The findings demonstrate the added value of combining predictive and causal machine learning, together with identification-robust and panel-robust inference, for uncovering heterogeneity that conventional econometric approaches overlook and offer a transferable methodological template for causal machine learning applic...
The results indicate that the value of machine learning forecasts in trading depends less on raw predictive accuracy than on the risk-management logic wrapped around it, and that this advantage is statistically robust across assets, regimes, and specification choices rather than an artifact of a single favorable sample...
László Vancsura, Tibor Tatay, Tibor Bareith· FinTech· 0 citations
This study evaluates the forecasting performance of classical econometric, deep learning, convolutional, and Transformer-based models for aluminum futures prices to demonstrate that parsimonious forecasting approaches can remain effective competitors to substantially more complex machine learning architectures in alumi...
László Vancsura, Tibor Tatay, Tibor Bareith et al.· Decision Making Advances· 0 citations
This study compares statistical and artificial intelligence-based forecasting models for copper price prediction under different market regimes and structural break conditions and reveals a consistent error–profit paradox: models with the highest statistical forecasting accuracy do not necessarily generate the best tra...
László Vancsura, Tibor Tatay, Tibor Bareith· Machine Learning and Knowled...· 0 citations
The value-creation capacity of active asset management remains one of the most debated issues within modern portfolio theory. While the relationship between costs and performance is typically negative in developed markets, in smaller and less liquid markets—where information asymmetry is more pronounced—higher fees may...
László Vancsura, Tibor Tatay, Tivadar Zakár et al.· Economies· 0 citations
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