New Segment! Monte Carlo Mondays & First Up Is $NU

This is part of a new segment I’m calling Monte Carlo Mondays — where I model out a company’s next 12 months using probability, financial fundamentals, and management’s own guidance.

This week, we’re looking at Nubank ($NU), one of Latin America’s more explosive fintech stories.

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Step 1: What I Used

To figure out where $NU might be 12 months from now, I built a Monte Carlo simulation using three inputs that I think matter most:

1. Earnings Per Share (EPS) Nubank is on pace to generate about $0.40 in earnings per share in 2025, based on current estimates. I took that as my base. It reflects margin expansion, profitable scaling in Brazil, and steady user growth across their three core markets.

2. Earnings Growth Rate Management has consistently guided for long-term earnings growth between 20–30% annually. I used 25% as the average annual growth rate and added a standard deviation of 10% to reflect uncertainty — things like macro pressure in Brazil, currency volatility, or just operational execution.

3. Valuation (P/E Ratio) Nubank has traded between 32–38x earnings recently. I used a more grounded 30x forward P/E, which I think fairly balances growth potential with some margin for sentiment shifts.

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Step 2: How I Modeled It

Here’s what the simulation actually does:

• I ran 5,000 scenarios.

• Each simulation randomly selects an EPS growth rate around the 25% mean.

• It grows the $0.40 base EPS by that rate over 12 months.

• Then it applies the 30x P/E multiple to that new EPS to generate a simulated future share price.

That gives us a range of possible outcomes based on execution and market valuation.

What makes this approach valuable is that it reflects not just the upside, but the probability-weighted range of outcomes. Instead of anchoring on a single price target, I get a fuller view of what might actually happen. This also builds in randomness — the same kind of uncertainty that drives real markets. Whether sentiment improves, earnings beat expectations, or growth slightly lags, this model accounts for it.

I also layered in the macro backdrop. The volatility we’re seeing in the S&P, potential trade war headlines, the general sentiment around interest rates and tariffs — all of that is indirectly reflected in the range of outcomes. You can’t time macro, but you can acknowledge its weight. That’s why the growth rate in this simulation isn’t static — it reflects the global mess we’re all investing through. It gives a much more realistic picture of risk-adjusted returns.

Here’s the visual output from the simulation — the distribution of possible 12-month outcomes based on 5,000 scenarios:

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Step 3: Results

Here’s what the simulation showed: