Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Anastasakis, Leonidas; Mort, N.

doi:10.1016/j.eswa.2009.03.057

Cited by 51 publications

(17 citation statements)

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“…Statistical models such as autoregressive (AR) autoregressive moving average (ARMA) [4], autoregressive conditional heteroskedasticity (ARCH) [5][6], generalized autoregressive conditional heteroscedasticity (GARCH) [7][8][9] models and linear regression methods are among the first data driven approaches to be applied in the modeling of financial assets. However, some necessary premises in these methods restrict the scale of application [10]. Therefore, artificial neural networks (ANNs), as a novel and efficient alternative in financial forecasting, were proposed because of their salient predominance of nonlinearity, nonparametric characteristic, universal approximations [11][12][13].…”

Section: Introductionmentioning

confidence: 99%

Notice of Retraction: A Modified Model of Fuzzy Time Series for Forecasting Exchange Rates

Chi

Che

2010

2010 Second International Workshop on Education Technology and Computer Science

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Financial forecasting has become an important and challenging task for both researchers and investors. In order to improve the forecasting accuracy rate, in this paper, a modified heuristic model of fuzzy time series using FCM is proposed. Using the daily prices of USD/JPY and USD/CHY exchange rates as testing data, the empirical results show that the proposed method is able to get better forecasting results and higher accuracy than those of traditional econometric methods and some existing models of fuzzy time series.

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Section: Introductionmentioning

confidence: 99%

Notice of Retraction: A Modified Model of Fuzzy Time Series for Forecasting Exchange Rates

Chi

Che

2010

2010 Second International Workshop on Education Technology and Computer Science

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“…Overall lowest recorded accuracy is 38 % when forecasting is made for every 6 months and 12 data points are used for JPY/USD. Anasataskis and Mort (2009) use a combination of parametric and nonparametric self-organizing modeling approaches to predict exchange rates. The authors use neural networks in combination with analog complexing to forecast daily values of all six combinations of the currencies: British Pound, American Dollar, Deutsche Mark and Japanese Yen.…”

Section: Previous Workmentioning

confidence: 99%

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

2016

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This paper addresses problem of predicting direction and magnitude of movement of currency pairs in the foreign exchange market. The study uses Support Vector Machine with a novel approach for input data and trading strategy. The input data contain technical indicators generated from currency price data (i.e., open, high, low and close prices) and representation of these technical indicators as trend deterministic signals. The input data are also dynamically adapted to each trading day with genetic algorithm. The study incorporates a currency strength-biased trading strategy which selects the best pair to trade from the available set of currencies and is an improvement over the previous work. The accuracy of the prediction models are tested across several different sets of technical indicators and currency pair sets, spanning 5 years of historical data from 2010 to 2015. The experimental results suggest that using trend deterministic technical indicator signals mixed with raw data improves overall performance and dynamically adapting the input data to each trading period results in increased profits. Results also show that using a strength-biased trading strategy among a set of currency pair increases the overall prediction accuracy and profits of the models.

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“…The use of parametric and nonparametric modeling methods has been found to produce promising results when tested on foreign exchange rates [13]. The prediction of a longer term period has also been explored by researchers.…”

Section: Introductionmentioning

confidence: 98%

Comparative Study of FOREX Trading Systems Built with SVR+GHSOM and Genetic Algorithms Optimization of Technical Indicators

Brito

Oliveira

2012

2012 IEEE 24th International Conference on Tools With Artificial Intelligence

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Considerable effort has been made by researchers from various areas of science to forecast financial time series such as stock market and foreign exchange market (Forex). Recent studies have shown that the market can be outperformed by trading systems built with computational intelligence techniques. This study applies the Genetic Algorithm (GA) technique to optimize technical indicators parameters in order to maximize profit in the nine most tradable foreign exchange rates. Fifteen trading systems were created by combining four technical indicators optimized by the GA. It is then compared to an SVR+GHSOM model trading system and an analysis is performed to assess the most adaptable model in a period of international economic crisis. We report in the experiments that the GA model was far superior compared to the SVR+GHSOM model in the test period. The comparison considered performance measures such as profitability (ROI) and the maximum drawdown (MD). The experiments have also shown that it is possible to increase profit by adjusting the risk parameter (lots size), at the expense of increasing the risk.Foreign exchange rates prediction; support vector regression; self-organizing map; genetic algorithms; trading system

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Exchange rate forecasting using a combined parametric and nonparametric self-organising modelling approach

Cited by 51 publications

References 50 publications

Notice of Retraction: A Modified Model of Fuzzy Time Series for Forecasting Exchange Rates

Notice of Retraction: A Modified Model of Fuzzy Time Series for Forecasting Exchange Rates

A strength-biased prediction model for forecasting exchange rates using support vector machines and genetic algorithms

Comparative Study of FOREX Trading Systems Built with SVR+GHSOM and Genetic Algorithms Optimization of Technical Indicators

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