BNNmetrics · Bayesian Neural Networks for Bridging the Gap Between Machine Learning and Econometrics
Horizon 2020 — Marie Skłodowska-Curie Actions
- Duration
- 2020-10-01 → 2022-09-30
- EU contribution
- €207,312
- Participants
- 1
- Scheme
- MSCA-IF
Lines connect the coordinator with its partners.
Results in brief
Bayesian Neural Networks for Bridging the Gap Between Machine Learning and Econometrics
Bayesian methods are relevant in applications over desperate domains is because they implicitly deal with and focus on uncertainties. In high-risk and decisional domains (such as economics or medicine) uncertainties associated with models' forecasts and on the values of their estimated parameters are certainly not negligible. Unfortunately, Bayesian methods are known, at a general level, for being of difficult application though very attractive from a theoretical standpoint. There are only a few classes of problems that can be easily tackled in a Bayesian way, but in general, the shift toward a Bayesian prescription of a non-Bayesian model is challenging. It is thus not surprising that the applicability of Bayesian principles in machine learning has long been almost inaccessible. The overall objective of the action and of the research line I followed is that of devising feasible solutions for performing Bayesian inference in complex models characterized by a high number of parameters such as machine learning ones, and analyses to what extent such models compare with traditional ones. The application domain of the Action involves financial and economic data for estimation and forecasting with standard statistical models (regressions), econometric models (volatility models), and neural networks under a Bayesian approach. In particular, the action addresses the following two major points. 1. Establish if (i) a Bayesian approach to complex financial models is in first place feasible, (ii) whether and to which extent it outperforms analogous models estimated with non-Bayesian techniques, (iii) address the use of predictive distributions to analyze errors and uncertainties associated with the estimated parameters and model forecast. (iv) Address to which extent the Bayesian dimension provides decisional advantages with respect to standard non-Bayesian estimation. 2. Developing on and extending current existing methods for Bayesian inference in complex models to ease their implementation, computational requirements, and applicability.
Data: CORDIS, © European Union
Project 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.
Original text from CORDIS.
Participants
- AARHUS UNIVERSITET · Aarhus CCoordinatorDenmark
Links
Data: CORDIS, © European Union