Replicating Evans & Archer (1968)
How many stocks does it actually take to diversify? A simulation study.
A project led under Dr. Daniel Rodriguez through the FinTech Club, replicating a classic finance result from first principles.
The original claim
Evans and Archer (1968) asked a deceptively simple question: as you add randomly-chosen stocks to a portfolio, how fast does risk fall? The answer shapes how every retail investor should think about their holdings.
What we did
Built a Monte Carlo simulation in Python (Pandas, NumPy, SciPy) over historical price data: repeatedly draw random portfolios of size $n$, compute the standard deviation of returns, and trace how that standard deviation behaves as $n$ grows.
The result
Risk falls steeply at first and then flattens hard. Most unsystematic risk is gone by 10–20 assets — and past that, adding names buys you very little. The remaining variance is systematic: market risk you cannot diversify away, only choose to bear.
Why replicate something already known
Because replicating a result you expect is the cheapest possible way to learn whether your methodology is sound. If the simulation hadn't reproduced the 1968 curve, the bug would have been mine — and I'd rather discover that on a known answer than on an unknown one.
It's the same instinct that shows up in my execution research: build the control that could prove you wrong, and run it first.