Early Stage Researcher (ESR)

Positions available within the research project entitled: “Digital Finance - Reaching New Frontiers (DIGITAL)”, project number 101119635 through the Horizon Europe Marie Skłodowska-Curie Actions for Doctoral Networks (MSCA DN)

Offer Description*: We are looking for highly motivated and creative Ph.D. candidates interested in Digital Finance, for the following Individual Research Projects (IRP):

• Developing industry-ready automated trading systems to conduct EcoFin analysis using deep learning algorithms
• Detecting anomalies and dependence structures in high dimensional, high frequency financial data

During the project, the PhD students will closely collaborate with industry and a doctoral training network spread throughout Europe, including extended research stays abroad. 

The successful applicant will join the Dep. of Statistics and Econometrics, Faculty of Cybernetics, Statistics and Economic Informatics, Bucharest University of Economic Studies.

Background 

These 2 Ph.D. positions are at the Bucharest University of Economic Studies in the context of the international Marie Skłodowska-Curie Actions project DIGITAL. For the general description of DIGITAL and the Ph.D. positions, please check https://www.digital-finance-msca.com/.  

DIGITAL's main goal? To significantly advance the methodologies and business models for Digital Finance through the use of five interconnected research objectives https://www.digital-finance-msca.com/: 

1. Ensure sufficient data quality to contribute to the EU's efforts of building a single digital market for data 
2. Address deployment issues of complex artificial intelligence models for real-world financial problems 
3. Validate the utility of state-of-the-art explainable artificial intelligence (XAI) algorithms to financial applications and extend existing frameworks 
4. Design risk management tools concerning the applications of the Blockchain technology in Finance 
5. Simulate financial markets and evaluate products with a sustainability component 

IRP Detecting anomalies and dependence structures in high dimensional, high frequency financial data

Herding, a well-known financial anomaly, is thought to cause high volatility, volatile prices, and low liquidity (Bikhchandani and Sharma, 2000). Greed and herd behaviour caused the seventeenth-century tulip mania, the 1995–2000 Internet bubble, and the 2015 Chinese stock market crash. This project studies high-dimensional sentiment networks and herd behaviour on the stock market. To better fit investor sentiment, the project will calibrate the option pricing model, Stochastic Volatility and Correlated Jump (SVCJ).

The project will detect anomalies like herd behaviour and dependence structures in high-dimensional, high-frequency financial data. We plan to create a tail event-driven network that graphs or matrices the interconnections of a large panel to understand sentiment network mechanics. That will inform our herd behaviour detection and option pricing model calibration. Results will be disseminated through publications in prestigious journals available via public repositories, presentations at prestigious conferences, and knowledge exchange.

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

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.

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.

About the organization 

Bucharest University of Economic Studies (ASE) ASE is the leader of the higher education institutions in the field of Economic Sciences and Public Administration in Romania and South-East Europe. It has13 Faculties, 23.000 + students (3.5% international),  900 + academic staff (10% international), 25 Undergraduate programs, 78 Master programs, 2 international MBA programs, 10 doctoral schools.