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SUMMARY:Quantitative Trading with Bayesian Methods - Dr Bluford Putnam\, C
 hicago Mercantile Exchange
DTSTART:20120328T100000Z
DTEND:20120328T110000Z
UID:TALK35417@talks.cam.ac.uk
CONTACT:Hugh Christensen
DESCRIPTION:Chief Economist of the CME Group (www.cmegroup.com).\n\nBayesi
 an inference methods are especially suited for analyzing financial markets
  for several reasons. Financial markets are all about forming expectations
 \, determining one’s confidence in those expectations\, then absorbing n
 ew information and revising one’s expectations and confidence. Bayesian 
 inference goes through the same process of forming an hypothesis and confi
 dence assessment\, receiving new information\, and revising that hypothesi
 s and related confidence. Moreover\, Bayesian methods are designed to acce
 pt expert information and to update its usefulness in a step by step manne
 r through time. And finally\, Bayesian methods are well-suited for forecas
 ting problems with time-varying parameters which are common in the analysi
 s of financial markets. For all of these reasons and more\, Bayesian metho
 ds are extremely appealing for use in forecasting financial market expecte
 d returns\, as well as estimating volatility and correlations between pair
 s of exposure returns.\n\nThe practical application of Bayesian methods to
  trading and investing in financial markets\, however\, has to integrate a
  Bayesian forecasting and confidence assessment process for market exposur
 es with a portfolio construction and risk control process. One approach is
  to use mean-variance optimization systems of the kind pioneered by Profes
 sor Harry Markowitz for use in developing portfolios for investment in fin
 ancial markets. The Bayesian forecasting focus on revising both projection
 s of returns and estimating the confidence (volatility) in those projectio
 ns fits neatly into the mean-variance framework which requires as inputs e
 xpectations for returns\, volatility\, and correlations\, as part of the p
 rocess of designing a portfolio to maximize returns relative to the volati
 lity of the expected return stream.\n\nThis presentation\, "Quantitative T
 rading with Bayesian Methods"\, explores the lessons learned from over 20 
 years of practical experience in using Bayesian methods in asset managemen
 t. The presentation will examine the investment challenges associated with
  using next-step ahead Bayesian time series analysis with daily\, weekly\,
  and monthly data for currencies\, fixed income securities\, and equity in
 dices\, with special attention to the problems of investing during and aft
 er the financial crisis of 2008. Careful attention is paid to the underlyi
 ng assumptions and whether they are sufficiently robust for use in ever-ch
 anging financial markets.\n\nMuch of the presenter’s research has been o
 f a proprietary nature. The types of Bayesian models being utilized in the
  presenter’s investment experience\, however\, were described in a seque
 nce of four articles in published in the Proceedings of the American Stati
 stical Association in 1994\, 1996\, 1997\, and 1998. Also\, the presenter 
 has written a number of business press articles related to the practical a
 pplication of quantitative techniques to investing. While the presenter is
  no longer directly involved with asset management\, two companies using B
 ayesian-based quantitative systems with several billions of dollars of ass
 ets use systems which descended intellectually (in part) from the work of 
 the presenter and his colleagues\, and they have established continuous in
 vestment track records since 1998.\n
LOCATION:Lecture Room 3B\, Inglis Building\, Department of Engineering\, T
 rumpington Street
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