Kelly Criterion and mean variance optimization

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I noticed that the Kelly Criterion resembles a ratio between the mean and variance in a continuous probability distribution. Now the mean and variance are important values in portfolio optimization (Modern Portfolio Theory). Is there some relationship between the two since both seek to maximize returns and minimize risk?

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Well, what rational person doesn't want to maximise returns and minimise risk?


The Kelly criterion is used in Intertemporal portfolio choice.

From Wiki:

The Kelly criterion for intertemporal portfolio choice states that, when asset return distributions are identical in all periods, a particular portfolio replicated each period will outperform all other portfolio sequences in the long run. Here the long run is an arbitrarily large number of time periods such that the distributions of observed outcomes for all assets match their ex ante probability distributions. The Kelly criterion gives rise to the same portfolio decisions as does the maximization of the expected value of the log utility function as described above.


'above' refers to:


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Also:


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So they seem to both involve maximising log returns


You mentioned Modern Portfolio Theory, which I'm guessing refers to Markowitz mean-variance, criticised by NNT. Markowitz doesn't seem to differentiate.


From Wiki:


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