Project description
Bayesian neural networks for better insight into markets
The complexity of financial data has rapidly expanded over the past two decades. This poses challenges for appropriate and effective tools for data analytics. Econometrics relies on parsimonious probabilistic models aimed at describing the economic phenomena. While it allows for models of great interpretability and excellent properties, it cannot upscale and embed the complexity of such modern data. Conversely, machine learning proved to be of high appeal for tackling a broad class of challenging multidimensional big-data problems. The EU-funded project BNNmetrics proposes to apply a class of ML methods known as Bayesian Neural Networks as a feasible tool for modern econometric research. This will significantly improve modelling processes, analyses and insights into the complexity of real markets.
Objective
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 do deal with uncertainties represent DL’s main shortcomings. On the other hand, classic econometrics methods, of limited variables, great interpretability and with excellent probabilistic properties, have failed to prove appropriate for the analysis of modern high-frequency data. The application in financial econometrics of a DL sub-class of algorithms known as Bayesian neural networks (BNNs) is expected to revolutionize the process of modeling, analyzing, and understanding trading behavior in real markets. BNNs’ attractive properties have the potential of bridging the gap between classic econometrics and ML. This research will show measurable improvements over the current state of the art, both from the financial econometrics and the ML sides, in three problems defined on high-frequency financial data: volatility modeling, stock mid-price movement prediction, and interdependence analysis between stock prices.
Fields of science
Not validated
Not validated
Programme(s)
Funding Scheme
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)Coordinator
8000 Aarhus C
Denmark