expected return - Intuitive explanation of stochastic portfolio theory

Fernholz and Karatzas have published various papers about so called stochastic portfolio theory. Basically they say that the return to be expected from a portfolio on the long run is rather the growth rate

$$\gamma = \mu - \frac12 \sigma^2$$ than $\mu$, where $\mu$ is the drift coefficient of the price process $S_t$ which solves the following SDE: $$dS_t = \mu S_t dt + \sigma S_t dB_t.$$

One can argue with Ito's lemma, with the geometric mean of a lognormal random variable and similar - but what is the intuition behind this?

As references see Stochastic Portfolio Theory and Stock Market Equilibrium by Fernholz and Shay for the first paper on this and Does a Low Volatility Portfolio Need a “Low Volatility Anomaly?” by Meidan as a more recent reference.

If I am not mistaken then the above SDE would look like this

$$dS_t = (\mu-\sigma^2/2) S_t dt + \sigma S_t \circ dB_t$$ in Stratonovich form and one sees the "correct" growth rate... which is another link. But what is the big picture of all this?

Answer

This will depend on the definition of "return on the long run". If we define the annualized return on the long run by $\frac{1}{T}\ln \frac{S_T}{S_0}$ for a certain time $T$ in the future, then

$$\begin{align*} E\left( \frac{1}{T}\ln \frac{S_T}{S_0} \right) = \mu-\frac{1}{2}\sigma^2, \end{align*}$$ as claimed. Note that $\mu$ is the instant, or instantaneous, return.