Description du projet
Peut-on apprendre à un robot à devenir un négociateur en bourse?
L’apprentissage automatique a stimulé les progrès dans des domaines aussi divers que la reconnaissance d’images et les voitures à conduite autonome. Le projet DataABM, financé par l’UE, vise à exploiter ce potentiel en formant un ordinateur à la simulation du comportement des investisseurs sur le marché boursier. Pour ce faire, les chercheurs créeront un modèle à base d’agents (ABM pour «Agent-Based Model») qui sera entraîné par apprentissage automatique sur un vaste ensemble de données concernant les investisseurs. Le modèle pourrait améliorer la compréhension du processus de prise de décision des investisseurs et fournir un outil permettant de mieux prévoir et simuler les fluctuations du marché boursier. Cela pourrait aider les régulateurs et les décideurs politiques à calculer les répercussions des mesures économiques à l’avenir.
Objectif
Image recognition or self-driving cars are just a few among many applications of machine learning (ML) methods. Given that we can train a cobot to mimic human behaviour, why not train a computer to mimic and simulate investor behaviour in stock markets? This would not only improve understanding about investor decision making and their interaction, but provide effective tools to predict investor behaviour on the microscopic level and simulate stock markets on the macroscopic level. The main objective is to create a data-driven Agent-Based Model (ABM), where agents' behaviour is governed by ML. Such models need appropriate data to be trained, which is possible thanks to a unique, big data set on investor level data accessible through the host. The objectives are: i) framework for data-driven ABM, ii) interpretable ML for ABM, iii) verification of the interpretability of data-driven ABMs using synthetic data, iv) training the data-driven ABMs using actual shareholder registration data, and finally v) analysis of investors’ decision-making mechanism. The objectives will be reached by using ML methods that achieve intrinsic interpretability with and without deep supervised learning. This research requires: a) strong numerical skills and experience with simulations, b) computer infrastructure allowing to carry out largescale numerical analysis for which the fellow and the host have complementary experience. The results will bring us closer to understanding the behavioural mechanism of market participants. The project does not just gain understanding, but introduces a data-driven approach to more realistic agent-based modelling, which is completely new. The outcome should focus the attention of regulators and policy makers, who are often unable to realistically predict the effects of considered economic measures. Finally, the project contributes to the ML literature on verification of interpretable methods with extensive data sets.
Champ scientifique
CORDIS classe les projets avec EuroSciVoc, une taxonomie multilingue des domaines scientifiques, grâce à un processus semi-automatique basé sur des techniques TLN.
CORDIS classe les projets avec EuroSciVoc, une taxonomie multilingue des domaines scientifiques, grâce à un processus semi-automatique basé sur des techniques TLN.
- engineering and technologymechanical engineeringvehicle engineeringautomotive engineeringautonomous vehicles
- natural sciencescomputer and information sciencesartificial intelligencemachine learningsupervised learning
- natural sciencescomputer and information sciencesdata sciencebig data
- natural sciencescomputer and information sciencesartificial intelligencecomputer visionimage recognition
- natural sciencesmathematicsapplied mathematicsnumerical analysis
Programme(s)
- HORIZON.1.2 - Marie Skłodowska-Curie Actions (MSCA) Main Programme
Régime de financement
HORIZON-TMA-MSCA-PF-EF - HORIZON TMA MSCA Postdoctoral Fellowships - European FellowshipsCoordinateur
33100 Tampere
Finlande