QFinLab is pleased to announce:
Speaker
Prof. Emilio Barucci
Politecnico di Milano
Toward the digital euro
technology, banks and monetary policy
Two appointments:
APRIL 8th 2021, 17.30 CET
Zoom Virtual Room:
https://polimi-it.zoom.us/j/82532398177
ID: 825 3239 8177
APRIL 14th 2021, 17.30 CET
Zoom Virtual Room:
https://polimi-it.zoom.us/j/83251333953
ID: 832 5133 3953
Valerio Potì (University College Dublin)
COVID Narrative Risk: A Computational Linguistic Approach to the Econometric Identification of Narrative Risk During the COVID-19 Pandemic
Abstract
In this paper, we study the role in financial markets of narratives related to the ongoing COVID-19 pandemic. The pandemic represents a natural setting for the development of narratives that may effects or be affected by financial markets. We thus treat the pandemic as a natural experiment on the relation between prevailing narratives and financial markets. We adopt the SIR model and natural language processing on financial newspaper news and Twitter tweets that deal at the same time with financial market topics and COVID-19 to study the dynamics and determinants of coronavirus narrative epidemics. Our aim is to establish whether there an “infodemic” develops, and whether the prevailing narrative, whether resemnbling an infodemic or otherwise, drives or is driven by financial markets developments, controlling for developments regarding the COVID-19 pandemic. We find associations between narratives about the epidemic, stock market dynamics (both regarding returns and volatility) and government responses to COVID-19. We also find that the narrative spread does resemble an infodemic, since it is well described by the SIR epidemic model. Our estimates of the shape of the narrative infodemic curves in different countries depend on whether the COVID-19 outbreak occurred early and on its severity. Negative tones and tones communicating uncertainty are prevalent in the growing stage of the infodemic. We find preliminary evidence of a causality relation between the negative and uncertainty tones of coronavirus tweets and both market return and volatility change.
Charalampos Stasinakis – University of Glasgow
(with G. Sermpinis)
Abstract
The financial technology revolution is a reality, as the financial world is gradually transforming into a digital domain of high-volume information and high-speed data transformation and processing. The more this transformation takes place, the more consumer and investor behaviour shifts towards a pro-technology attitude of financial services offered by market participants, financial institutions and financial technology companies. This new norm is confirming that information technology is driving innovation for financial technology. In this framework, the value of big data, artificial intelligence and machine learning techniques becomes apparent. The aim of this chapter is multi-fold. Firstly, a multidimensional descriptive analysis is shown to familiarise the reader with the extent of penetration of the above in the financial technology road-map. A short non-technical overview of the methods is then presented. Next, the impact of data analytics and relevant techniques on the evolution of financial technology is explained and discussed along with their applications’ landscape. The chapter also presents a glimpse of the shifting paradigm these techniques bring forward for several fintech related professions, while artificial intelligence and machine learning techniques are tied with the future challenges of AI ethics, regulation technology and the smart data utilisation.
