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Portfolio Construction and Systematic Trading with Factor Entropy Pooling Meucci, Ardia, Colasante Presented by Marcello Colasante R/Finance 2014 2014/05/161.

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Presentation on theme: "Portfolio Construction and Systematic Trading with Factor Entropy Pooling Meucci, Ardia, Colasante Presented by Marcello Colasante R/Finance 2014 2014/05/161."— Presentation transcript:

1 Portfolio Construction and Systematic Trading with Factor Entropy Pooling Meucci, Ardia, Colasante Presented by Marcello Colasante R/Finance /05/161 STUDY IT: (white papers and code)www.symmys.com DO IT: Advanced Risk and Portfolio Management® Bootcamp

2 Factor Entropy Pooling: purpose What is the optimal investment strategy if we believe that, qualitatively, higher price on earnings imply higher returns, but we do not know precisely? 2014/05/162 Inequality views of Sharpe-ratios

3 Factor Entropy Pooling: purpose What is the set of expected returns and covariances that are consistent with CAPM equilibrium and thus can be used effectively as a starting point of mean variance optimization? 2014/05/163 Equality views consistent with equilibrium

4 Reference model Set of risk drivers represented by probability density function is the number of risk drivers Approach 1.Non-parametric 2.Parametric 2014/05/164

5 Entropy pooling Framework: 1.Prior distribution 2.Views 3.Posterior distribution Relative entropy (target function) 2014/05/165

6 Case study Normal assumption: 1.Prior distribution 2.Views on expectations and covariances 3.Posterior distribution (analytical solution) Reletive entropy (explicit form) 2014/05/166

7 Problem General views are not addressed by analytical solution Numerical approach is computationally expensive: 1.Large number of parameters 2.Constrained specification 2014/05/167

8 Solution Covariance matrix of low-rank-diagonal type Consistence with a systematic-idiosyncratic linear factor model uncorrelated 2014/05/168

9 Numerical approach with general views is possible: 1.Small number of parameters ( ) 2.Unconstrained specification 3.Analytical expression of the gradient and the Hessian of the entropy 4.The high-dimensional inverses that appear in the gradient and in the Hessian are obtained analytically by means the binomial inverse theorem 2014/05/169

10 Views on ranking We back-test a standard reversal strategy processing ranking (inequality) trading signals: Step 1. Momentum/reversal indicator Step 2. Reorder the stocks in such a way that Step 3. Lower ranking gives rise to a lower Sharpe ratio is a buffer that induces stronger inequalities. 2014/05/1610

11 Step 4. Standard approach Problem 1.Sharpe ratios never change through time 2.Volatilities are not updated Solution Step 4’. Compute the optimal parameters that satisfy the signal inequalities and are closest to the estimated covariances and expected returns 2014/05/1611

12 Cumulative P&L generated by the reversal strategy back-test for various parametrizations. The plot reports the median (solid line), the 50% percentile range (dim shading) and the 90% percentile range (dimmer shading). 2014/05/1612

13 Views on equilibrium Step 1. Target optimal portfolio Step 2. Equilibrium constraints Step 3. BL-equilibrium parameters Step 3’. Generalized FEP-equilibrium parameters 2014/05/1613

14 Historical means and covariances (blue) for various pairs of stocks versus respective implied expected returns and covariances: Black-Litterman (black) and Factor Entropy Pooling (red). 2014/05/1614


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