scholarly journals OTC market during COVID-19 pandemic and impact on its further development

2021 ◽  
Vol 129 ◽  
pp. 01019
Author(s):  
Veronika Machová ◽  
Jiří Kučera ◽  
Michaela Juhaňáková
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Normal Level ◽  
Return On Investment ◽  
Value Added ◽  
The Past ◽  
Index Value ◽  
Further Development ◽  
Volume Of Trade

Research background: The onset of the COVID-19 pandemic strongly affected the development of the volume of trade on the OTC market. The volume was below its normal level due to the uncertainty and fear in the society. Purpose of the article: The objective of the paper was to explain the differences between the period before the pandemic, during the pandemic, and to determine the predictions of further development until the year 2025 Methods: Using the method of neural networks, the prediction of the closing exchange rate until 2025 was determined based on the data from the past years, according to which the RM index value will be higher in December this year, with a subsequent slight decrease and fluctuations. Findings & Value added: The results showed that the volume of trades was lower in the past compared to the period just before the pandemic, i.e. the year 2020, when the RM index was clearly highest. Investors should postpone investments until the value of the RM index is stable and trading on the OTC market is advantageous again. However, if they are willing to risk, investments can be made, although the return on investment will be unreasonably long.

scholarly journals The Dependence of the CZK Exchange Rate on the Development of the GDP in the Czech Republic in the Conditions of an Expected Crisis

2021 ◽  
Vol 92 ◽  
pp. 08013
Author(s):  
Veronika Machová ◽  
Tomáš Krulický
Keyword(s):  
Neural Networks ◽  
Exchange Rate ◽  
Czech Republic ◽  
Value Added ◽  
The Czech Republic ◽  
The Past ◽  
Currency Exchange ◽  
Currency Exchange Rate ◽  
The World ◽  
Rate Development

Research background: In the past, some studies proved that the development of a currency exchange rate predicts the development of the whole national economy. The monetary market overtakes the development of the actual economy for a few months. Does this apply also in the case of the Czech koruna, in the era of the global Coronavirus pandemics and in the world affected by the pandemics? Purpose of the article: The main objective is to analyze a dependence of the Czech koruna (CZK) to Euro (EUR) exchange rate development on gross domestic product of the Czech Republic in the conditions of an expected crisis. Methods: The data used of the analysis are represented by the information about the CZK and EUR exchange rate from the beginning of 1999 to the 15th June 2020 and by the quarterly development of the Czech GDP. To measure the dependence and predict the development of the GDP based on the CZK exchange rate development, the method of AI is used, namely the regression analysis using the artificial neural networks. Findings & Value added: The effect of EUR/CZK on GDP can be quantified reaching around 31%. It is assumed that the GDP will fall significantly in 2020 with a certain growth only being possibly expected in 2021 (even more significantly in the second quarter of 2021). Due to the GDP development, the development of the EUR/CZK could then be forecasted as well.

scholarly journals Evaluation of selected capital market

2021 ◽  
Vol 129 ◽  
pp. 03011
Author(s):  
Jakub Horák ◽  
Eva Kalinová ◽  
Andrea Novotná
Keyword(s):  
Capital Market ◽  
Stock Exchange ◽  
Value Added ◽  
Sharp Decline ◽  
Stock Market Index ◽  
Initial Value ◽  
The Past ◽  
Market Index ◽  
Complex Development ◽  
Further Development

Research background: Stock exchange trading is an activity carried out in order to achieve a profit. The oldest and largest market operator in the CR is the Prague Stock Exchange. The complex development of the market in a given period with regard to its development trends is monitored by means of the stock market index. The index of the Prague Stock Exchange is PX index. Purpose of the article: The objective of the contribution is the evaluation of the development of the PX index in the years 2018-2020 and the prediction of its further development. Methods: The data on the PX index were obtained from the official Prague Stock Exchange websites. The data are available for the period of 26 March 2018-31 March 2021. The processed data are analysed using neural networks, specifically the time series analysis. The opening price is used as a variable. Findings & Value added: The research results show that the Czech market index has been relatively stable in the past, its values being around its initial value, 1,000 points. No major fluctuations were recorded, as the PX index included very stable firms. However, with the onset of the COVID-19 pandemic, there was a sharp decline caused by the effect of anti-pandemic measures on the economy. Currently, the Czech market index is expected to grow gradually and stabilize at around 1,000 points.

Perceived Burden of Informal Caregivers of a Chronically Ill Older Family Member

GeroPsych ◽  
2011 ◽  
Vol 24 (3) ◽  
pp. 143-154 ◽  
Author(s):  
Elmar Gräßel ◽  
Raffaela Adabbo
Keyword(s):  
Family Member ◽  
Informal Caregiver ◽  
Chronically Ill ◽  
Stress Model ◽  
The Past ◽  
Perceived Burden ◽  
Hypothetical Construct ◽  
Care Situation ◽  
The Impact ◽  
Further Development

The burden of caregivers has been intensively researched for the past 30 years and has resulted in a multitude of individual findings. This review illustrates the significance of the hypothetical construct of perceived burden for the further development and design of the homecare situation. Following explanations regarding the term informal caregiver, we derive the construct burden from its conceptual association with the transactional stress model of Lazarus and Folkman. Once the extent and characteristics of burden have been set forth, we then present the impact of perceived burden as the care situation. The question of predictors of burden will lead into the last section from which implications can be derived for homecare and relief of caregivers.

Commonwealth of Independent States: The First 20 Years

2011 ◽  
pp. 90-101 ◽  
Author(s):  
N. Shumsky
Keyword(s):  
Commonwealth Of Independent States ◽  
The Past ◽  
Independent States ◽  
Further Development

The article assesses the effectiveness and outcomes of cooperation of the Commonwealth participating states over the past 20 years. It reviews perspectives and directions for further development of the CIS taking into account the conditions and characteristics of integration processes of the post-Soviet states, implementation of the principles of multilevel and multispeed integration of the Commonwealth participating states.

Recognition Pattern, Functional Mechanism and Application of Chitin and Chitosan Oligosaccharides in Sustainable Agriculture

2020 ◽  
Vol 26 (29) ◽  
pp. 3508-3521 ◽  
Author(s):  
Xiaochen Jia ◽  
Mijanur R. Rajib ◽  
Heng Yin
Keyword(s):  
Agricultural Production ◽  
Growth And Yield ◽  
Biotic Stresses ◽  
The Past ◽  
Chitosan Oligosaccharides ◽  
Recognition Pattern ◽  
Functional Mechanism ◽  
Chitin And Chitosan ◽  
And Storage ◽  
Further Development

Background: Application of chitin attracts much attention in the past decades as the second abundant polysaccharides in the world after cellulose. Chitin oligosaccharides (CTOS) and its deacetylated derivative chitosan oligosaccharides (COS) were shown great potentiality in agriculture by enhancing plant resistance to abiotic or biotic stresses, promoting plant growth and yield, improving fruits quality and storage, etc. Those applications have already served huge economic and social benefits for many years. However, the recognition mode and functional mechanism of CTOS and COS on plants have gradually revealed just in recent years. Objective: Recognition pattern and functional mechanism of CTOS and COS in plant together with application status of COS in agricultural production will be well described in this review. By which we wish to promote further development and application of CTOS and COS–related products in the field.

scholarly journals Organic magnetoelectric and optomagnetic couplings: perspectives for organic spin optoelectronics

2021 ◽  
Vol 13 (1) ◽  
Author(s):  
Zhongxuan Wang ◽  
Wei Qin
Keyword(s):  
Ferromagnetic Materials ◽  
The Past ◽  
Potential Applications ◽  
Further Development

AbstractOver the past years, the development of organic ferromagnetic materials has been investigated worldwide for potential applications. Due to the couplings among the charge, orbit, spin, and phonon in organic ferromagnetic materials, magnetoelectric, and optomagnetic couplings have been realized and observed. In this review, progress in organic magnetoelectric and optomagnetic couplings is presented, and the mechanisms behind the phenomena are also briefly summarized. Hopefully, the understanding of magnetoelectric and optomagnetic couplings could provide guidance for the further development of organic spin optoelectronics.

Open Door System and Endogenous Growth in Indian Economy: An Empirical Analysis on the Role of Human Capital and R&D in Explaining Industrial Productivity

2021 ◽  
Vol 46 (1) ◽  
pp. 24-37
Author(s):  
Arjun K. ◽  
Sanjay Kumar ◽  
A. Sankaran ◽  
Mousumi Das
Keyword(s):  
Human Capital ◽  
Exchange Rate ◽  
Real Exchange Rate ◽  
Endogenous Growth ◽  
Value Added ◽  
Capital Formation ◽  
Knowledge Capital ◽  
Test Technique ◽  
Long Run ◽  
The Impact

The present study investigates the impact of human capital, knowledge capital which is a function of human capital, and real exchange rate scenario in explaining long-run industrial total factor productivity (TFP) from 1980 to 2015 on the theoretical basis of the open endogenous growth model. The variables employed in the contemporary study include manufacturing value added (MNVA) as industrial output measure, gross fixed capital formation (GFCF) as a measure of capital and labour input which is measured using employment data. Gross enrolment ratio (GER) is taken as a measure for human capital formation, expenditure on research and development (R&D) as a proxy for knowledge capital, and real exchange rate indicates global economic shocks. The study involves estimating TFP for Industrial Sector during the post-liberalization period by employing Cobb-Douglas production function. The ARDL bounds test technique for cointegration revealed long-run relation among the varying factors studied. The Toda-Yamamoto causality test concluded bi-directional causality running between, R&D expenditure and Industrial TFP which sends a strong signal to the policymakers for a well-framed long-term integrated approach for human & knowledge capital formation which will act as a strong impetus for manufacturing firms to come up in terms of augmenting production and productivity and expanding foreign market horizon. JEL Classification: D24, E2, J24

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.

scholarly journals Laplacian networks: bounding indicator function smoothness for neural networks robustness

2021 ◽  
Vol 10 ◽  
Author(s):  
Carlos Lassance ◽  
Vincent Gripon ◽  
Antonio Ortega
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Supervised Learning ◽  
Indicator Function ◽  
Training Data ◽  
Theoretical Justification ◽  
The Past ◽  
Noisy Examples

For the past few years, deep learning (DL) robustness (i.e. the ability to maintain the same decision when inputs are subject to perturbations) has become a question of paramount importance, in particular in settings where misclassification can have dramatic consequences. To address this question, authors have proposed different approaches, such as adding regularizers or training using noisy examples. In this paper we introduce a regularizer based on the Laplacian of similarity graphs obtained from the representation of training data at each layer of the DL architecture. This regularizer penalizes large changes (across consecutive layers in the architecture) in the distance between examples of different classes, and as such enforces smooth variations of the class boundaries. We provide theoretical justification for this regularizer and demonstrate its effectiveness to improve robustness on classical supervised learning vision datasets for various types of perturbations. We also show it can be combined with existing methods to increase overall robustness.

Sign in / Sign up

Export Citation Format

Share Document