Dr. Branka Hadji Misheva

Persönliches Profil

Arbeits- und Forschungsschwerpunkte, Spezialkenntnisse

Machine learning for finance;
eXplainable AI
Network models

Aus- und Fortbildung

PhD in Economics and Management of Technology

Projekte

• DataInc – Intelligent Data Integration and Cleaning / Teammitglied / Projekt laufend
• Strengthening Swiss Financial SMEs through Applicable Reinforcement Learning / Teammitglied / Projekt laufend
• Decentralized financing of Fairtrade producers using a blockchain-based solution / Teammitglied / Projekt laufend
• COST Action – Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry / ProjektleiterIn / Projekt abgeschlossen
• Towards Explainable Artificial Intelligence and Machine Learning in Credit Risk Management / ProjektleiterIn / Projekt abgeschlossen
• Advanced/AI-supported Rating Models for P2P systems / ProjektleiterIn / Projekt abgeschlossen
• Hybrid Approach for Robust Identification and Measurement of Investors Driving Corporate Sustainability and Innovation. Design of Policy Tools for Evaluating the Impact of Specific Investors and Assessing the Quality of Companies’ Investor Bases. / Teammitglied / Projekt abgeschlossen
• Digitalisierung nicht bankfähiger Vermögenswerte (insbesondere Kunst) / Teammitglied / Projekt abgeschlossen
• Deep Learning & Neuronal Networks: Selbstständige KI-Agenten zur Entwicklung von neuartigen Handelsstrategien im Asset Management auf Basis von Self-Play / Teammitglied / Projekt abgeschlossen
• 4th Conference Finance and Industry 2019 / Teammitglied / Projekt abgeschlossen
• Europäische Workshops in Finance / Teammitglied / Projekt abgeschlossen
• FIN-TECH – Financial Supervision and Technology Compliance Training Programme / ProjektleiterIn / Projekt abgeschlossen
• Big Data Analytics Research / ProjektleiterIn / Projekt abgeschlossen
• Währungsabsicherung für KMUs und Pensionskassen / Teammitglied / Projekt abgeschlossen
• Mathematics and Fintech: The next revolution in the digital transformation of the finance industry / Teammitglied / Projekt abgeschlossen

Publikationen

• Posth, Jan-Alexander; Kotlarz, Piotr Kamil; Hadji Misheva, Branka; Osterrieder, Jörg; Schwendner, Peter,

  2021.

  The applicability of self-play algorithms to trading and forecasting financial markets.

  Frontiers in Artificial Intelligence.

  4(668465).

  Verfügbar unter: https://doi.org/10.3389/frai.2021.668465

• Hadji Misheva, Branka; Jaggi, David; Posth, Jan-Alexander; Gramespacher, Thomas; Osterrieder, Joerg,

  2021.

  Audience-dependent explanations for AI-based risk management tools : a survey.

  Frontiers in Artificial Intelligence.

  4(794996).

  Verfügbar unter: https://doi.org/10.3389/frai.2021.794996

• Chen, Ying; Giudici, Paolo; Hadji Misheva, Branka; Trimborn, Simon,

  2020.

  Lead behaviour in Bitcoin markets.

  Risks.

  8(1), S. 4.

  Verfügbar unter: https://doi.org/10.3390/risks8010004

• Ahelegbey, Daniel Felix; Giudici, Paolo; Hadji Misheva, Branka,

  2019.

  Factorial network models to improve P2P credit risk management.

  Frontiers in Artificial Intelligence.

  2, S. 8.

  Verfügbar unter: https://doi.org/10.3389/frai.2019.00008

• Ahelegbey, Daniel Felix; Giudici, Paolo; Hadji Misheva, Branka,

  2019.

  Latent factor models for credit scoring in P2P systems.

  Physica A: Statistical Mechanics and its Applications.

  522, S. 112-121.

  Verfügbar unter: https://doi.org/10.1016/j.physa.2019.01.130

• Giudici, Paolo; Hadji Misheva, Branka; Spelta, Alessandro,

  2019.

  Network based credit risk models.

  Quality Engineering.

  32(2), S. 199-211.

  Verfügbar unter: https://doi.org/10.1080/08982112.2019.1655159

• Giudici, Paolo; Hadji Misheva, Branka; Spelta, Alessandro,

  2019.

  Network based scoring models to improve credit risk management in peer to peer lending platforms.

  Frontiers in Artificial Intelligence.

  2(3).

  Verfügbar unter: https://doi.org/10.3389/frai.2019.00003

• Hirsa, Ali; Osterrieder, Jörg; Hadji Misheva, Branka; Posth, Jan-Alexander,

  2021.

  Deep reinforcement learning on a multi-asset environment for trading.

  arXiv.

  Verfügbar unter: https://doi.org/10.21256/zhaw-22850

• Posth, Jan-Alexander; Hadji Misheva, Branka; Kotlarz, Piotr Kamil; Osterrieder, Jörg; Schwendner, Peter,

  2020.

  The applicability of self-play algorithms to trading and forecasting financial markets : a feasibility study.

  SSRN.

  Verfügbar unter: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=3737714

Publikationen vor Tätigkeit an der ZHAW

Ahelegbey, D.F., Giudici, P. and Hadji Misheva, B. (2019) Latent factor models for credit scoring in P2Psystems. Physica A 522, pp.112-121

Giudici, P. and Hadji Misheva, B. (2018). P2P lending scoring models: Do they predict default? Journal of Digital Banking, 2(4):353-368

Hadji Misheva, B., Giudici, P. and Pediroda, V. (2018). Network-based models to improve credit scoring. 2018IEEE 5th International Conference on Data Science and Advanced Analytics accuracy

Giudici, P. and Hadji Misheva, B. (2017). Scoring models for P2P lending platforms: An evaluation of predictive performance. Conference proceedings: Statistics and Data Science: New Challenges, New Generations

Giudici, P. and Hadji-Misheva, B. (2017). P2P Lending Scoring Models: Do they Predict Default? Journal of Digital Banking.

Bucevska, V. and Hadji-Misheva, B. (2015). Determinants of Profitability in the Banking Industry: EmpiricalEvidence from Selected Balkan Countries. Eastern European Economics Journal

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