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SUMMARY:Modelling commodity prices using novel data sources - Chris Longwo
 rth\, Cantab
DTSTART:20170314T160000Z
DTEND:20170314T170000Z
UID:TALK70053@talks.cam.ac.uk
CONTACT:CCA
DESCRIPTION:Traditional approaches to systematic investing have focused on
  long-term investing in stationary risk premia\, with models typically con
 structed as a function of price or long-term macro-economic data. However 
 many emerging data sources are starting to become available that can offer
  a much more detailed view of the world - such as high-frequency power con
 sumption\, shipping or weather forecast data. Unlike traditional financial
  inputs\, such as price or yield curve changes\, the nature of the relatio
 nship between this data and resulting price move is often idiosyncratic an
 d can vary significantly across markets. In addition\, for many of these m
 arkets traditionally stable market relationships are at risk of disruption
  from technological change - potentially making traditional modelling appr
 oaches unreliable. In this talk we'll describe recent work at Cantab that 
 attempts to apply machine learning techniques such as dynamic bayesian net
 works\, and neural networks to dynamically model relationships in this dat
 a and identify trading opportunities. Compared to existing approaches this
  can potentially yield more adaptable models and require fewer explicit as
 sumptions about the dynamics of individual markets.
LOCATION:MR14
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