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SUMMARY:Machine Learning for Quantitative Finance: A collaboration between
  the Cambridge Machine Learning Group and Cambridge Capital Management - C
 reighton Heaukulani\, Matt Hoffman\, Zoubin Ghahramani\, and Andrew Baxter
 \, 
DTSTART:20150430T100000Z
DTEND:20150430T110000Z
UID:TALK59309@talks.cam.ac.uk
CONTACT:Zoubin Ghahramani
DESCRIPTION:We discuss some results from a collaboration between the Machi
 ne Learning Group and Cambridge Capital Management.  We’ll briefly outli
 ne our attempts at using common trading signals to model (and predict) the
  returns from a portfolio of assets with some (relatively) modern machine 
 learning techniques.  Our focus will be on identifying the important chara
 cteristics of a model for asset returns\, with a desire for rapid implemen
 tation on realistic amounts of data.  We’ll see how some off-the-shelf i
 mplementations of popular methods\, such as Gaussian processes\, random fo
 rests\, probabilistic linear models\, and neural networks\, perform on the
  prediction problem.  We will also briefly look at the multi-step predicti
 on problem and the use of these results for multi-step portfolio optimizat
 ion. Along the way we’ll learn about some difficulties when working with
  training data\, and why using machine learning in trading applications pr
 esents an interesting and difficult problem.\n\nThe talk will start with b
 rief introductions from Zoubin Ghahramani (Cambridge) and Andrew Baxter (C
 CM)\, followed by presentations by Creighton Heaukulani and Matt Hoffman.\
 n
LOCATION: Cambridge University Engineering Department\, Lecture Room 5
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