Decent quants know about the shortcomings of Markowitz allocation. The mean is more or less impossible to measure, and covariances shift over time and are awkward to measure concurrently. So the art is in specifying the parameters. I've used zero mean and block-diagonal covariance in asset allocation before, which isn't far off 1/N allocation.
The issue with Kelly betting is the same as with Markowitz: knowing the parameters of the problem. If you overstate your probability of win by mis-estimating, you'll get ruined. For what it's worth, the version of Kelly betting I'm familiar with is for Bernoulli trials. I think the Gaussian version of the Kelly criterion essentially reduces to Markowitz.
Side note: kelly betting isn't new! Daniel Bernoulli knew about it back in the 1700s. Kelly just connected the formula to ideas in information theory.
This is what I thought when I first ran across the theory in a book. I feel like it's a widespread pattern, there is some big intractable problem, and some genius says "aha, I can reduce this to..." but what they have reduced it to is also an intractable problem, only because of the novelty it feels like progress.
Everybody goes around saying "ha, you can't predict future returns from prior returns, aren't we smart", but how can you predict future covariances from prior covariances any better?
I'm not sure how useful Kelly betting is though, for similar reasons. Isn't it premised on knowing your edge, so knowing how much to bet is not such a valuable secret; you need to figure out how to identify your edge reliably. Particularly that it's not negative.
I eventually decided the way to go is (1) start with every idea or security equal weighted, and (2) basically never rebalance, because things that outperform do tend to continue to do so and the "imbalance" reflects new knowledge disseminated by the market.
The issue with Kelly betting is the same as with Markowitz: knowing the parameters of the problem. If you overstate your probability of win by mis-estimating, you'll get ruined. For what it's worth, the version of Kelly betting I'm familiar with is for Bernoulli trials. I think the Gaussian version of the Kelly criterion essentially reduces to Markowitz.
Side note: kelly betting isn't new! Daniel Bernoulli knew about it back in the 1700s. Kelly just connected the formula to ideas in information theory.