Andreas obtained his PhD from City University of Hong Kong in 2014 analysing network structures of international trade and cross-border investment flows. His current research is dedicated to applying techniques from machine learning and artificial intelligence to the analysis of large granular datasets with the goal to integrate those into frameworks for economic modelling and policy analysis. You can visit his page on Google Scholar.
When bigger isn’t better: UK firms’ equity price performance during the Covid-19 pandemic - November 2020
Wir sind die Roboter: can we predict financial crises? - October 2020
All you need is cash - February 2020
Opening the machine learning black box - May 2019
New machines for The Old Lady - November 2017
Big Data jigsaws for Central Banks – the impact of the Swiss franc de-pegging - April 2017
Compendium on the diagnostic toolkit for competitiveness, with G Aiello, K Benkovskis, B Bluhm, E Bobeica, F Cavallini, S Christodoulopoulou, M De Clercq, C Giordano, P Karadeloglou, A Leonte, K Lommatzsch, P Lopez-Garcia, G Momchilov, C Osbat, L Orszaghova, F Pappadà, E Prades Illanes, M Silgoner, O Tkacevs, R Vermeulen, J and F Zollino, ECB, Occasional Paper Series (2015)
Interactions between financial and environmental networks in OECD countries, with F Ruzzenenti, E Ticci, P Vozzella and G Gabbi, PLOS One (2015)
Cross-border Portfolio Investment Networks and Indicators for Financial Crises, with S Joseph and G Chen, Nature Scientific Reports (2014)
Netconomics: Novel Forecasting Techniques from the Combination of Big Data, Network Science and Economics, with I Vodenska, E Stanley and G Chen, arXiv preprint arXiv:1403.0848 (2014)
Network Centrality and Key Economic Indicators: A Case Study, with G Chen, Springer International Publishing, Network Models in Economics and Finance (2014)
Composite centrality: A natural scale for complex evolving networks, with G Chen, PhysiPhysica D: Nonlinear Phenomena (2014)
