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SUMMARY:Deep Learning for Asset Bubbles Detection
DTSTART:20200312T120000
DTEND:20200312T130000
DTSTAMP:20260501T144258Z
UID:6caed56f20f242a998d3bcf3e9a29cc2e364951440a7b5df5161a8a6
CATEGORIES:Conferences - Seminars
DESCRIPTION:Alexis MARCHAL\, SFI@EPFL\, PhD student\nWe develop a methodol
 ogy for detecting asset bubbles using a neural network. We rely on the the
 ory of local martingales in continuous-time and use a deep network to esti
 mate the diffusion coefficient of the price process more accurately than t
 he current estimator\, obtaining an improved detection of bubbles. We show
  the outperformance of our algorithm over the existing statistical method 
 in a laboratory created with simulated data. We then apply the network cla
 ssification to real data and build a zero net exposure trading strategy th
 at exploits the risky arbitrage emanating from the presence of bubbles in 
 the US equity market from 2006 to 2008. The profitability of the strategy 
 provides an estimation of the economical magnitude of bubbles as well as s
 upport for the theoretical assumptions relied on.
LOCATION:UNIL\, Extranef\, room 118
STATUS:CONFIRMED
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