CORDIS Project
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This research explores the application of Bayesian neural networks in financial econometrics to analyze high-frequency financial data. It aims to enhance modeling and prediction of market behaviors, addressing limitations of traditional econometric methods.
The complexity and volume of financial data in modern financial markets have been exponentially growing during the last decades.
Machine learning (ML) methods such as Deep learning (DL) have been widely utilized for several classification and prediction problems, given their intrinsic flexibility, appropriateness for large multidimensional problems, and ability to discover and adapt to non-linear patterns.
However, the enormous number of parameters, their difficult interpretation and inability…
AARHUS UNIVERSITET
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