Classical optimal strategies are notorious for producing remarkably volatile portfolio weights over time when applied with parameters estimated from data. This is predominantly explained by the difficulty to estimate expected returns accurately. In Lindberg (Bernoulli 15:464–474, <CitationRef CitationID="CR10">2009</CitationRef>), a new parameterization of the drift rates was proposed with the aim to circumventing this difficulty, and a continuous time mean–variance optimal portfolio problem was solved. This approach was further developed in Alp and Korn (Decis Econ Finance 34:21–40, <CitationRef CitationID="CR1">2011a</CitationRef>) to a jump-diffusion setting. In the present paper, we solve a different portfolio problem under the market parameterization in Lindberg (Bernoulli 15:464–474, <CitationRef CitationID="CR10">2009</CitationRef>). Here, the admissible investment strategies are given as the amounts of money to be held in each stock and are allowed to be adapted stochastic processes. In the references above, the admissible strategies are the deterministic and bounded fractions of the total wealth. The optimal strategy we derive is not the same as in Lindberg (Bernoulli 15:464–474, <CitationRef CitationID="CR10">2009</CitationRef>), but it can still be viewed as investing equally in each of the n Brownian motions in the model. As a consequence of the problem assumptions, the optimal final wealth can become non-negative. The present portfolio problem is solved also in Alp and Korn (Submitted, <CitationRef CitationID="CR2">2011b</CitationRef>), using the L <Superscript>2</Superscript>-projection approach of Schweizer (Ann Probab 22:1536–1575, <CitationRef CitationID="CR15">1995</CitationRef>). However, our method of proof is direct and much easier accessible. Copyright Springer-Verlag 2013
