Huh. So I wrote the code, and ran the simulation. Now I get it. Investors: 100,000 Iterations: 100 Average worth after 100 iterations: $83.923 Average net worth increases. However the distribution of wealth is skewed dramatically. Winners: 13,704 (net worth of more than $1 at the end) Investors worth What That Guy was worth (the investor who made the most money): $1,171,830.00 He flipped 71 heads and 29 tails. Median…
And to put the phenomena in much simpler words: flipping a coin once is good, having $100 bucks you either lose $40 or get $60, on average you get $10, and you win half of the time. The problem is if you have to flip a coin at least twice, because then the accumulated multiplier of your value, rather than .6 / 1.5 becomes:
.6 / .9 / .9 / 2.25
So while on average you gain 16.25% of value, you are a winner only ¼ of the time, and it gets better/worse depending on average gain / chance to gain.
…And apparently, what I learned just now, since your chance to gain approaches 0, for a finite population and infinite time, the chance approaches 0, therefore rendering the average gain to also be 0.