Daily, weekly and monthly returns give per-day variance estimates of 1.0000, 1.3225 and 1.4000, and the reflex is to average them into 1.2408, a figure no horizon produced. The variance ratio is a weighted sum of autocorrelations, so a forty percent overshoot at twenty periods measures dependence rather than noise, and the coefficient that reproduces it is 0.17554. With twenty years of daily data that ratio sits 4.6 standard errors above one and with five years only 2.3, which is why the number means nothing without the sample size attached.
Both seats in the marble game average a dollar a play, and that arithmetic stays true to the last line. Seat A carries variance 3/2 against seat B's 1, and seat A's law turns out to be seat B's law with one prize smeared outward, so every concave utility prefers B without variance ever being mentioned. Once both players stop flipping coins, seat B is ahead on the average too, at 1 against 3/4.
The standard deviation of a sum is not the sum of the standard deviations, so quadrupling the horizon only doubles the risk. The article carries the general square-root law, the ratio that diagnoses the mistake, and the controls showing a bell curve does none of the work: a two-point yearly return lands on 0.20026 and a uniform one on 0.20008. It also carries what actually breaks the rule, which is dependence rather than fat tails.