Tibor Bareith
57211468212
Publications - 9
INVESTIGATING THE ROLE OF ACTIVATION FUNCTIONS IN PREDICTING THE PRICE OF CRYPTOCURRENCIES DURING CRITICAL ECONOMIC PERIODS
Publication Name: Virtual Economics
Publication Date: 2024-12-31
Volume: 7
Issue: 4
Page Range: 64-91
Description:
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.
Open Access: Yes
Navigating Inflation Challenges: AI-Based Portfolio Management Insights
Publication Name: Risks
Publication Date: 2024-03-01
Volume: 12
Issue: 3
Page Range: Unknown
Description:
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.
Open Access: Yes
Evaluating the Effectiveness of Modern Forecasting Models in Predicting Commodity Futures Prices in Volatile Economic Times
Publication Name: Risks
Publication Date: 2023-02-01
Volume: 11
Issue: 2
Page Range: Unknown
Description:
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.
Open Access: Yes
The Impact of Changes in Financial Supervision on the Profitability of the Hungarian Banking Sector
Publication Name: Economies
Publication Date: 2022-07-01
Volume: 10
Issue: 7
Page Range: Unknown
Description:
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.
Open Access: Yes
Navigating AI-Driven Financial Forecasting: A Systematic Review of Current Status and Critical Research Gaps
Publication Name: Forecasting
Publication Date: 2025-09-01
Volume: 7
Issue: 3
Page Range: Unknown
Description:
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.
Open Access: Yes
Enhancing Policy Insights: Machine Learning-Based Forecasting of Euro Area Inflation HICP and Subcomponents
Publication Name: Forecasting
Publication Date: 2025-12-01
Volume: 7
Issue: 4
Page Range: Unknown
Description:
Accurate inflation forecasting is of central importance for monetary authorities, governments, and businesses, as it shapes economic decisions and policy responses. While most studies focus on headline inflation, this paper analyses the Harmonised Index of Consumer Prices (HICP) and its 12 subcomponents in the euro area over the period 2000–2023, covering episodes of financial crisis, economic stability, and recent inflationary shocks. We apply a broad set of machine learning and deep learning models, systematically optimized through grid search, and evaluate their performance using the Normalized Mean Absolute Error (NMAE). To complement traditional accuracy measures, we introduce the Forecastability Index (FI) and the Interquartile Range (IQR), which jointly capture both the difficulty and robustness of forecasts. Our results show that RNN and LSTM architectures consistently outperform traditional approaches such as SVR and RFR, particularly in volatile environments. Subcomponents such as Health and Education proved easier to forecast, while Recreation and culture and Restaurants and hotels were among the most challenging. The findings demonstrate that macroeconomic stability enhances forecasting accuracy, whereas crises amplify errors and inter-model dispersion. By highlighting the heterogeneous predictability of inflation subcomponents, this study provides novel insights with strong policy relevance, showing which categories can be forecast with greater confidence and where uncertainty requires more cautious intervention.
Open Access: Yes
Volatility-Sensitive Forecasting: Deep Learning Model Robustness across Calm and Crisis Periods
Publication Name: Virtual Economics
Publication Date: 2026-03-31
Volume: 9
Issue: 1
Page Range: 28-64
Description:
This study investigates how the forecasting performance of deep learning models is affected by changing market conditions, with particular emphasis on periods of differing volatility. While deep learning methods are increasingly applied in financial forecasting, relatively limited evidence exists on how their predictive accuracy varies across distinct market regimes. The aim of this paper is to examine whether model performance systematically deteriorates during more turbulent periods and whether this effect differs across model architectures. The empirical analysis covers multiple asset classes, including equities, commodities, cryptocurrencies, and currency pairs, over three partially overlapping periods (2016–2018, 2018–2020, and 2020–2022), capturing both relatively stable and highly volatile market environments. Forecasting performance is evaluated using several recurrent neural network architectures, and the relationship between market volatility and prediction error is analysed using cross-sectional regression techniques. Additional sensitivity analyses are conducted to assess the robustness of the results across alternative asset samples. The results reveal a consistent positive relationship between market volatility and forecasting error, indicating that predictive performance tends to deteriorate under more turbulent market conditions. However, the magnitude of this effect varies across model architectures. In particular, GRU-based architectures appear less sensitive to volatility, whereas the use of multivariate inputs does not consistently improve forecasting accuracy. These findings suggest that greater model complexity does not necessarily translate into more robust performance under changing market conditions. The study is subject to several limitations, including the relatively small cross-sectional sample and the use of standardised model configurations. Future research could extend the analysis by considering alternative model classes, broader datasets, and more comprehensive robustness frameworks.
Open Access: Yes
Beyond Forecast Accuracy: Evaluating the Error–Profit Paradox in AI-Based Copper Price Prediction
Publication Name: Machine Learning and Knowledge Extraction
Publication Date: 2026-07-01
Volume: 8
Issue: 7
Page Range: Unknown
Description:
Copper is a strategically important commodity whose price dynamics are increasingly affected by structural changes, geopolitical shocks, and the global energy transition. These conditions create substantial challenges for forecasting models and provide a useful setting for evaluating the practical value of machine learning predictions. This study compares statistical and artificial intelligence-based forecasting models for copper price prediction under different market regimes and structural break conditions. Model performance is assessed using a multi-dimensional evaluation framework that combines statistical accuracy (MAPE), dynamic pattern reproduction (Taylor diagrams and time-lagged cross-correlation analysis), and the economic performance of forecast-driven trading strategies. The results reveal a consistent error–profit paradox: models with the highest statistical forecasting accuracy do not necessarily generate the best trading outcomes. In several cases, models with larger prediction errors achieve superior economic performance because they capture directional market dynamics more effectively. The analyses further show that structural breaks substantially alter model rankings and predictive usefulness, highlighting the importance of regime-aware evaluation. These findings suggest that forecast accuracy alone provides an incomplete assessment of model quality in financial and commodity forecasting applications. The study contributes to machine learning evaluation research by proposing an integrated framework that jointly considers predictive accuracy, temporal dynamics, model robustness, and economic utility, thereby offering a more comprehensive approach to assessing forecasting systems in real-world decision-making environments.
Open Access: Yes
DOI: 10.3390/make8070209
Forecasting Aluminum Futures Prices: A Comparative Evaluation of Classical, Deep Learning and Transformer-based Models
Publication Name: Decision Making Advances
Publication Date: 2026-01-01
Volume: 4
Issue: 1
Page Range: 154-190
Description:
Forecasting commodity prices remains a challenging task due to market volatility, structural breaks, and changing economic conditions. This study evaluates the forecasting performance of classical econometric, deep learning, convolutional, and Transformer-based models for aluminum futures prices. Daily aluminum futures data are analysed using 11 forecasting approaches. Forecasting experiments are conducted for two distinct evaluation periods representing the years 2022 and 2025 to assess the robustness of model performance under different market environments. The empirical results reveal substantial differences in forecasting performance across model families. Recurrent neural network architectures, particularly GRU and RNN, consistently achieve the lowest forecasting errors across most horizons. The classical ARIMA model remains highly competitive despite its relative simplicity. In contrast, Transformer-based models generally fail to outperform simpler alternatives and frequently produce higher forecast errors. Statistical comparisons based on Diebold–Mariano tests and Model Confidence Set procedures indicate that performance differences among the best-performing models are often limited, suggesting that increased model complexity does not necessarily translate into superior predictive accuracy. Overall, the findings highlight the importance of empirical model evaluation and demonstrate that parsimonious forecasting approaches can remain effective competitors to substantially more complex machine learning architectures in aluminum futures price forecasting.
Open Access: Yes
