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 (EuroSciVoc)
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
CORDIS classifies projects with EuroSciVoc, a multilingual taxonomy of fields of science, through a semi-automatic process based on NLP techniques. See: The European Science Vocabulary.
- social sciences economics and business economics econometrics
- natural sciences computer and information sciences artificial intelligence machine learning deep learning
- natural sciences computer and information sciences artificial intelligence computational intelligence
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Keywords
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Project’s keywords as indicated by the project coordinator. Not to be confused with the EuroSciVoc taxonomy (Fields of science)
Programme(s)
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
Multi-annual funding programmes that define the EU’s priorities for research and innovation.
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H2020-EU.1.3. - EXCELLENT SCIENCE - Marie Skłodowska-Curie Actions
MAIN PROGRAMME
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H2020-EU.1.3.2. - Nurturing excellence by means of cross-border and cross-sector mobility
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Topic(s)
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Calls for proposals are divided into topics. A topic defines a specific subject or area for which applicants can submit proposals. The description of a topic comprises its specific scope and the expected impact of the funded project.
Funding Scheme
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
Funding scheme (or “Type of Action”) inside a programme with common features. It specifies: the scope of what is funded; the reimbursement rate; specific evaluation criteria to qualify for funding; and the use of simplified forms of costs like lump sums.
MSCA-IF - Marie Skłodowska-Curie Individual Fellowships (IF)
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Call for proposal
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
Procedure for inviting applicants to submit project proposals, with the aim of receiving EU funding.
(opens in new window) H2020-MSCA-IF-2019
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Net EU financial contribution. The sum of money that the participant receives, deducted by the EU contribution to its linked third party. It considers the distribution of the EU financial contribution between direct beneficiaries of the project and other types of participants, like third-party participants.
8000 Aarhus C
Denmark
The total costs incurred by this organisation to participate in the project, including direct and indirect costs. This amount is a subset of the overall project budget.