Speculative Opportunities for Currency Exchange Under Soft Peg

2005 ◽  
Author(s):  
Nikolai Dokuchaev
Keyword(s):  
Currency Exchange

FORMATION OF METASUBJECT COMPETENCIES IN THE COURSE “INFORMATION TECHNOLOGIES” BY MEANS OF THE BIG DATA PROCESSING LANGUAGE R

2019 ◽  
pp. 12-22
Author(s):  
D. M. Nazarov
Keyword(s):  
Data Processing ◽  
Information Technologies ◽  
Data Sets ◽  
Training Methods ◽  
Economic Knowledge ◽  
R Language ◽  
Currency Exchange ◽  
Business Informatics ◽  
Exchange Data ◽  
Processing Language

The article describes the training methods in the course “Information Technologies” for the future bachelors of the directions “Economics”, “Management”, “Finance”, “Business Informatics”, the development of metasubject competencies of the student while his use of tools for data processing by means of the language R. The metasubject essence of the work is to update traditional economic knowledge and skills through various presentation forms of the same data sets. As part of the laboratory work described in the article, future bachelors learn to use the basic tools of the R language and acquire specific skills and abilities in R-Studio using the example of processing currency exchange data. The description of the methods is presented in the form of the traditional Key-by-Key technology, which is widely used in teaching information technologies.

Forming an effective financial and investment environment in the eu countries-experience for Ukraine

2019 ◽  
pp. 4-13
Author(s):  
Ryzhyuk Yevgeny
Keyword(s):  
Banking System ◽  
Executive Branch ◽  
Investment Environment ◽  
Financial Investment ◽  
Eu Countries ◽  
Currency Exchange ◽  
Clearing System ◽  
Financial Infrastructure ◽  
Supranational Level ◽  
The Eu

The subject of the research is a set of institutional institutions and organizational and managerial relations that effectively regulate the financial and investment environment in the EU countries, comparing them with Ukrainian realities.The goal of writing this article is to develop practical and scientific-methodicalrecommendations on how to increase the efficiency of using financial and investment potential based on the experience of EU countries. The methodology of thework-system-structural and comparative studies (to understand the logic of thefunctioning of institutions that form the investment environment and the mechanisms of their interaction); monographic analysis (in studying the problems ofattracting investors); historical and economic analysis (in assessing the state andprospects of the European, as well as the Ukrainian economy). Results of work -it is revealed that modern European regulators are aimed at forming a holisticinvestment and financial infrastructure and investment platform at the supranational level. It was proposed to carry out further liberalization of currency regulation in Ukraine in order to transform it into a convenient and efficient electronicautomated currency exchange system and introduce the integration of the domestic depository system into the international depositary clearing system Clearstream.It was noted that the financial and investment environment in Ukraine is blockedand domestic monopolies are interested in this, thanks to lobbying in the Verkhovna Rada of Ukraine and in the executive branch they have distorted financial,investment and currency legislation for their interests and needs. Conclusions-thepresence of a holistic investment and financial infrastructure in the EU countriesis due to the gradual convergence and unification of legislation at the nationallevel to the supranational level. In addition, it is reasonably high investment positions of Ireland in the world and it was proposed to use this experience to createa favorable financial and investment environment in Ukraine. Note that the formation of the financial and investment environment in Ukraine according to European standards is hampered by: oligarchic monopolies, which parasitizes mainly onnatural monopolies; government corruption; confusing and incomprehensible legislation for investors; high tax rates and tax administration system; instability ofthe banking system, the risks of hryvnia devaluation; the insecurity of landagrarian relations; as well as armed conflict in the east of Ukraine.

Cross-space Consumption among Undocumented Chinese Immigrants in the United States

2016 ◽  
Vol 2 (2) ◽  
pp. 158-182 ◽  
Author(s):  
Min Zhou ◽  
Xiangyi Li
Keyword(s):  
New York ◽  
Social Status ◽  
Chinese Immigrants ◽  
The United States ◽  
Ethnographic Study ◽  
Social Value ◽  
Left Behind ◽  
Currency Exchange ◽  
The Social ◽  
International Migrants

We consider cross-space consumption as a form of transnational practice among international migrants. In this paper, we develop the idea of the social value of consumption and use it to explain this particular form of transnationalism. We consider the act of consumption to have not only functional value that satisfies material needs but also a set of nonfunctional values, social value included, that confer symbolic meanings and social status. We argue that cross-space consumption enables international migrants to take advantage of differences in economic development, currency exchange rates, and social structures between countries of destination and origin to maximize their expression of social status and to perform or regain social status. Drawing on a multisited ethnographic study of consumption patterns in migrant hometowns in Fuzhou, China, and in-depth interviews with undocumented Chinese immigrants in New York and their left-behind family members, we find that, despite the vulnerabilities and precarious circumstances associated with the lack of citizenship rights in the host society, undocumented immigrants manage to realize the social value of consumption across national borders and do so through conspicuous consumption, reciprocal consumption, and vicarious consumption in their hometowns even without being physically present there. We conclude that, while cross-space consumption benefits individual migrants, left-behind families, and their hometowns, it serves to revive tradition in ways that fuel extravagant rituals, drive up costs of living, reinforce existing social inequality, and create pressure for continual emigration.

scholarly journals Business Days Time Series Weekly Trend and Seasonality

2021 ◽  
Vol 5 (1) ◽  
pp. 26
Author(s):  
Karlis Gutans
Keyword(s):  
Time Series ◽  
Global Warming ◽  
Exchange Rates ◽  
Temperature Data ◽  
Adjustment Method ◽  
Currency Exchange ◽  
Currency Exchange Rates ◽  
Time Series Decomposition ◽  
The World

The world changes at incredible speed. Global warming and enormous money printing are two examples, which do not affect every one of us equally. “Where and when to spend the vacation?”; “In what currency to store the money?” are just a few questions that might get asked more frequently. Knowledge gained from freely available temperature data and currency exchange rates can provide better advice. Classical time series decomposition discovers trend and seasonality patterns in data. I propose to visualize trend and seasonality data in one chart. Furthermore, I developed a calendar adjustment method to obtain weekly trend and seasonality data and display them in the chart.

Beyond Deep Learning: An Econometric Example

2020 ◽  
Vol 28 (Supp01) ◽  
pp. 31-38 ◽  
Author(s):  
Ruofan Liao ◽  
Paravee Maneejuk ◽  
Songsak Sriboonchitta
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Prediction Models ◽  
Original Data ◽  
Parametric Model ◽  
Parametric Models ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
Linear And Nonlinear

In the past, in many areas, the best prediction models were linear and nonlinear parametric models. In the last decade, in many application areas, deep learning has shown to lead to more accurate predictions than the parametric models. Deep learning-based predictions are reasonably accurate, but not perfect. How can we achieve better accuracy? To achieve this objective, we propose to combine neural networks with parametric model: namely, to train neural networks not on the original data, but on the differences between the actual data and the predictions of the parametric model. On the example of predicting currency exchange rate, we show that this idea indeed leads to more accurate predictions.

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