Cryptocurrencies are a new field of investment opportunities that has experienced a significant growth in the last decade. The crypto market was capitalized at more than USD 3000 bn, having grown from USD 10 m over the period 2011–2021. Generating high returns, investments in cryptocurrencies have also shown high levels of price volatility. By comparing the performance of cryptocurrencies (measured by the crypto index) and standard equities (included in the S&P 500 index), we found that the former has outperformed the latter 14 times over the last two years. In the present paper, we analyzed the 2012–2022 global crypto market developments and main constituents. With a focus on the top 30 cryptocurrencies and their prices, as of 9 April 2022, covering data of the two major market stress events—outbreaks of the COVID-19 pandemic (February 2020) and the Russian invasion of Ukraine (February 2022). We applied the dynamic time warping method including barycentre averaging and k-Shape clustering of time series. The use of the dynamic time warping has been essential for the preparation of data for subsequent clustering and forecasting. In addition, we compared performance of cryptocurrencies and equities. Cryptocurrency time series are rather short, sometimes involving high levels of volatility and including multiple data gaps, whereas equity time series are much longer and well-established. Identifying similarities between them allows analysts to predict crypto prices by considering the evolution of similar equity instruments and their responses to historical events and stress periods. Moreover, we tested various forecasting methods on the 30 cryptocurrencies to compare traditional econometric methods with machine learning approaches.

Purpose: The purpose of the article is to compare a degree of beta-convergence between V4 countries and EU28 at national and NUTS 2 level. Methodology/Approach: We will make this comparison separately for each indicator (gross domestic product at current market prices, unemployment rate and disposable income of households.). To evaluate beta-convergence differences at national and regional level, we will compare the data for the V4 countries and for Germany and Austria. To show convergence, we approximated the GDP growth trend in individual countries, using a trend line for three different time periods. Findings: Our results point to persistent disparities between regions. They have confirmed that the cohesion policy in the regions of the V4 countries should promote innovations and investments into less developed and predominantly agricultural regions, complete the necessary backbone infrastructure and develop a high-quality regional education. At regional level, the implementation of high value-added programs may be hampered by institutional factors and a lack of capacity to make the necessary infrastructure or human capital investments. Our analysis showed that differences in regional performance are also accompanied by significant differences in investigated indicators. Research Limitation/Implication: Limitations of the paper are at first missing data for Poland to the year 2013. Second limit of the article are data from regions, because data are related to the palce of residence of its branch. Originality/Value of paper: Paper is full original,also a data analysis does not copy any other articles neither article in a journal nor paper on conference.

The Cox proportional hazard model may predict whether an individual belonging to a given group would likely register an event of interest at a given time. However, the Cox model is limited by relatively strict statistical assumptions. In this study, we propose decomposing the time-to-event variable into “time” and “event” components and using the latter as a target variable for various machine-learning classification algorithms, which are almost assumption-free, unlike the Cox model. While the time component is continuous and is used as one of the covariates, i.e., input variables for various classification algorithms such as logistic regression, naïve Bayes classifiers, decision trees, random forests, and artificial neural networks, the event component is binary and thus may be modeled using these classification algorithms. Moreover, we apply the proposed method to predict a decrease or non-decrease of IgG and IgM blood antibodies against COVID-19 (SARS-CoV-2), respectively, below a laboratory cut-off, for a given individual at a given time point. Using train-test splitting of the COVID-19 dataset ($n=663$ individuals), models for the mentioned algorithms, including the Cox proportional hazard model, are learned and built on the train subsets while tested on the test ones. To increase robustness of the model performance evaluation, models’ predictive accuracies are estimated using 10-fold cross-validation on the split dataset. Even though the time-to-event variable decomposition might ignore the effect of individual data censoring, many algorithms show similar or even higher predictive accuracy compared to the traditional Cox proportional hazard model. In COVID-19 IgG decrease prediction, multivariate logistic regression (of accuracy $0.811$), support vector machines (of accuracy $0.845$), random forests (of accuracy $0.836$), artificial neural networks (of accuracy $0.806$) outperform the Cox proportional hazard model (of accuracy $0.796$), while in COVID-19 IgM antibody decrease prediction, neither Cox regression nor other algorithms perform well (best accuracy is $0.627$ for Cox regression). An accurate prediction of mainly COVID-19 IgG antibody decrease can help the healthcare system manage, with no need for extensive blood testing, to identify individuals, for instance, who could postpone boosting vaccination if new COVID-19 variant incomes or should be flagged as high risk due to low COVID-19 antibodies.