Chicago math finance seminar

Practical experiences in financial markets using Bayesian forecasting systems.

Bluford H. Putnam, EQA Partners, L.P.

Going from theory to practice can be exciting when real money is on
the line. This presentation itemizes and discusses from a
theoretical and practical perspective a list of lessons learned from
20 years of investing using Bayesian statistical forecasting
techniques linked to mean-variance optimization systems for portfolio
construction. Several simulations will be provided to illustrate
some of the key points related to risk management, time decay of
factor data, and other lessons from practical experience. The
forecasting models focus on currencies, global government benchmark
bonds, major equity indices, and a few commodities. The models use
Bayesian inference (1) in the estimation of factor coefficients for
the estimation of future excess returns for securities and (2) in the
estimation of the forward-looking covariance matrix used in the
portfolio optimization process. Zellner's seeming unrelated
regressions is also used, as is Bayesian shrinkage. The mean-variance
methodology uses a slightly modified objective function to go beyond
the risk-return trade-off and also penalize transactions costs and
size-unbalanced portfolios. The portfolio optimization process is
not constrained except for the list of allowable securities in the
portfolio, given the objective function. ?This is a multi-model
approach, as experience has rejected the one-model "Holy Grail"
approach to building the one model for all seasons, so several
distinct and stylized models will be discussed.

link to presentation notes