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.

Neural Network Forecasting of Tourism Demand

2005 ◽  
Vol 11 (3) ◽  
pp. 301-328 ◽  
Author(s):  
Sen Cheong Kon ◽  
Lindsay W. Turner
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Accurate Method ◽  
Future Research ◽  
Data Series ◽  
Structural Method ◽  
Short Term ◽  
Neural Network Models ◽  
The Neural Network ◽  
Short Term Forecasting

In times of tourism uncertainty, practitioners need short-term forecasting methods. This study compares the forecasting accuracy of the basic structural method (BSM) and the neural network method to find the best structure for neural network models. Data for arrivals to Singapore are used to test the analysis while the naïve and Holt-Winters methods are used for base comparison of simpler models. The results confirm that the BSM remains a highly accurate method and that correctly structured neural models can outperform BSM and the simpler methods in the short term, and can also use short data series. These findings make neural methods significant candidates for future research.

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.

scholarly journals Modeling Exchange Rates With Neural Networks

2011 ◽  
Vol 14 (1) ◽  
pp. 1 ◽  
Author(s):  
A. M. M. Jamal ◽  
Cuddalore Sundar
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Exchange Rates ◽  
Network Model ◽  
Neural Network Model ◽  
Econometric Model ◽  
Short Term ◽  
Bilateral Exchange ◽  
The Neural Network ◽  
The U.S

<span>This paper applies the neural network model to forecast bilateral exchange rates between the U.S. and Germany and U.S. and France. The predictions from the neural network model were compared to those based on a standard econometric model. The results suggest that the neural network model may have some advantages when frequent short term forecasts are needed.</span>

The Short-Term Forecast Model of Pork Price Based on CNN-GA

2012 ◽  
Vol 628 ◽  
pp. 350-358 ◽  
Author(s):  
Zhe Min Li ◽  
Shi Wei Xu ◽  
Li Guo Cui ◽  
Gan Qiong Li ◽  
Xiao Xia Dong ◽  
...  
Keyword(s):  
Neural Network ◽  
Forecast Accuracy ◽  
Forecast Model ◽  
Basic Structure ◽  
Retail Price ◽  
Practical Significance ◽  
Short Term ◽  
The Neural Network ◽  
High Prediction ◽  
Short Term Forecasting

After analyzing and reviewing the short-term forecasting methods research of pork price at home and abroad, a chaotic neural network model based on genetic algorithm (CNN-GA) was put forward according to the nonlinear characteristics of pork price,which established on the base of the chaotic theory and the neural network technology. Chosen the daily retail price data of the pork (streaky pork) from January 1, 2008 to June 11, 2012,we designed the basic structure of CNN-GA, and thentrainedit in order to attain the trained CNN-GA model. Finally, the trained CNN-GA model was used to predict the 20 days’ (from June 12, 2012 to July 1, 2012) retail price of pork (streaky pork) and then compared the predicted price with the real price to test the model’s forecast accuracy and application ability.The result shows that the model has high prediction precision, good fitting effect and hasan important reference and practical significance for the short-term price forecasting of the pork market.

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 Devizaárfolyam előrejelzés neurális háló modell segítségével = Foreign Exchange Rate Forecast Using Neural Network

2021 ◽  
Vol 16 (1) ◽  
pp. 117-137
Author(s):  
Zsolt Lakatos
Keyword(s):  
Neural Network ◽  
Network Models ◽  
Foreign Exchange Rate ◽  
Neural Models ◽  
Short Term ◽  
Neural Network Models ◽  
Technical Indicators ◽  
The Neural Network ◽  
Master's Thesis ◽  
Technical Indicator

Modelljeimben a technikai indikátorok használatát kapcsolom össze a neurális hálós modellek előrejelző képességeivel. A technikai indikátorok használata mellett szól, hogy rövid távon a pénzügyi idősorok autokorreláltak, a neurális modellek pedig nemlineáris kapcsolatok modellezésére alkalmasak. A kapott eredmények révén azt a következtetést vontam le, hogy ugyan a neurális háló modellek optimalizációs képessége nagyon jó és alkalmazásukkal a megfelelő technikai indikátorok is meghatározhatók, de csak lassan képesek rátanulni az adatokra, így még a legkisebb tranzakciós költség alkalmazása mellett is csak a kezdeti befektetésünk elvesztését tudjuk halogatni. My present paper is the shortened version of my master's thesis in finance presented in November 2015, in which I presented the results of the research implemented in the Training Center for Bankers. In my models I combine the use of technical indicators with predictive capabilities of neural network models. The use of a technical indicator suggests that in the short term the financial timeseries are autocorrelated, and neural models are suitable for modeling nonlinear relationships. Based on the results I concluded that although the optimization capabilities of the neural network models are very good and their application can be determined by the appropriate technical indicators, but learning from timeseries data takes too much time, so even with the smallest transaction cost we can only delay the loss of our initial investment.

Efficiency of Using Artificial Neural Network for Short-Term Load Forecasting

2015 ◽  
Vol 792 ◽  
pp. 312-316 ◽  
Author(s):  
Svetlana Rodygina ◽  
Valentina Lyubchenko ◽  
Alexander Rodygin
Keyword(s):  
Neural Network ◽  
Electricity Market ◽  
Load Forecasting ◽  
Operating Conditions ◽  
Short Term ◽  
The Neural Network ◽  
Short Term Load Forecasting ◽  
Neural Network Algorithm ◽  
Artificial Neural ◽  
Short Term Forecasting

Using artificial neural networks (ANN) for short-term load forecasting is an efficient method to get the best result. Considered problem of short-term load forecasting shows that the accuracy of short-term forecasting models and methods significantly influences on the further planning of operating conditions at the modern electricity market. The obtained error for short-term load forecasting using the neural network algorithm is 2.78%.

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