DC17: Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns

Objectives

The surge in interest in algorithmic fairness and sustainability is present in numerous fields of study, including finance and portfolio management in particular. This project's objective is to create new portfolio optimization models that address some of the difficulties associated with incorporating fairness and sustainability into investment management. The objective of the project is to increase understanding of the source and methods for eliminating algorithmic bias in finance in order to generate sustainable outcomes. The project will equip financial institutions with new sustainable and equitable algorithmic solutions to increase customer trust.

Expected Results

The primary anticipated outcome of the project is the development of new algorithmic solutions for multiple areas of finance, such as sustainable portfolio management. The project will equip financial institutions with new tools to comply with EU sustainability regulations. The subsequent anticipated outcome is the publication of a library containing all of the designed algorithms in a public repository. A significant emphasis will be placed on the dissemination of the anticipated results.

Secondments (2)

Recruitment & Hosting Details

DC 17 BFH53, 54 University of Twente M9 36

Total 540 + 72

Individual Research Projects, including secondment plan

Table 3.1.c Individual Research Projects

Fellow Host institution PhD enrolment Start date Duration Deliverables

1

DC 17 BFH UTW Month 9 36 months D 3.2, 3.3

Fair Algorithmic Design and Portfolio Optimization under Sustainability Concerns (WP 3)

Objectives: The surge in interest in algorithmic fairness and sustainability is present in numerous fields of study, including finance

and portfolio management in particular. This project's objective is to create new portfolio optimization models that address some of

the difficulties associated with incorporating fairness and sustainability into investment management. The objective of the project is

to increase understanding of the source and methods for eliminating algorithmic bias in finance in order to generate sustainable

outcomes. The project will equip financial institutions with new sustainable and equitable algorithmic solutions to increase customer

trust.

Expected Results: The primary anticipated outcome of the project is the development of new algorithmic solutions for multiple areas

of finance, such as sustainable portfolio management. The project will equip financial institutions with new tools to comply with EU

sustainability regulations. The subsequent anticipated outcome is the publication of a library containing all of the designed algorithms

in a public repository. A significant emphasis will be placed on the dissemination of the anticipated results, which will be accomplished

through the following channels: at least one publication in prestigious open-access journals and at least three presentations at

prestigious conferences and open events. The final outcome of the project will be a comprehensive exchange of knowledge with

project partners.

Planned secondments: BIS,Rafael Schmidt, M15, 18 months, contribute macro-economic datasets, ongoing projects as well as

overall expertise in banking supervision

ECB, Dr. Lukazs Kubicki, M33, 12 months, exposure to gloabally leading central bank research, training ion EU principles

34

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

Deliverables