[2] https://blog.alphatheory.com/2013/01/kelly-criterion-in-prac...
[0] https://alphatheory.zendesk.com/hc/en-us/articles/3600356960... has an explanation of the “Alpha Theory” which I couldn’t quickly find on the alpha theory site.
It seems the choosing the optimal strategy for allocating a portfolio to maximise growth is often called “Kelly style”, “Kelly strategies”, “Kelly methods”, and also “Kelly criterion” by some people (which is why I was confused).
The details of an optimal strategy are completely different depending upon your assumptions (how reallocation is performed as new information is received, accounting for error in predicted outcomes, blah blah blah) so there cannot be a single definition for the Kelly Criterion for a portfolio, instead there are a variety of strategies (each with different assumptions and constraints).
For example the “many assets” model you refer to looks like it models a single market correlation (alpha), and not the multiple correlations within a real market.
Disclaimer: I am not an investment professional, but a small amount of software experience with hedge fund NAV calculations.
The post you replied to is right. The fundamental principle of Kelly is that you know your edge, in the markets that is mostly untrue. Funds will volatility-weight their portfolio but this isn't the same as Kelly in practice. Most fund managers will also weight their portfolio towards their "best" position but that is not necessarily based on return. Indeed, picking high return assets is only half the battle.
I also bet a lot, so I am familiar with Kelly. It is totally unusable in finance, no-one uses it in finance, and retail investors have an obsession with it.
In particular, if you Kelly-weight a value portfolio (which the firm linked to in your post is) then you are setting cash on fire. And if you Kelly-weight a long/short portfolio (again, the firm linked to appears to be doing this) then you are setting cash on fire. It is important to understand how a tool works at a practical level.
When I place a bet, I can estimate my edge because the outcome is binary. When the outcome is continuous, it is far more tricky. It is like saying a kid who learns to ride his trike is ready for MotoGP...they are just totally different.
And yes Soros put on big bets, but what you are missing with Soros is the fact that his hit rate was still 30%. Most of the stuff he did didn't work out, macro is largely bets on skewness not returns. Buffett had a higher hit rate but trying to suggest someone optimise a strategy based on what literally the best investor of all time did is...not smart. Even if you were better than Buffett, you might not be lucky.
The reason why Kelly doesn't work with value investing in particular is because your returns are largely random, you know that your portfolio has an edge but you don't usually know which position is going to revalue.
The reason why Kelly doesn't work with long-short in particular is because you aren't only betting on return but correlation. Anyone who runs Kelly will eventually get a correlation spike and blow up (this is also roughly true of macro, again why Soros isn't a good example, he largely bet on skewness).
I was a "pro" so I am also aware of what most pros do. Again, investors don't only look at return, they have to look at correlation, volatility (note that if you are betting on sports, you don't have to worry about things like correlation).
I agree that “the inputs to the Kelly formula are imprecise and therefore we should not mindlessly implement its recommendations.”
I agree that retail investors should not model their 401k allocations like Soros and Buffett.
Having run a factor neutral long short book I’m extremely familiar with the role of correlation and volatility in portfolio management and position sizing. As others have noted, there are extensions of Kelly (and related portfolio construction formulas) that account for correlations.
I disagree that risk-reward (broadly defined) shouldn’t be the primary bet sizing metric. I think many investors ignore risk reward calculations in their sizing and they would be better off if they paid attention to it. Many of the smartest investors I know have their entire sizing strategy based on risk reward.
To suggest that active investors should ignore risk reward / odds / whatever you want to call it, is wrong, in my opinion.
There needs to be a term for "This page you're reading is bogus horseshit theory, do not try to apply it practically".