Bootstrap Methods for Foreign Currency Exchange Rates Prediction

He, Haibo; Shen, Xiaoping

doi:10.1109/ijcnn.2007.4371141

Cited by 8 publications

(5 citation statements)

References 21 publications

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“…In [7], ridge polynomial neural networks are employed to forecast the future trends of financial time series data. The bootstrap ensemble method is used to predict the exchange rates of six foreign currencies in [9], and the simulation results provided in this paper show the bagging method is better than the single model.…”

Section: Introductionmentioning

confidence: 94%

A novel portfolio optimization method for foreign currency investment

Cao

Chandramouli

2009

2009 International Joint Conference on Neural Networks

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In this paper, we present the research of a foreign currency investment framework involving the prediction of the foreign currency exchange rates and the portfolio optimization under certain constrains. We adopt two machine learning methods, support vector machines (SVMs) and neural networks (NNs), as well as the traditional moving average method, to predict the exchange rates for three foreign currencies including Australia Dollars (ADD), European Euro (EDR), and Swiss Francs (CHF). Based on these forecastings, we choose two out of the three currencies listed above and build a portfolio by adopting multi-objective portfolio optimization techniques by maximizing the return and minimizing the risk. Karush-KuhnTucker (KKT) theorem guarantees that the optimal portfolio is reachable. Simulation results show that the optimal portforlio investment can achieve superior return performance compared with three single currency investment benchmarks.

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Section: Introductionmentioning

confidence: 94%

A novel portfolio optimization method for foreign currency investment

Cao

Chandramouli

2009

2009 International Joint Conference on Neural Networks

Self Cite

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“…Actually, some researchers suggest applying ensemble methods in order to improve the regression and classification performance. In [32], He and Shen have used a bootstrap method based on neural networks to construct multiple learning models and combined the output of these models to predict currency exchange rates.…”

Section: Related Workmentioning

confidence: 99%

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

Chihab

Bousbaa

Chihab

et al. 2019

Applied Computational Intelligence and Soft Computing

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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 indicators. Having a good strategy to buy and sell can make a profit from the above changes. A successful strategy in Forex should take into consideration the relation between benefits and risks. In this work, we propose an intraweek foreign exchange speculation strategy for currency markets based on a combination of technical indicators. This system has a two-level decision and is composed of the Probit regression model and rules discovery using Random Forest. There are two minimum requirements for a trading strategy: a rule to enter the market and a rule to exit it. Our proposed system, to enter the currency market, should validate two conditions. First, it should validate Random Forest access rules over the following week while in the second one the predicted value of the next day using Probit should be positive. To exit the currency market just one negative warning from Probit or Random Forest is enough. This system was used to develop dynamic portfolio trading systems. The profitability of the model was examined for USD/(EUR, JYN, BRP) variation within the period from January 2014 to January 2016. The proposed system allows improving the prediction accuracy. This indicates a good prediction of the behavior market and it helps to identify the good times to enter it or to leave it.

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“…Instead of using (2), one can also define the probability distance by an alternative KL divergence as follows:…”

Section: B Aa Rulementioning

confidence: 99%

“…Over the past decade, many theoretical analyses, practical algorithms, and empirical studies have been proposed in this field. Ensemble learning methods also have been widely applied in many real-world applications, including Web mining [1], financial engineering [2], geosciences and remote sensing [3], [4], biomedical data analysis [5], [6], decisionmaking and supporting systems [7]- [9], surveillance [10], homeland security and defense [11], and others.…”

Section: Introductionmentioning

confidence: 99%

SSC: A Classifier Combination Method Based on Signal Strength

Cao

2012

IEEE Trans. Neural Netw. Learning Syst.

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We propose a new classifier combination method, the signal strength-based combining (SSC) approach, to combine the outputs of multiple classifiers to support the decision-making process in classification tasks. As ensemble learning methods have attracted growing attention from both academia and industry recently, it is critical to understand the fundamental issues of the combining rule. Motivated by the signal strength concept, our proposed SSC algorithm can effectively integrate the individual vote from different classifiers in an ensemble learning system. Comparative studies of our method with nine major existing combining rules, namely, geometric average rule, arithmetic average rule, median value rule, majority voting rule, Borda count, max and min rule, weighted average, and weighted majority voting rules, is presented. Furthermore, we also discuss the relationship of the proposed method with respect to margin-based classifiers, including the boosting method (AdaBoost.M1 and AdaBoost.M2) and support vector machines by margin analysis. Detailed analyses of margin distribution graphs are presented to discuss the characteristics of the proposed method. Simulation results for various real-world datasets illustrate the effectiveness of the proposed method.

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Bootstrap Methods for Foreign Currency Exchange Rates Prediction

Cited by 8 publications

References 21 publications

A novel portfolio optimization method for foreign currency investment

A novel portfolio optimization method for foreign currency investment

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

SSC: A Classifier Combination Method Based on Signal Strength

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