Key insights
- An equity research analyst developed a free Google Sheets Monte Carlo simulation tool for DCF analysis. This tool aims to provide a distribution of potential outcomes rather than a single point estimate, aiding in more informed position sizing. The analyst seeks feedback on handling correlated inputs, distribution preferences, and output format usability for position sizing.
Hey all. As you know by now, I run an equity research Substack and I use Monte Carlo on every thesis I publish. I'm not interested in fair values accurate to the cent, I want a distribution of possible outcomes that helps me size positions honestly.
I'd been doing all my Monte Carlo work in Excel. When I migrated to Sheets I went looking for a simulator add-on. Couldn't find a good free one. The paid options had real limitations. So I built my own and it got approved on the Google Workspace Marketplace this week.
What it does: highlight any cell in your model (WACC, growth, terminal multiple, margin, capex, whatever), tell the tool what distribution it should draw from, pick your output cell, run thousands of simulations. Get back the full distribution from 10th/90th percentile, P(intrinsic > price), histogram, confidence band.
Why this matters for sizing: my point-estimate DCF on a recent name said 25% upside, 5% position size. The Monte Carlo on the same model said P(intrinsic > price today) was 58%. A 58% probability of being right is not a 5% position. It's much smaller.
It's free.
What I'd love feedback on:
- How you handle correlated inputs in your models (WACC and growth aren't really independent) * Which distributions you reach for most often for which cells * Whether the output formats are useful for actual position-sizing decisions, or whether you'd want something different