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.

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.

This study examines the competitive nature of the Hungarian poultry sector between 2006 and 2016. The poultry population has stagnated over the…

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 aim of the study is to explore the relationship between overspending and the method of payment, to highlight its causes. The non-representative survey was conducted between 2020 and 2021 in Hungary (n = 499) using the snowball sampling of data collection. They examined the relationship between age, education, place of residence, and payment methods, and analyzed the impact of internal and external factors on cash consumption and sustainability. The results showed that the use of cash as a method of payment is characteristic with advancing age, and higher education has a higher willingness to pay electronically according to the examined sample, and the existence of electronic access is not related to the size of the settlement. It can be stated that the majority of respondents have no choice when choosing a payment method. The answers reflect confidence in electronic payment solutions (a value of 2.21 on a six-point scale). Each group believes that they can consciously plan their budget (alternative budget). With proper communication and awareness of these influencing factors, financial awareness can be strengthened.

Purpose: We analyze the role of monetary policy shocks on food inflation in Hungary from January 2007 to March 2023, including the period of the COVID-19 crisis and the Russo–Ukrainian war. Design/methodology/approach: We use quantile regression with three different specifications. The structural breaks in the time series and the monetary policy’s lag in response are also taken into account. We use the M0 money supply and the three-month Hungarian National Bank (MNB) deposit rate as monetary measures to check the robustness of our findings. Findings: We find that neither the money supply nor the exchange rate affected food inflation across quantiles. In the case of high food price inflation, reducing short-term government bond yields may be an effective solution. Household final consumption affected food prices in the lower quantiles, and the global food price index similarly affected Hungarian food inflation. The results are robust to different specifications. Research limitations/implications: This research has limitations as follows: while Hungary’s food prices provide a valuable case study, expanding to more countries is advisable; although quantile regression captures details, its reliability for non-linear relationships is questionable; additionally, considering various global food price indicators can enhance result robustness. Originality/value: The paper contributes to the longstanding political debate regarding the effectiveness of monetary policy in stabilizing food inflation. The findings emphasize the importance of considering both domestic and global factors in formulating policy responses to food price dynamics.