ◽  
The Neural Network ◽  
Out Of Sample ◽  
Japanese Yen

This chapter describes the application of neural networks in foreign exchange rate forecasting between American dollar and five other major currencies: Japanese yen, Deutsch mark, British pound, Swiss franc and Australian dollar. Technical indicators and time series data are fed to neural networks to mine, or discover, the underlying “rules” of the movement in currency exchange rates. The results presented in this chapter show that without the use of extensive market data or knowledge, useful prediction can be made and significant paper profit can be achieved for out-of-sample data with simple technical indicators. The neural-network-based forecasting is also shown to compare favorably with the traditional statistical approach.

Sign in / Sign up

Export Citation Format

Share Document