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Higher order and recurrent neural architectures for trading the EUR/USD exchange rate

Christian L. Dunis, Jason Laws, Georgios Sermpinis
2011 Quantitative finance (Print)  
The motivation for this paper is to investigate the use of alternative novel neural network architectures when applied to the task of forecasting and trading the Euro/Dollar (EUR/USD) exchange rate.  ...  More specifically, the trading performance of the six models is investigated in a forecast and trading simulation competition on the EUR/USD time series over a period of 8 years.  ...  such as the EUR/USD exchange rate.  ... 
doi:10.1080/14697680903386348 fatcat:2yaclj2grfdcpjrgwl42wf3nt4

Forex exchange rate forecasting using deep recurrent neural networks

Alexander Jakob Dautel, Wolfgang Karl Härdle, Stefan Lessmann, Hsin-Vonn Seow
2020 Digital Finance  
The paper examines the potential of deep learning for exchange rate forecasting.  ...  Empirical results indicate the suitability of deep networks for exchange rate forecasting in general but also evidence the difficulty of implementing and tuning corresponding architectures.  ...  This general observation also holds roughly true for the accuracy for three of the four time series, but not for the EUR/USD exchange rate.  ... 
doi:10.1007/s42521-020-00019-x fatcat:toyyq63thrh5pkgx5na6utyb3m

Combining Empirical Mode Decomposition with Neural Networks for the Prediction of Exchange Rates

J. Mouton, A. J. Hoffman
2014 Proceedings of the International Conference on Neural Computation Theory and Applications  
at a significance level of 90% for the EUR/USD and USD/JPY exchange rates.  ...  the forecast horizon (70 minutes for the EUR/USD predictions).  Subsample the filtered and unfiltered exchange rates to the required level (5, 7 and 35 minutes for the EUR/USD predictions).  Convert  ...  Appendix C EUR/USD at the optimal forecast horizon of 35 minutes  ... 
doi:10.5220/0005130702440249 dblp:conf/ijcci/MoutonH14 fatcat:q6trlaqja5fkhlcubnfykgfhhq

Currency trading in volatile markets: Did neural networks outperform for the EUR/USD during the financial crisis 2007–2009?

Christian L Dunis, Jason Laws, Ulrike Schilling
2012 Journal of Derivatives & Hedge Funds  
The motivation for this article is to check whether neural network models have remained a superior method for forecasting the EUR/USD exchange rate during the financial crisis of 2007-2009.  ...  It is shown that the HONN structure gives the overall best results on a simple trading simulation; however, for an advanced trading simulation with a confirmation filter, the MLP outperforms all other  ...  the EUR/USD exchange rate.  ... 
doi:10.1057/jdhf.2011.31 fatcat:koujbb2hxzbqvp4wyao7225ieq

How successful is the G7 in managing exchange rates?

Marcel Fratzscher
2009 Journal of International Economics  
ECB Working Paper Series No 952 October 2008 Notes: Panel A shows the direction criterion for the volatility of the three bilateral G3 currency pairs jointly (USD-EUR, USD-YEN, YEN-EUR), and Panel B for  ...  The classification yields 29 G7 meetings with an indication on exchange rate levels, which allows inferring desired directional changes for 58 currency pairs, though for various parts of the analysis below  ... 
doi:10.1016/j.jinteco.2009.06.002 fatcat:fsepujm44jgu7abfjligaeichq

Development and Performance Evaluation of Adaptive Hybrid Higher Order Neural Networks for Exchange Rate Prediction

Sarat Chandra Nayak
2017 International Journal of Intelligent Systems and Applications  
Friedman"s test and Nemenyi post-hoc test are conducted for statistical significance of the results.  ...  Performance of these hybrid models is evaluated through prediction of one-step-ahead exchange rates of some real stock market.  ...  ACKNOWLEDGEMENT The author likes to thank anonymous reviewer(s) and the Chief Editor for their valuable comments and suggestions which have certainly improved the quality of this paper.  ... 
doi:10.5815/ijisa.2017.08.08 fatcat:tqobsmmdubeerkyj2xqoyqweum

Currency exchange prediction using machine learning, genetic algorithms and technical analysis [article]

Gonçalo Abreu, Rui Neves, Nuno Horta
2018 arXiv   pre-print
In this work, an architecture for automatic feature selection is proposed to optimize the cross validated performance estimation of a Naive Bayes model using a genetic algorithm.  ...  The features selected and the model decision boundary are visualized using the algorithm t-Distributed Stochastic Neighbor embedding.  ...  A currency trading pair defines the ratio in which euros and dollars will be exchanged at the current market rate, i.e. if the current rate of EUR/USD is 1,18 it means that at this moment if an individual  ... 
arXiv:1805.11232v1 fatcat:m4vphi3cdrgl7nsslgcm3rsml4

Probability distributions, trading strategies and leverage: an application of Gaussian mixture models

Andreas Lindemann, Christian L. Dunis, Paulo Lisboa
2004 Journal of Forecasting  
Firstly, to assess the merit of estimating probability density functions rather than level or classification estimations on a oneday-ahead forecasting task of the EUR/USD time series.  ...  of more sophisticated trading strategies and leverage.  ...  Since this algorithm contains regularisation, namely the Bayesian Evidence Scheme, it is not necessary to select a stopping point in the way described in section 4.5. for the benchmark MLP 27 .  ... 
doi:10.1002/for.935 fatcat:dysg3fjta5a5pkafemhbnzlgue

Machine learning based forecasting of significant daily returns in foreign exchange markets [article]

Firuz Kamalov, Ikhlaas Gurrib
2020 arXiv   pre-print
Our findings hold across different currency pairs, significance levels, and time horizons indicating the robustness of the proposed method.  ...  A key contribution is the novel use of outlier detection methods for this purpose.  ...  We begin our analysis with application of the forecasting methods to the EUR/USD exchange rate data. The graph of EUR/USD daily returns is given in Figure 4 .  ... 
arXiv:2009.10065v1 fatcat:zdpd5grutrd5hbempqomyjurqi

Stochastic and genetic neural network combinations in trading and hybrid time-varying leverage effects

Georgios Sermpinis, Charalampos Stasinakis, Christian Dunis
2014 Journal of international financial markets, institutions, and money  
Firstly, we apply a Multi-Layer Perceptron (MLP), a Recurrent Neural Network (RNN) and a Psi-Sigma Network (PSN) architecture in a forecasting and trading exercise on the EUR/USD, EUR/GBP and EUR/CHF exchange  ...  rates and explore the utility of Kalman Filter, Genetic Programming (GP) and Support Vector Regression (SVR) algorithms as forecasting combination techniques.  ...  Rate 1 1 1 EUR/USD Exch. Rate 1 1 2 EUR/USD Exch. Rate 1 3 1 EUR/USD Exch. Rate 3 4 2 EUR/USD Exch. Rate 2 3 3 EUR/USD Exch. Rate 4 4 2 EUR/USD Exch.  ... 
doi:10.1016/j.intfin.2014.01.006 fatcat:vr37jdfmc5cxfndq73566d6frq

Deep LSTM with Reinforcement Learning Layer for Financial Trend Prediction in FX High Frequency Trading Systems

2019 Applied Sciences  
The trading system has been validated over several financial years and on the EUR/USD cross confirming the high performance in terms of Return of Investment (98.23%) in addition to a reduced drawdown (  ...  HFT strategies have reached considerable volumes of commercial traffic, so much so that it is estimated that they are responsible for most of the transaction traffic of some stock exchanges, with percentages  ...  Conflicts of Interest: The authors declare no conflict of interest.  ... 
doi:10.3390/app9204460 fatcat:qzzo3bru3nasfonnhuth456qa4

Algo-Trading Strategy for Intraweek Foreign Exchange Speculation Based on Random Forest and Probit Regression

Younes Chihab, Zineb Bousbaa, Marouane Chihab, Omar Bencharef, Soumia Ziti
2019 Applied Computational Intelligence and Soft Computing  
In the Forex market, the price of the currencies increases and decreases rapidly based on many economic and political factors such as commercial balance, the growth index, the inflation rate, and the employment  ...  This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest.  ...  The first dataset utilized for this research encompasses 650 days of past currency rates of the EUR/USD currency pairs. An example of a currency pair is the EUR/USD.  ... 
doi:10.1155/2019/8342461 fatcat:oxssropr7fe3vbjgg452hqdftm

On the relationship between the prices of oil and the precious metals: Revisiting with a multivariate regime-switching decision tree

Philippe Charlot, Vêlayoudom Marimoutou
2014 Energy Economics  
This study examines the volatility and correlation and their relationships among the euro/US dollar exchange rates, the S&P500 equity indices, and the prices of WTI crude oil and the precious metals (gold  ...  Our results are categorized into three groups, namely (1) exchange rates and oil, (2) S&P500 indices, and (3) precious metals.  ...  Figure 2 : 2 Plots of EUR/USD, S&P500, WTI, Gold, Silver and Platinum series in level (subplot 2(a)) and in returns (subplot 2(b) specification 3 . 3 Both models were estimated using maximum likelihood  ... 
doi:10.1016/j.eneco.2014.04.021 fatcat:f6ejhu2svfa3tdaxrxg4w6nfpu

Using Recurrent Neural Networks To Forecasting of Forex [article]

V.V.Kondratenko, Yu. A Kuperin
2003 arXiv   pre-print
The trained recurrent neural networks forecast the exchange rates between American Dollar and four other major currencies, Japanese Yen, Swiss Frank, British Pound and EURO.  ...  Time series data and technical indicators such as moving average, are fed to neural nets to capture the underlying "rules" of the movement in currency exchange rates.  ...  This shows that all the studied currencies are persistent. The highest value is 0.570 for the exchange rate of EUR/USD.  ... 
arXiv:cond-mat/0304469v1 fatcat:j7zghtfaancg7elt3oyg6nwxuq

Trading Macro-Cycles of Foreign Exchange Markets Using Hybrid Models

Joseph Zhi Bin Ling, Albert K. Tsui, Zhaoyong Zhang
2021 Sustainability  
In contrast, this study takes the novel approach of forecasting and trading the longer-term trends (macro-cycles) of exchange rates.  ...  The finding that the government bond yield differentials and CPI differentials are the important factors in exchange rate forecasts further implies that interest rate parity and PPP have strong influence  ...  Acknowledgments: The authors wish to thank the journal editor and two anonymous referees for their helpful comments and suggestions which have greatly improved the quality of the paper.  ... 
doi:10.3390/su13179820 fatcat:pv7mgyxxongphf6d57gfwomwzm
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