HEAD
AI
For the Artificial Intelligence analytics we have created 3 Use Cases. Find more about each of them
 bellow! For each use case you can:
Check the main details here

  • go into details by reading the support peer-reviewed research paper

  • download the code and run it again with your own data

  •       get in contact with the creator of the use case
Use case 1

Use Case 1 Artificial Intelligence – Network models to enhance automated cryptocurrency portfolio management (Authors: Paolo Giudici, Paolo Pagnatoni, Gloria Polinesi) 

Use case 2

Use Case 2 Artificial Intelligence – Convergence and Divergence in European bond correlations (Authors: Peter Schwendner, Martin Schule, Martin Hildebrand) 

Use case 3

Use Case 3 Artificial Intelligence – Explainable Machine Learning in credit risk management (Authors: Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, Jochen Papenbrock) 

The 3 used cases were presented all across Europe in mode than 30 events.
Artificial Intelligence research events more
Artificial Intelligence regtech events more
Artificial Intelligence suptech events more
For enriching the field, we have also produced significant research and the main 12
research upapers produced on the topic are:
Check out the feedback we have collected during our events!
Check out more details about the use cases here!
Sentiment Analysis of European Bonds 2016–2018

Authors: Peter Schwendner, Martin Schule, martin Hillebrand

Key words: Bond market models, Correlation network models, Machine learning econometrics

Explainability of a Machine Learning Granting Scoring Model in Peer-to-Peer Lending,

Authors: Miller Janny Ariza-Garzon, Javier Arroyo, Antonio Caparrini, Maria-Jesus Segovia-Vargas

Key words: Explainable Artificial Intelligence, credit scoring, lending decisions

Assessment of Machine Learning Performance for Decision Support in Venture Capital Investments

Authors: Javier Arroyo, Francesco Corea, Guillermo Jimenez-Diaz, Juan A. Recio-Garcia,

Key words: Investment models, machine learning models, predictive accuracy

Explainable Machine Learning in Credit Risk Management,

Authors: Niklas Bussmann, Paolo Giudici, Dimitri Marinelli, Jochen Papenbrock, 

Key words: Explainable Artificial Intelligence, peer to peer lending, clustering models

COVID-19 contagion and digital finance,

Authors: Arianna Agosto, Paolo Giudici, 

Key words: Financial market models, covid-19 contagion, predictive models

Significance, relevance and explainability in the machine learning age: an econometrics and financial data science perspective,

Authors: Andreas G. F. Hoepner, David McMillan, Andrew Vivian, Chardin Wese Simen, 

Key words:  Financial market models, Machine learning models, econometric models

Neural networks and arbitrage in the VIX,

Authors: Joerg Osterrieder, Daniel Kucharczyk, Silas Rudolf, Daniel Wittwer, 

Key words: Financial market models, Neural networks, volatility models

Shapley-Lorenz eXplainable Artificial Intelligence,

Authors: Paolo Giudici, Emanuela Raffinetti

Key words: Explainable Artificial Intelligence, Shapley method, Rank-based performance

Interpretable Machine Learning for Diversified Portfolio Construction

Authors: Markus Jaeger, Stephan Krügel, Dimitri Marinelli, Jochen Papenbrock, Peter Schwendner, 

Key words: Explainable Artificial Intelligence, portfolio allocation, machine learning models

Matrix Evolutions: Synthetic Correlations and Explainable Machine Learning for Constructing Robust Investment Portfolios

Authors: Jochen Papenbrock, Peter Schwendner, Markus Jaeger, Stephan Krügel

Key words: Explainable Artificial Intelligence, portfolio robustness, machine learning models

 

Evaluation of multi-asset investment strategies with digital assets

Authors: Alla Petukhina, Erin Sprünken

Key words: Financial Market imodels, Digital investments, portfolio allocation

Predictability and pricing efficiency in forward and spot, developed and emerging currency markets

Authors: Valerio Potì, Richard Levich, Thomas Conlon

Key words: Currency markets models, Machine learning models, predictive models