  It is widely recognized that when classical optimal strategies are applied
with parameters estimated from data, the resulting portfolio weights are
remarkably volatile and unstable over time. The predominant explanation for
this is the difficulty of estimating expected returns accurately. In this
paper, we modify the $n$ stock Black--Scholes model by introducing a new
parametrization of the drift rates. We solve Markowitz' continuous time
portfolio problem in this framework. The optimal portfolio weights correspond
to keeping $1/n$ of the wealth invested in stocks in each of the $n$ Brownian
motions. The strategy is applied out-of-sample to a large data set. The
portfolio weights are stable over time and obtain a significantly higher Sharpe
ratio than the classical $1/n$ strategy.
