11:44 a.m.
*Title:
Econophysics and economics: the role of AI
*Abstract:
Econophysics and economics. Two scientific disciplines that have carved a long way into
the subject of financial markets in the last 30 years, providing new theoretical models,
methods and results. Nevertheless, despite sharing the same element of scientific
investigation, they seem to proceed on strictly separated ways with an absolute lack of
dialogue. By considering a crucial problem on a financial market (the early detection of
“abnormal behaviors” such as financial crashes or bubbles), aim of my research is to bring
in the best of both worlds: the trends and explanations via rational behaviours from
economics and the apparent extreme behaviours from econophysics. The conceptual bridge is
provided by the introduction of the concept of time asymmetry (i.e. irreversibility) as a
fundamental component of economic behaviour. The asymmetry can be easily seen by direct
inspection of most time series data for financial instruments in which it is clear that an
equilibrium process is not generating the signal. We can model this disruption of
equilibrium using concepts from Prigogine’s thermodynamics (the dissipative structures)
and in so doing can explain the general dynamics of financial observables, rather than
either the trend-like behaviour or the formation of bubbles and crashes. According to the
dissipative structures conceptual paradigm, I have identified the news on a financial
market (that is the complex system) as the crucial parameter explaining its changes of
phase. The role for the AI inside this conceptual model is to detect possible way for
demonstrating this role of the news, by measuring the level of entropy implied by the
signal conveyed. The fundamental hypothesis is that a high level of entropy of the message
inside the news allows for a stable market, whereas the opposite for the case of financial
crashes. Therefore, aim of my research will be to develop possible algorithms to make a
computer able to classify the financial news as information with low or high entropy,
helping therefore the financial operator to identify the trend in the market.
*Speaker:
Luisella Balestra holds a degree in political science (Pavia University), PHD in economics
(University of Milan), Diploma in financial economics (London University), and was a
visiting research student in Harvard and LSE. She also will hold a Computer Science for
Artificial Intelligence Professional Certificate (C language and Python) from Harvard
(2020 - 2021) – edx.org.
She has been an associate researcher at HSG S.Gallen University (FIM/ARC) and visiting
researcher at the Center for the study of time, Faculty of Arts and Sciences with
Prof.Dean Rickles, Sydney University, Australia. She’s also a teacher in economics for
professional School in Ticino (Switzerland).
*Date:
Friday, December 18th, 2020, at 12:00
*Teams link:
https://supsi.zoom.us/j/92900752754
ID meeting: 929 0075 2754 (no password needed)