A Jackknife-Type Estimator for Portfolio Revision

This paper proposes a novel approach to portfolio revision. The current literature
on portfolio optimization uses a somewhat naïve approach, where portfolio weights are always completely revised after a predefined fixed period. However, one shortcoming of this procedure is that it ignores parameter uncertainty in the estimated portfolio weights, as well as the biasedness of the in-sample portfolio mean and variance as estimates of the expected portfolio return and out-of-sample variance. To rectify this problem, we propose a Jackknife procedure to determine the optimal revision intensity, i.e. the percent of wealth that should be shifted to the new, in-sample optimal portfolio. We find that our approach leads to highly stable portfolio
allocations over time, and can significantly reduce the turnover of several well
established portfolio strategies. Moreover, the observed turnover reductions lead
to statistically and economically significant performance gains in the presence of
transaction costs.