DC12: Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms

Objectives

This IRP will focus on addressing the challenges associated with automated trading systems in the direction of industry-ready platforms, i.e. minimising the risks of mechanical failures, improving the explainability of the underlying AI/ML models used in automated trading systems to better address performance-related issues, and also addressing ESG/CSR and ethical issues. This area will contribute significantly to Green Finance, thereby addressing the European Green Deal.

Expected Results

The project's outcomes will provide financial institutions with new automated trading tools. The primary anticipated outcome of the project is the design of new trading algorithm solutions for mitigating the risks of mechanical failures, enhancing the explainability of the underlying AI/ML models used in automated trading systems to better address performance-related issues, as well as ESG/CSR and ethical concerns.

Secondments (2)

Recruitment & Hosting Details

DC 12 ASE Bucharest University of Economic Studies M9 36

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Project: 101119635 — DIGITAL — HORIZON-MSCA-DN-2022

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DC 12 ASE ASE Month 9 36 months D 2.3

Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms (WP 2)

Objectives: This IRP will focus on addressing the challenges associated with automated trading systems in the direction of industry-ready

platforms, i.e. minimising the risks of mechanical failures, improving the explainability of the underlying AI/ML models used in automated

trading systems to better address performance-related issues, and also addressing ESG/CSR and ethical issues. This area will contribute

significantly to Green Finance, thereby addressing the European Green Deal.

Expected Results: The project's outcomes will provide financial institutions with new automated trading tools. The primary anticipated

outcome of the project is the design of new trading algorithm solutions for mitigating the risks of mechanical failures, enhancing the

explainability of the underlying AI/ML models used in automated trading systems to better address performance-related issues, as well as

ESG/CSR and ethical concerns. The anticipated results will be disseminated through the following channels: 1) Publications in prestigious

journals made widely accessible through public repositories; 2) Presentations at prestigious conferences; and 3) Knowledge exchange with

project partners.

Planned secondments: Royalton, Dr. Michael Althof, M21, 18 months, research on crypto assets for prototype and user acceptance

ARC, Prof. Dr. Ioannis Emiris, M39, 6 months, applied industry-research, use large-scale computing infrastructure for deep-learning

algorithms

Fellow Host institution PhD enrolment Start date Duration Deliverables

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Deliverables