A recent experiment was conducted involving forty people with Doctorate Degrees. Their Ph.D.s were not in mathematics, but all were in traditional areas of academic endeavor. The doctorates were given a computer game to trade. They started with $10,000 and were given 100 trials playing a game in which they would win 60% of the time. When they won, they won the amount of money they risked in that trial. When they lost, they lost the amount of money they risked in that trial.
How many American Ph.D.s made money at the end of the experiment? Two. The other 38 lost money. 95% of these very academically smart people lost money playing a game in which the odds of winning were better than any odds in Las Vegas. Why did they lose? Poor Strategy Rules?
They lost because of poor money management skills. For example, if you start out risking $1,000 and lose 4 times in a row, you are now down to $6000. You might be thinking, I am due for a win now. But that’s nonsense since your chances of winning are and will always be 60%!
Even though your chances of winning are still 60%, let’s say you decide to double your bet size since you are due now. You lose again and you’re down 60% now. If you keep thinking you?re due for a win you will soon be broke with any potential recovery vanished.
Good money management activities are fundamental to avoiding disasters like this one.
Money Management Quotes
You may enjoy some of the following quotes:
“Balancing your money is the key to having enough.” — Elizabeth Warren
“When you work on something that only has the capacity to make you 5 dollars, it does not matter how much harder you work — the most you will make is 5 dollars.” — Idowu Koyenikan
“The strength of your personal financial resources is equivalent to the quality of your financial decision making.” — Wayne Chirisa
Why Smart People Fail at Money Management
The experiment result is striking precisely because of who the participants are. These are not uninformed retail investors or casino tourists. They are people with doctoral degrees, trained to think rigorously and systematically in their respective fields. Yet 95% of them lost money in a game with better odds than any casino table. The intelligence that earned the doctorate is not the intelligence required to manage money correctly. They are different skills entirely.
The failure mode described in the experiment is the gambler’s fallacy applied to position sizing. After four consecutive losses, the intuition is that a win is overdue. It is not. Each trial is independent. The probability remains 60% regardless of the recent sequence. Doubling the bet after losses is the classic Martingale approach, the same strategy dissected on the scale trading page. It feels logical because it seems to make the recovery happen faster. It actually concentrates risk at the worst possible time, when the account has already been depleted by the losing streak, and can destroy the account on a single subsequent loss.
The correct position sizing for this game is calculable using the Kelly formula. With a 60% win rate and a 1:1 payoff ratio, the Kelly percentage is: 0.60 minus (0.40 divided by 1) equals 0.20, or 20% of current equity per trade. Applied consistently to a starting account of $10,000 across 100 trials with the expected 60/40 win/loss distribution, 20% Kelly sizing produces account growth rather than depletion. Overbetting relative to Kelly, even when the direction of the bet is correct on 60% of occasions, produces worse outcomes than the Kelly-sized approach and can produce ruin despite the positive edge.
The experiment exposes the gap between analytical intelligence and financial intelligence. The Ph.D. holders could solve complex problems in their fields. They could not intuitively maintain consistent, mathematically correct position sizing under the psychological pressure of a real-time loss sequence. This is not a failure of intelligence. It is a failure of the specific training that money management requires. Systematic trend following addresses this by building position sizing into the rules rather than leaving it to real-time judgment. The rules specify what percentage of equity to risk per trade. The calculation is made when the mind is clear. The execution happens according to the rule.
Frequently Asked Questions
Why did 95% of the Ph.D.s lose money in a positive-expectancy game?
Because having a positive-expectancy system is necessary but not sufficient for profitable trading. The sizing of each bet determines whether the edge is captured or destroyed. Increasing bet size after losses, which feels intuitive because a win seems overdue, concentrates risk when the account is most depleted. Correct position sizing, which means a consistent percentage of current equity per trade regardless of recent results, captures the positive edge over a sufficient number of trials. The Ph.D.s had the edge but not the sizing discipline to extract it.
What is the gambler’s fallacy and how did it affect the experiment participants?
The gambler’s fallacy is the belief that after a streak of one outcome, the other outcome becomes more likely. After four consecutive losses in a 60% win game, the fallacy says a win is overdue. In reality, each trial is independent and the probability remains 60% regardless of recent history. The fallacy led participants to increase bet sizes after losses, compounding the damage done by losing streaks rather than limiting it.
What is the correct approach to position sizing in a 60% win rate game?
The Kelly formula calculates the correct bet fraction as win probability minus (loss probability divided by the payoff ratio). For a 60% win rate with a 1:1 payoff ratio: 0.60 minus 0.40 equals 0.20, or 20% of current equity per trial. Applied consistently across a large number of trials, this produces positive compound growth. Betting more than the Kelly fraction increases risk faster than return and can produce ruin despite the positive edge.
Trend Following Systems
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