<abstract>
<title style='display:none'>Abstract</title>
Statistical learning models have profoundly changed the rules of trading on the stock exchange. Quantitative analysts try to utilise them predict potential profits and risks in a better manner. However, the available studies are mostly focused on testing the increasingly complex machine learning models on a selected sample of stocks, indexes etc. without a thorough understanding and consideration of their economic environment. Therefore, the goal of the article is to create an effective forecasting machine learning model of daily stock returns for a preselected company characterised by a wide portfolio of strategic branches influencing its valuation. We use Nvidia Corporation stock covering the period from 07/2012 to 12/2018 and apply various econometric and machine learning models, considering a diverse group of exogenous features, to analyse the research problem. The results suggest that it is possible to develop predictive machine learning models of Nvidia stock returns (based on many independent environmental variables) which outperform both simple naïve and econometric models. Our contribution to literature is twofold. First, we provide an added value to the strand of literature on the choice of model class to the stock returns prediction problem. Second, our study contributes to the thread of selecting exogenous variables and the need for their stationarity in the case of time series models.
</abstract>

Nvidia's Stock Returns Prediction Using Machine Learning Techniques for Time Series Forecasting Problem

<abstract>
<title style='display:none'>Abstract</title>
This paper investigates the relationship between sustainability disclosure measured through the combined and individual environmental, social, and governance (ESG) scores and the value of European companies from energy and health care industries. The reasons for this research are the increased awareness of sustainability disclosure, the inclusion of nonfinancial information in corporate reporting and possible added value to the company, the ongoing COVID-19 pandemic, and its impact on the global economy. The sample consists of 305 observations for 61 European companies from different categories in the energy industry and 225 observations for 45 European companies in the health care industry. To apply the research model to the samples of companies set as panel data, two linear regressions were designed, each using a different dependent variable to increase the reliability of the outputs. The results indicate a negative and significant correlation between the environmental factor and firms’ value for the energy sector and no influence of the sustainability disclosure on the value of companies in the health care industry. The research contributes to the literature by addressing the impact of sustainability disclosure on the value of the European companies from sensitive industries, using both the combined and individual ESG scores to measure sustainability disclosure. Furthermore, the study encourages other academic research on this subject.
</abstract>

Sustainability disclosure and its impact on firm's value for Energy and Healthcare industry

<abstract><title style='display:none'>Abstract</title>In this paper, cryptocurrencies are analysed as investment instruments. The study aims to verify whether they can be classified as an asset class and what kind of benefits they may bring to the investor's portfolio. We used 6 indices as proxies for the major asset classes, including the cryptocurrency index CRIX, for all cryptographic assets.Cryptocurrencies relatively fully satisfied 7 asset class requirements, namely stable aggregation, investability, internal homogeneity, external heterogeneity, expected utility, selection skill and cost-effective access. It was found that crypto assets have diversification properties. Portfolio optimisation with the Modern Portfolio Theory showed an increase in the Sharpe ratio of tangency portfolios with the inclusion of CRIX. However, the Post-Modern Portfolio Theory identified significant deterioration of the downside risk and the Sortino ratio.</abstract>

Cryptocurrencies as an asset class in portfolio optimisation

<abstract>
<title style='display:none'>Abstract</title>
The introduction of a formulaically apportioned common consolidated corporate tax base (CCCTB) could represent a milestone in international taxation. No agreement has yet been reached, however. In contrast, Germany already has a long-standing system that apportions corporate taxes by splitting trade tax and corporate income tax. This conceptual study, presented at the European Accounting Association (EAA) Congress in Bergen in 2022, will examine whether the German method of splitting could lead to some lessons for an appropriate design for an international profit distribution formula.
Methodologically, we use a two-step approach: First, we compare the designs, and then we juxtapose both on a factual level. Next, we ask what the objectives these mechanisms have; do they even coincide? If the goals are not comparable, one cannot indisputably serve as a model for the other. We determine that, even though there are partial deviations, a closer look reveals significant overlaps; however, the German implementation is far from consistent and prioritises practicability. This leads us to our main result: The German system makes a clear value decision towards practicability, although there is a different set of aims. For the implementation of formulaic EU profit sharing, the lesson to be learned is that practicability should play a central role in the design of the formula. This lesson is important and helpful to accompany and support the implementation process in the EU.
</abstract>

Splitting of Corporate Taxes in Germany and Formulaic Distribution of a CCCTB – Critical Comparison

<abstract>
<title style='display:none'>Abstract</title>
The paper analyses the economic implications of the accession of New Member States (NMS) to the European Union (EU) in 2004 and 2007. The estimation effects of integration with the EU were carried out as a comparative case study using the synthetic control method (SCM) proposed by Abadie and Gardeazabal. Compared to previous studies analysing the effects of accession to the EU (Campos, Coricelli and Moretti), we check for the importance of the quality of economic institutions for the matching process of the analysed economies with their comparators. The results of the econometric analysis show a positive impact on the country performance 6 years and 12 years after accession to the EU. The gains from accession are large but not universal. For 5 of the 10 analysed countries the difference in levels of per capita gross domestic product (GDP) against the counterfactual is at least 30%.
</abstract>

Economic impact of the EU Eastern enlargement on New Member States revisited: The role of economic institutions

<abstract><title style='display:none'>Abstract</title>This paper analyses the influence of financial development on income inequality. Throughout this work, one may find the overview of theoretical and empirical literature as well as the empirical model using fixed panel data method. This research paper tries to disentangle the opposing views on the relationship between finance and income distribution, by evaluating the impact of the different dimensions of financial development on the level of income inequality. The important added value of this research is the usage of quintiles of income distribution as a dependent variable that may help to recognise the effect of financial development on the poorest and richest. Another novelty of the paper is the consideration of the effects of financial variables on Gini coefficient in the long and short run. The main results of the analysis using dataset from 2003 to 2014 indicate that financial access decreases income inequality.</abstract>

Financial development and income inequality

<abstract>
<title style='display:none'>Abstract</title>
This study aims to measure the impact of environmental regulation on the production of renewable energies in OECD countries from 1990 to 2021. Environmental policies stringency, environmental taxes, and CO2 emissions are variables indicating environmental regulation, which affect renewable energies production. The study relied on unconditional quantitative regression methods. The study found that strict environmental policies do not necessarily enhance renewable energy production in countries with high or low production. Moreover, environmental tax revenues have varying impacts on renewable energy production based on renewable energy production in each country. For countries with below-average levels of renewable energy (Q25), environmental taxes positively affect renewable energy production; however, in countries with high production levels (Q90), environmental taxes show a negative effect. Furthermore, CO2 emissions negatively affect the total production of renewable energy in all quantiles except Q50, whereas R&amp;D spending positively affects renewable energies in all quantiles except Q75. The estimates also showed a significant negative effect of patents on the renewable energy production in quantile Q10. The results underscore the importance of flexibility and adaptability in environmental policies and taxes. Finally, the study indicates that policies must be dynamic and respond to the specificity of each stage of renewable energy development in the studied countries.
</abstract>

Environmental Regulation and Renewable Energies: Evidence from Generalized Panel Unconditional Quantile Regression

<abstract>
<title style='display:none'>Abstract</title>
This paper aims to analyse consumers’ perceptions and attitudes towards natural cosmetic products from three Central European countries. In this study, we employed quantitative surveys on samples of 230 Czech, 175 Slovak, and 325 Polish respondents. Based on the obtained data, we investigate whether and how the respondents’ country impacts how they formulate their statements regarding the features of natural cosmetics. For each country separately, we study the dependencies of respondents’ opinions and statements on their sociodemographic profile. The results of the research show statistically significant differences in the respondents’ answers to all substantive questions depending on their country of origin. Four findings are worth highlighting: first, respondents from all three countries prefer natural cosmetics over conventional ones mainly for health and environmental reasons. Second, the frequency of buying natural cosmetics is influenced by some sociodemographic factors, although this varies in all three countries. Third, respondents who buy natural cosmetics more often are also more likely to indicate them as products of higher quality than conventional cosmetics. Lastly, the declared composition of the product is the prevailing decision criterion for consumers when selecting natural cosmetic products.
</abstract>

Perception of Natural Cosmetics Among Central European Consumers

<abstract>
<title style='display:none'>Abstract</title>
The present study aims to explain and predict the monetary amount awarded by courts as compensation for harm suffered. A set of machine-learning algorithms was applied to a sample of decisions handed down by the Polish common courts. The methodology involved two steps: identification of words and phrases whose counts or frequencies affect the amounts adjudicated with LASSO regression and expert assessment, then applying OLS, again LASSO, random forests and XGBoost algorithms, as well as a BERT approach to make predictions. Finally, an in-depth analysis was undertaken on the influence of individual words and phrases on the amount awarded. The results demonstrate that the size of awards is most strongly influenced by the type of injury suffered, the specifics of treatment, and the family relationship between the harmed party and the claimant. At the same time, higher values are awarded when compensation for material damage and compensation for harm suffered are claimed together or when the claim is extended after it was filed.
</abstract>

Predicting the Amount of Compensation for Harm Awarded by Courts Using Machine-Learning Algorithms

<abstract>
<title style='display:none'>Abstract</title>
In Nigeria, there has been a decline in oil revenue. This has impacted negatively on infrastructural development. This paper seeks to examine the effect of non-oil revenue as an alternative source of revenue for infrastructural development. The research design of the study was the ex post facto research design. The source of data was the secondary source and a time series of data from 1981 to 2021 was used in carrying out the research. The Autoregressive Distributed Lagged (ARDL) bounds test was used to determine the long-run and short-run relationship between the dependent and independent variables. It was observed that the variables are co-integrated, and as such, a long-run and short-run relationship exists among the explanatory variables. Furthermore, the ARDL short-run estimation result shows that the non-oil tax variables (proxied by VAT, CUSTD, and CIT) have a positive and significant effect on infrastructural development (proxied by total electricity production measured in Gigawatt hours (GWh) in Nigeria. In tandem, the ARDL long-run estimation results reveal that value-added tax, customs duties, and company income tax have a positive and significant impact on infrastructural development in Nigeria. Hence, an increase in the non-oil tax revenue base will boost infrastructural development in Nigeria in the long run. This finding is in tandem with the ARDL short-run estimation result. Therefore, it is inferred that Nigeria can experience infrastructural development when genuine commitment is made to explore an increase in non-oil revenue generation instead of being over-dependent on oil revenue.
</abstract>

Non-Oil Tax Revenue and Infrastructural Development in Nigeria

<abstract>
<title style='display:none'>Abstract</title>
Observable climate change and an increase in the frequency of extreme climate events undoubtedly pose challenges for society and business operations. The changes being implemented in sustainability efforts are a response to these challenges. However, the question is how these measures affect companies‘ financial performance.
The study aims to verify the relationship between the reporting of sustainability scores related to three aspects: environmental, social, and corporate governance (ESG). It focuses on the financial performance of companies in the Central and Eastern Europe (CEE) region in 2017–2021. The study will use panel regression and cross-sectional analysis. The results indicate a positive relationship between the disclosure of ESG activities and the financial performance of companies as measured by ROA. It was also observed that for companies operating in the financial sector, the correlation is greater, compared to companies operating in other sectors. This study contributes to the ongoing debate on the environment, society, and governance in the economy.
</abstract>

Relationship between ESG and Financial Performance of Companies in the Central and Eastern European Region

<abstract>
<title style='display:none'>Abstract</title>
The aim of the article is to analyse the structure of consumer behaviour models and their applications to financial behaviour. The paper is theoretical in nature, examining generational breakdowns and selected trends in consumer behaviour. An analysis of the ‚rules‘ significant for types of consumer behaviour is conducted. Despite recently intensified theoretical and empirical analysis of consumer behaviour, a coherent research approach that integrates the issue of consumer behaviour with the specificities of the financial-services market has not yet been developed. Models remain frail, their functionality is still insufficient, and their applicability is constantly being altered by influences on consumer behaviour, including macro and microeconomic factors and the influence of scientific fields such as economics, sociology, psychology, management and anthropology. This article attempts to create a model that takes into account the general factors adopted in models constructed so far, along with the assumption that economic, cultural, social, personal, experiences, and other factors play a role in shaping and stimulating at least five variables: type of consumer from a particular generation (Cy); type of financial products and services (Xy); motivation (My); capacity (Capy); and opportunities (Oy).
</abstract>

The Frailty of Models, the New Era, or a Rotten World of Consumers’ Financial Behaviour?

<abstract><title style='display:none'>Abstract</title>The aim of this analysis is to examine the characteristics of the Airbnb network, to verify the share of Airbnb offers that belong to the sharing economy and to identify the differences between the spatial distribution of the Airbnb network and the traditional hotel industry. The article is based on a unique dataset of web-scraped data on Airbnb listings in Warsaw (Poland), combined with district-level official statistics on the hotel industry. The analysis shows that only approximately 11% of offers belong to the sharing economy (“individuals granting each other temporary access to their under-utilised assets”), while at least one third of offers are provided by professional firms. The Airbnb network shows a strong centre-periphery pattern, with 75% of offers located within a range of 4.3 kilometres from the centre. The spatial concentration of Airbnb offers is strongly driven by their distance from metro lines, while it is weakly related to the amount of living space. On the district-level, the spatial distribution of Airbnb listings is correlated with that of the hotel industry, although Airbnb contributes to a more even spread of tourism in the city. The major contribution of this analysis is its presentation of the size and characteristics of the platform, which is essential for data-driven policy making.</abstract>

Airbnb and the Hotel Industry in Warsaw: An Example of the Sharing Economy?

<abstract><title style='display:none'>Abstract</title>The pattern of trade of the Central and Eastern European countries has been changing since the beginning of the economic transition in the early 1990s. By the end of the century this process was additionally strengthened by their integration with the European Union and overlapped with the development of global value chains (GVC) spanning across Europe with which the new member states (NMS) have become increasingly integrated.In this paper, we shed light on these changes by analysing the position of the NMS within the global value chains. We employ the upstreamness measure proposed by <xref ref-type="bibr" rid="j_ceej-2017-0003_ref_003_w2aab3b7b1b1b6b1ab1b6b3Aa">Antràs et al. (2012)</xref> and use the World Input–Output Database. Although we observe a global increasing trend in the upstreamness of all countries, we find that the NMS have in many cases gone against this trend while converging in their production structure within their group and with the EU-15. This convergence is mostly observed in Czech Republic, Hungary, Poland and Slovakia where the level of upstreamness in the most important exporting sectors was close to that of Germany by the end of the analysed period 1995−2011.</abstract>

Up or Down the Value Chain? A Comparative Analysis of the GVC Position of the Economies of the New EU Member States

<abstract><title style='display:none'>Abstract</title>This study investigates the profitability of an algorithmic trading strategy based on training SVM model to identify cryptocurrencies with high or low predicted returns. A tail set is defined to be a group of coins whose volatility-adjusted returns are in the highest or the lowest quintile. Each cryptocurrency is represented by a set of six technical features. SVM is trained on historical tail sets and tested on the current data. The classifier is chosen to be a nonlinear support vector machine. The portfolio is formed by ranking coins using the SVM output. The highest ranked coins are used for long positions to be included in the portfolio for one reallocation period. The following metrics were used to estimate the portfolio profitability: %ARC (the annualized rate of change), %ASD (the annualized standard deviation of daily returns), MDD (the maximum drawdown coefficient), IR1, IR2 (the information ratio coefficients). The performance of the SVM portfolio is compared to the performance of the four benchmark strategies based on the values of the information ratio coefficient IR1, which quantifies the risk-weighted gain. The question of how sensitive the portfolio performance is to the parameters set in the SVM model is also addressed in this study.</abstract>

Robustness of Support Vector Machines in Algorithmic Trading on Cryptocurrency Market

<abstract><title style='display:none'>Abstract</title>During the last two decades, Poland has become a large recipient of inward foreign direct investment (FDI). This article uses standard panel data techniques to study empirically the determinants of inward FDI in Poland during the period 1996–2015 made by multinational enterprises coming from the old European Union (EU)-15 member states. The estimated specification is derived from the knowledge-capital (KC) model and includes two types of capital: human and physical. The assembled empirical evidence points to the horizontal motive as the primary reason for undertaking FDI in Poland by multinational firms based in the old EU-15 member states. Moreover, the KC model does not seem to explain better the pattern of inward FDI in Poland compared to the standard ad hoc gravity model of international capital mobility.</abstract>

Determinants of foreign direct investment from EU-15 Countries in Poland

<abstract><title style='display:none'>Abstract</title>Tanzania, like most other developing countries, faces numerous economic challenges in striving to achieve sustainable economic growth and development through taxation. In the literature, the debate on how effective taxes are as a tool for promoting economic growth and economic development remains inconclusive, as various research have reported mixed effects of tax on economic growth. This article investigates the effect of taxation on economic growth in Tanzania using the recently developed technique of autoregressive distributed lag model (ARDL) bounds testing procedure for the period from 1996 to 2019. Various preliminary tests were conducted including stationary tests as well as the pair-wise Granger causality test. According to the results obtained, domestic goods and services (TGS) taxes are positively related to GDP growth and are statistically significant at 1% level. Income taxes, on the other hand, were found to be negatively related to GDP growth and to be statistically significant at 5% level. The pair-wise Granger causality results indicated that there is bidirectional Granger causality between TGS and GDP growth at 1 % significance level. The government should aim at growing, nurturing and sustaining tax base to positively drive economic growth even further.</abstract>

Tax revenue and economic growth in developing country: an autoregressive distribution lags approach

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Central European Economic Journal

 Central European Economic Journal publishes original theoretical and empirical research papers in the field of economics as well as at the intersection of economics and sociology,...

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 Central European Economic Journal publishes original theoretical and empirical research papers in the field of economics as well as at the...