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Short-term scientific missions support our COST Action.

If you want to apply, please see here.

Applications Grant Period 1

If you are an evaluator for the STSM and ITC conference grant applications, you can find them here (protected).

Ahmad Amine Loutfi, PhD Fellow, Norwegian University of Science and Technology (NTNU), Norway.[1]

  • STSM title: Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry (FinAI).
  • Host institution: Institute of Wealth & Asset Management, Zurich University of Applied Sciences (ZHAW), Switzerland.
  • STSM project: In this project, we propose to study the extent to which alternative data (News/reports) can predict electricity spot prices. This will allow us to assess the backtesting of relevant investment strategies. The results of this project will also be used to extend an ongoing research project where we study electricity spot price prediction based only on conventional data. We aim to augment the conventional data set with a new feature which reflects the alternative data, and then run the newly augmented dataset through the same neural network model and then compute the new loss function results in order to assess the models’ performance with and without alternative data.

Miller Janny Ariza Garzón, PhD student at Data Science [4].Research member of Project FINTECH-EU Ho2020 [5]. Facultad de Informática [6]. Universidad Complutense de Madrid (UCM) [7]. Spain.

  • STSM title: Fintech and Artificial Intelligence in Finance - Towards a transparent financial industry (FinAI).
  • Host institution: Zurich University of Applied Sciences (ZHAW) School of Engineering, Winterthur, Switzerland.
  • Home institution: Universidad Complutense de Madrid (UCM), Madrid, Spain.
  • STSM project: Being part of the working group 2 - Transparent versus Black Box Decision-Support Models in the Financial Industry [8], we will investigate the tradeoff between explainability and predictive performance of different black-box models as they apply to financial problem sets, primarily in risk management-credit risk. Specifically, we will study the elements that must be evaluated for a black-box model to be considered interpretable and explainable to take advantage of its predictive potential.

Stjepan Picek, assistant professor, Delft University of Technology, The Netherlands.[9]

  • STSM title: Genetic Programming for the Fraudulent Activity Detection: Performance and Transparenct Perspectives.
  • Host institution: University of Zagreb, Faculty of Electrical Engineering and Computing (FER), Croatia.
  • Start date: 2021-01-11
  • End date: 2021-01-26
  • STSM project: In this project, we will explore available datasets for fraudulent behavior classification and evaluate their common characteristics. Based on it, we will start a series of experiments with techniques from the machine learning domain and genetic programming to compare their performance. Finally, we will investigate what are the transparency considerations when using genetic programming and what kind of interpretability/explainability one can hope to achieve with such techniques.

Danial Florian Saef, PhD Researcher, Humboldt University Berlin, Germany. [11]. Member of International Research Training Group 1792 "High Dimensional Nonstationary Time Series". [12]

  • STSM title: Contagion dynamics in high frequency - modeling shock impacts in cryptocurrency markets
  • Host institution: University College London, United Kingdom.
  • STSM project: Shocks in financial markets often lead to severe contagion effects, especially in strongly correlated and highly capitalised markets these effects can be observed almost immediately. A large branch of literature has already focused on the phenomenon of financial contagion. However, the recent availability of high frequency data gives new possibilities to investigate it. A gap in the literature exists regarding the effects of financial contagion in correlated assets w.r.t. high frequency data. The newly emerging cryptocurrency market is highly capitalised, yet it differs from traditional markets due to non-stop trading, lower volume and high correlation among currencies. These properties cause larger volatility and make cryptocurrencies more vulnerable to contagion effects. We analyse a diverse tick cryptocurrency tick dataset and test for jumps following Lee & Mykland (2012), and then aim to model cryptocurrencies as a network of interconnected assets. We want to show that patterns in contagion dynamics exist and that they can be used to predict how future shocks evolve. Eventually, this model aims at helping to understand the dynamics of this new and largely unregulated asset class better. The non-parametric nature makes it also adaptable to applications in traditional assets, thus our goal is to provide a general framework for modeling the dynamics in correlated financial time series with jumps in high frequency.

Wei Li, PhD Fellow, Norwegian University of Science and Technology, Norway. [14]. Member of Centre for Banking and Finance. [15]

  • STSM title: A Data-driven Evolutionary Case-based Reasoning Approach for Financial Risk Detection.
  • Host institution: Humboldt University Berlin, Germany.
  • STSM project: In this mobility, the main targets are to improve my current paper about financial risk prediction applying machine learning method, and build up common interests in machine learning application in finance and make a collaboration for new papers.