  We maximize the expected utility from terminal wealth for an HARA investor
when the market price of risk is an unobservable random variable. We compute
the optimal portfolio explicitly and explore the effects of learning by
comparing it with the corresponding myopic policy. In particular, we show that,
for a market price of risk constant in sign, the ratio between the portfolio
under partial observation and its myopic counterpart increases with respect to
risk tolerance. As a consequence, the absolute value of the partial observation
case is larger (smaller) than the myopic one if the investor is more (less)
risk tolerant than the logarithmic investor. Moreover, our explicit
computations enable to study in details the so called hedging demand induced by
learning about market price of risk.
