Accurate cryptocurrency price forecasting is crucial due to the significant financial implications of prediction errors. The volatile and non-linear nature of cryptocurrencies challenges traditional statistical methods, revealing a gap in effective predictive modelling. This study addresses this gap by examining the impact of activation functions on neural network models during critical economic periods, specifically aiming to determine how optimising activation functions enhances accuracy in neural network models, including RNN, GRU, LSTM, and hybrid architectures. Using data from January 2016 to June 2022—encompassing stable periods, the COVID-19 pandemic, and the onset of the 2022 Ukraine conflict—we analysed price trends under various market conditions. Our methodology involved testing three activation functions (ReLU, sigmoid, and Tanh) across these models. Both univariate and multivariate analyses were conducted, with the latter incorporating additional metrics such as opening, highest, and lowest prices. The results indicate that optimising activation functions enhances prediction accuracy. Among the models, GRU demonstrated the highest accuracy, whereas RNN was the least efficient. Multivariate models outperformed univariate ones, highlighting the benefits of incorporating comprehensive data. Notably, the Tanh activation function led to the greatest improvements, particularly in underperforming models such as RNN. These findings underscore the critical role of activation function selection in enhancing the predictive power of neural networks for cryptocurrency markets. Optimising activation functions can lead to more reliable forecasts, facilitating better trading decisions and risk management. This study highlights activation functions as key parameters in neural network modelling, encouraging further exploration. Future research could investigate different economic periods and cryptocurrency behaviours to assess model robustness. Additionally, examining a broader range of cryptocurrencies may reveal whether the benefits of activation function optimisation are consistent across various assets. Incorporating external factors such as macroeconomic indicators or social media sentiment could further enhance models and improve forecasting accuracy.

After 2010, the consumer price index fell to a low level in the EU. In the euro area, it remained low between 2010 and 2020. The European Central Bank has even had to take action against the emergence of deflation. The situation changed significantly in 2021. Inflation jumped to levels not seen for 40 years in the EU. Our study aims to use artificial intelligence to forecast inflation. We also use artificial intelligence to forecast stock index changes. Based on the forecasts, we propose portfolio reallocation decisions to protect against inflation. The forecasting literature does not address the importance of structural breaks in the time series, which, among other things, can affect both the pattern recognition and prediction capabilities of various machine learning models. The novelty of our study is that we used the Zivot–Andrews unit root test to determine the breakpoints and partitioned the time series into training and testing datasets along these points. We then examined which database partition gives the most accurate prediction. This information can be used to re-balance the portfolio. Two different AI-based prediction algorithms were used (GRU and LSTM), and a hybrid model (LSTM–GRU) was also included to investigate the predictability of inflation. Our results suggest that the average error of the inflation forecast is a quarter of that of the stock market index forecast. Inflation developments have a fundamental impact on equity and government bond returns. If we obtain a reliable estimate of the inflation forecast, we have time to rebalance the portfolio until the inflation shock is incorporated into government bond returns. Our results not only support investment decisions at the national economy level but are also useful in the process of rebalancing international portfolios.

The paper seeks to answer the question of how price forecasting can contribute to which techniques gives the most accurate results in the futures commodity market. A total of two families of models (decision trees, artificial intelligence) were used to produce estimates for 2018 and 2022 for 21- and 125-day periods. The main findings of the study are that in a calm economic environment, the estimation accuracy is higher (1.5% vs. 4%), and that the AI-based estimation methods provide the most accurate estimates for both time horizons. These models provide the most accurate forecasts over short and medium time periods. Incorporating these forecasts into the ERM can significantly help to hedge purchase prices. Artificial intelligence-based models are becoming increasingly widely available, and can achieve significantly better accuracy than other approximations.

Since 2013, the central bank has been responsible for supervision in Hungary. In addition to the regulatory change, a law was published in the same year that started the process of abolishing the savings co-operative system. This paper investigates the impact of these two significant changes on the profitability of the Hungarian banking sector between 2003 and 2019 using dynamic panel model estimates. The supervisory change has reduced the profitability of credit institutions and tighter supervision has been implemented. The transformation of the savings co-operative system was in fact an integration that led to the disappearance of savings co-operatives by 2019. Competition in the market has been weakened, which has increased the profitability of the remaining financial institutions. The results were robust in terms of the multiple specifications and profitability ratio.

This systematic literature review explores the application of artificial intelligence (AI) and machine learning (ML) in financial market forecasting, with a focus on four asset classes: equities, cryptocurrencies, commodities, and foreign exchange markets. Guided by the PRISMA methodology, the study identifies the most widely used predictive models, particularly LSTM, GRU, XGBoost, and hybrid deep learning architectures, as well as key evaluation metrics, such as RMSE and MAPE. The findings confirm that AI-based approaches, especially neural networks, outperform traditional statistical methods in capturing non-linear and high-dimensional dynamics. However, the analysis also reveals several critical research gaps. Most notably, current models are rarely embedded into real or simulated trading strategies, limiting their practical applicability. Furthermore, the sensitivity of widely used metrics like MAPE to volatility remains underexplored, particularly in highly unstable environments such as crypto markets. Temporal robustness is also a concern, as many studies fail to validate their models across different market regimes. While data covering one to ten years is most common, few studies assess performance stability over time. By highlighting these limitations, this review not only synthesizes the current state of the art but also outlines essential directions for future research. Specifically, it calls for greater emphasis on model interpretability, strategy-level evaluation, and volatility-aware validation frameworks, thereby contributing to the advancement of AI’s real-world utility in financial forecasting.