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By Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah

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By Greg N. Gregoriou, Vassilios Karavas, François-Serge Lhabitant, Fabrice Douglas Rouah

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Only funds/CTAs with at least three observations are included. The model is estimated using feasible generalized least squares. The null hypothesis considered is that all funds have the same mean returns, provided that adjustments have been made for changes in overall returns and differences in leverage. This is equivalent to testing the null hypothesis H0: ai = a᎑ where a᎑ is an unknown constant. 2 consistently show that some funds and pools have different mean returns than others. This finding does contrast with previous research, but is not really surprising given that funds and pools have different costs.

5, 2; s = 2. 5; s = 5, 10, 15, 20. 5, 2; s = 5, 10, 15, 20. 5, 2; s = 5, 10, 15, 20. aData bData differences are found in CTA data. But they show little ability to find persistence with the small differences in performance in the public fund data used by EGR. The test of two means has even less ability to detect persistence. 5 does show that EGR’s method can find performance persistence that is strong enough. HISTORICAL PERFORMANCE AS AN INDICATOR OF LATER RETURNS Results based on methods similar to those of EGR are now provided.

3 LEHMAN GLB. 4 sures. To come up with a limited set of risk factors, we selected 16 factors known to be related to the strategies implemented by managed futures, namely stocks, bonds, interest rates, currency, and commodities factors. We then used stepwise regression with the backward entry procedure to avoid any multicollinearity problems and keep a sufficient number of degrees of freedom. S. S. dollar [USD] versus major currency, USD versus Japanese yen, and Goldman Sachs Commodity Index [GSCI], the corresponding exposures turn out to be very different.

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