The COVID-19 pandemic seems to be the most important phenomenon observed from March 2020 in virtually all countries of the world. The necessity to prevent the spread of COVID-19 and keep health care systems efficient resulted in the forced, drastic limitation of economic activity. Many service sectors were hit particularly hard with this but industry and agriculture were also affected. In particular, the pandemic substantially influenced financial markets and we can observe that some markets or instruments vary in stability since they have been affected in the different degree. In the paper, we present the problem of stability of stock markets during the COVID-19 pandemic. Due to the low number of works related to CEE countries during the pandemic, we analyze the Warsaw Stock Exchange, which is one of the most important markets in the CEE. Our main goal was to find how various industries represented by stock market indices have reacted to the COVID-19 shock and consequently which sectors turned out to keep stability and remained resistant to the pandemic. In our investigation, we use two clustering methods: the K-means and the Ward techniques with the criterion of maximizing the silhouette coefficient and six indicators describing stability in terms of profitability, volume, overbought/oversold conditions and volatility. The results of the research present that during the pandemic it was possible to identify 5 clusters of sector indices in the short term and 4 in the medium term. We found that the composition of the clusters is quite stable over time and that none of the obtained clusters can be univocally considered the most or the least stable taking into account all the analyzed indicators. However, we showed that the obtained clusters have different stability origins, i.e. they vary from each other in terms of the investigated indicators of stability.

The crude oil market is one of the most significant sectors in the global economy. Fluctuations in oil prices impact the financial performance of national economies. Crude oil prices are also the basis of many popular financial derivatives on the financial market. Binary-temporal representation state models enable the precise modelling and development of financially efficient decision-support systems in the crude oil market. Existing models are primarily based on the main technical analysis methods: trend and moving average analysis. In this paper, with the aim of enhancing forecasting efficiency, we introduce the concept of determining the widely used RSI indicator for binary-temporal representation and propose a new state model based on its readings. We also present empirical research on the proposed model applied to the oil market, using historical data from the past six years. The results confirm the validity of the approach adopted.

This study aims to analyze the development of global stock exchanges by integrating clustering, classification, and Shapley Values to identify growth patterns and understand the differences in market characteristics and dynamics. The research applies the K-means algorithm for clustering, which enables the segmentation of exchanges based on their similarities. This is followed by using the random forest algorithm to classify these clusters and evaluate the importance of various features. Shapley Values are employed to interpret the contribution of individual variables to the model’s predictions, considering all possible combinations of features. The empirical analysis is based on data from 82 stock exchanges worldwide, sourced from organizations such as the World Federation of Exchanges and the International Monetary Fund. Key variables used include market capitalization, trading value, the number of listed companies, and share turnover velocity. The results highlight the significant heterogeneity among exchanges, with major markets like those in China and the United States forming distinct clusters due to their size, capitalization, and high trading activity. This distinction underscores their dominant position in the global financial landscape. Moreover, exchanges that have emerged from mergers, such as Euronext and NASDAQ Nordic, demonstrate superior characteristics compared to their peers, indicating that consolidation can be an effective strategy for competing with larger markets and enhancing global competitiveness. The study’s findings show that integrating clustering, classification, and Shapley Values is a robust approach for uncovering complex structures within financial markets. This approach provides deeper insights for market participants and policymakers into the growth patterns and strategic positioning of stock exchanges, offering valuable implications for future market development and competition strategies.

We investigate a multi-class machine learning (ML) framework to generate daily Bitcoin trading signals—Buy, Sell, or Hold. Three algorithms—XGBoost, LightGBM, and Random Forest—are compared with a naive buy-and-hold strategy. Using BTC/USD daily data (2015–2024), we apply a range of technical indicators across trend, momentum, volatility, and volume, later pruned by correlation analysis. A ±1% threshold defines the "Hold" zone to avoid minor fluctuations. Empirical tests show that LightGBM outperforms other models and even surpasses buy-and-hold in final portfolio value. Our findings support the design of tri-class ML strategies tailored for high-volatility markets like cryptocurrency.

Effective forecasting of economic and financial processes is of fundamental significance for participants and analysts of economic life. At the same time, however, many areas can be identified where forecasting tools that provide results with satisfactory accuracy are still missing. For this reason, continuous work has been underway concerning new methodological concepts and the improvement of existing forecasting tools. In recent years, the potential of machine learning algorithms has been noticed, as evidenced by their growing popularity both in the scientific community and among business practitioners. The monograph is devoted to the use of innovative machine learning methods for forecasting financial and economic time series. It presents selected prediction algorithms, emphasising the possibilities of their application. The results of the research presented in this monograph confirm the advantages of machine learning, in particular the fact that the described algorithms can be an effective tool for forecasting economic and financial time series.