HEИндивидуална стипендия2023–2025

DataABM · Data-Driven Agent-Based Models of Investors with Machine Learning

„Хоризонт Европа“ — Действия „Мария Склодовска-Кюри“

Период: 2023-09-01 → 2025-08-31
Финансиране от ЕС: 215 534 €
Участници: 2
Схема: HORIZON-TMA-MSCA-PF-EF

Линиите свързват координатора с партньорите.

Накратко на български

Финансовите пазари се анализират чрез комбиниране на компютърни симулации с агенти и машинно обучение, за да се създадат по-реалистични модели на инвеститорите. Това помага за по-добра проверка на алгоритмите и повишава точността на прогнозите във финансите.

Този кратък обзор е генериран от изкуствен интелект

Кратко обяснение, генерирано от езиков модел по текста на CORDIS. Оригиналът е по-долу.

Резултати накратко

Data-Driven Agent-Based Models of Investors with Machine Learning

Financial markets are an exciting research subject with direct impact on the world around us. They also provide enormous amounts of highly detailed data. Such data is often subject to noise, errors, or missing information, but its quality is still much better than for most other non-experimental research fields. Nevertheless, limited experimental capabilities cannot easily be replaced by a lot of data. One way of dealing with this problem is the agent-based modeling, a bottom-up simulation approach, where simple programable agents were used to mimic financial markets. Agent-based modeling is simple, flexible, and allows to test all sorts of different settings. It also became more scalable once powerful computing power was at hand. There is, however, a question whether simple rule-based agents are realistic models of true investors. Access to data from financial markets makes them a suitable application for rapidly developing methods of machine learning and artificial intelligence. However, bluntly applying models to data can be tricky and pose a challenge in terms of verification and interpretation of obtained results. At the same time, we observe how novel machine learning tools allow to improve the predictive power in finance beyond the old models. The main objective of the project is to explore the two way benefits of combining agent-based modeling and machine learning in financial computing. On one hand, agent-based models can provide synthetic data with ground truths, which can be used to verify machine learning models. On the other hand, generative artificial intelligence models can be a way of providing more realistic agents, imitating closely true investors.

Текст от CORDIS, на английски · Данни: CORDIS, © Европейски съюз

Цел на проекта

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.

Оригинален текст от CORDIS (на английски).

Участници

TAMPEREEN KORKEAKOULUSAATIO SR · TampereКоординаторФинландия
AARHUS UNIVERSITET · Aarhus CДания