Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks

Machová, Veronika; Mareček, Jan

doi:10.1051/shsconf/20196101012

Cited by 9 publications

(10 citation statements)

References 18 publications

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“…To illustrate this, Table 4 shows the aggregate of the residuals for the equalized time series. Machova and Marecek (2019).…”

Section: Neural Structure Amentioning

confidence: 99%

“…There is no doubt that changeability of exchange rates has a serious impact on the decisions of all entities operating in the international market for goods and services. For this reason, it is essential to set it correctly (Machova and Marecek 2019).…”

Section: Introductionmentioning

confidence: 99%

“…Actual and predicted (according to retained neural networks) development of the CZK/RMB exchange rate for the period from 14 July to 21 October 2018 (Source: Own research; according toMachova and Marecek 2019).…”

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confidence: 99%

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Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

Rowland

Lăzăroiu

Podhorská

2020

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The global nature of the Czech economy means that quantitative knowledge of the influence of the exchange rate provides useful information for all participants in the international economy. Systematic and academic research show that the issue of estimating the Czech crown/Chinese yuan exchange rate, with consideration for seasonal fluctuations, has yet to be dealt with in detail. The aim of this contribution is to present a methodology based on neural networks that takes into consideration seasonal fluctuations when equalizing time series by using the Czech crown and Chinese yuan as examples. The analysis was conducted using daily information on the Czech crown/Chinese yuan exchange rate over a period of more than nine years. This is the equivalent of 3303 data inputs. Statistica software, version 12 by Dell Inc. was used to process the input data and, subsequently, to generate multi-layer perceptron networks and radial basis function neural networks. Two versions of neural structures were produced for regression purposes, the second of which used seasonal fluctuations as a categorical variable–year, month, day of the month and week—when the value was measured. All the generated and retained networks had the ability to equalize the analyzed time series, although the second variant demonstrated higher efficiency. The results indicate that additional variables help the equalized time series to retain order and precision. Of further interest is the finding that multi-layer perceptron networks are more efficient than radial basis function neural networks.

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“…To illustrate this, Table 4 shows the aggregate of the residuals for the equalized time series. Machova and Marecek (2019).…”

Section: Neural Structure Amentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

Rowland

Lăzăroiu

Podhorská

2020

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“…Chinese value added goods are process in CR in other to easily reach EU western market. China´s economy is the second largest in the world which makes them significant actors in economic global relations [15].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

Vochоzka

Rowland

2020

SHS Web Conf.

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The objective of the contribution is to introduce a methodology for considering seasonal fluctuations in equalizing time series using artificial neural networks on the example of the Czech Republic and the People´s Republic of China trade balance. The data available is the data on monthly balance for the period between January 2000 and July 2018, that is, 223 input data. The unit is Euro. The data for the analysis are available on the World Bank web pages etc. Regression analysis is carried out using artificial neural networks. There are two types on neural networks generated, multilayer perceptron networks (MLP) and radial basis function networks (RBF). In order to achieve the optimal result, two sets of neural structures are generated. There are generated a total of 10,000 neural structures, out of which only 5 with the best characteristics are retained. Finally, the results of both groups of retained neural networks are compared. The contribution this paper brings is the involvement of variables that are able to forecast a possible seasonal fluctuation in the time series development when using artificial neural networks. Moreover, neural networks have been identified that achieve slightly better results than other networks, specifically these are the neural networks 1. MLP 13-6-1 and 3. MLP 13-8-1.

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“…The idea of technology distortion arises from the fact that innovative approaches have been used to address business challenges and to find the best business strategies [6]. The situation described above indicates market inefficiency, which casts doubt on the idea of an effective market theory, but is in line with the adaptive market hypothesis [7]. Galeshchuk [8] uses shallow neural network that surpasses a random walk model which confirms the idea of market inefficiency.…”

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confidence: 99%

Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC

Vrbka

Vochоzka

2020

SHS Web Conf.

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The paper’s objective is to propose a particular methodology to be used to regard seasonal fluctuations on balancing time series while using artificial neural networks based on the example of imports from the People's Republic of China (PRC) to the USA (US). The difficulty of forecasting the volume of foreign trade is usually given by the limitations of many conventional forecasting models. For the improvement of forecasting it is necessary to propose an approach that would hybridize econometric models and artificial intelligence models. Data for an analysis to be conducted are available on the World Bank website, etc. Information on US imports from the PRC will be used. Each forecast is given by a certain degree of probability which it will be fulfilled with. Although it appeared before the experiment that there was no reason to include the categorical variable to reflect seasonal fluctuations of the USA imports from the PRC, the assumption was not correct. An additional variable, in the form of monthly value measurements, brought greater order and accuracy to the balanced time series.

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Estimation of the development of Czech Koruna to Chinese Yuan exchange rate using artificial neural networks

Cited by 9 publications

References 18 publications

Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

Use of Neural Networks to Accommodate Seasonal Fluctuations When Equalizing Time Series for the CZK/RMB Exchange Rate

Forecasting trade balance of Czech Republic and People´s Republic of China in equalizing time series and considering seasonal fluctuations

Considering seasonal fluctuations on balancing time series with the use of artificial neural networks when forecasting US imports from the PRC

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