Can You Run a Monte Carlo Simulation in Excel? The Honest Answer

Yes — you can build a genuine Monte Carlo simulation in Excel with nothing more than the `RAND()` function and a Data Table. Financial planners have done exactly this since the 1990s, long before dedicated software existed. So the short answer to "can I do this myself" is yes.

The longer answer is that building one is the easy part. Building one that tells you the truth about *your* goal, in *Indian markets*, without you accidentally lying to yourself with bad assumptions — that's where almost every DIY attempt quietly falls apart.

This article covers all three things people usually ask next: how to build it in Excel, whether ChatGPT can do the heavy lifting, and which option is actually worth your time. If you're newer to the concept itself, our earlier piece The One Number Your Financial Plan Gets Wrong explains what Monte Carlo simulation is and why it matters.

How to build a Monte Carlo simulation in Excel

Here's the bare-bones version — the same one behind most spreadsheet templates you'll find online.

1. Pick your uncertain input. Usually this is your annual investment return. Instead of assuming a fixed 12%, you treat it as a random variable.
2. Generate a random return each year. The standard formula is `=NORM.INV(RAND(), mean, standard_deviation)` — it pulls a random number from a normal distribution centered on your assumed average return, with realistic spread around it.
3. Chain the years together. Apply that random return to your corpus year over year, factoring in your SIP contributions, to get one possible ending value.
4. Run it thousands of times. One pass through the spreadsheet is one possible future. To get a distribution, you repeat this — usually with Excel's Data Table feature (What-If Analysis → Data Table) or a short macro — 1,000 to 10,000 times.
5. Read the distribution, not a single number. Once you have thousands of ending values, you can ask the question that actually matters: in what percentage of scenarios did you hit your goal? That percentage is your probability of success.

If you're comfortable with Excel, this is a genuinely useful weekend project — it will teach you more about why point-estimate calculators are misleading than any article will.

Where the DIY Excel model quietly breaks down

The mechanics above are correct. The problem is almost never the formula — it's the assumptions feeding it. For Indian investors specifically, three gaps show up in nearly every homemade version:

• The return and volatility numbers are usually borrowed from US markets. Most Monte Carlo templates online — including well-regarded ones — are built around S&P 500 historical statistics. Sensex and Nifty have a different volatility profile, different correlation to global markets, and a different relationship with Indian inflation and RBI rate cycles. Plugging in US-market assumptions and calling it an Indian financial plan produces a confident-looking number that's quietly wrong.
• Multi-asset portfolios need a correlation model, not three separate simulations. The moment you hold equity, debt, and gold together, their returns aren't independent — gold tends to move differently from equity during Indian market stress. A proper simulation needs a covariance matrix linking these assets. A single `RAND()` column per asset, run separately, misses this entirely and tends to overstate diversification benefit.
• Tax and sequence-of-returns risk are easy to leave out — and they change the answer. LTCG on equity, expense ratios, and what happens if a market downturn hits in the exact years you planned to start withdrawing — these details separate a real simulation from a rough sketch. Most spreadsheet templates skip them or bury them behind a single flat 'expected tax' line.

Can ChatGPT run a Monte Carlo simulation for you?

Also yes, with real caveats. ChatGPT (and similar AI tools) can write the Python or Excel-formula code to run a Monte Carlo simulation, explain the statistics as it goes, and even generate a chart of the results. For a simple version — model my SIP against a distribution of returns — it holds up reasonably well.

Where it gets shakier is the same place the DIY Excel model does: the assumptions. Ask ChatGPT for "realistic Indian equity return and volatility assumptions" and it will confidently give you numbers — but it isn't pulling from a verified, current dataset unless you supply one yourself. It also has no persistent memory of your actual goal, portfolio, or plan from one session to the next.

A side-by-side review of AI tools for retirement planning found that neither ChatGPT nor Claude ran an actual Monte Carlo simulation by default — both defaulted to flat return assumptions unless explicitly pushed to do otherwise. In short: ChatGPT is a decent *coding assistant* for building your own simulation. It is not, on its own, a *financial planning tool*.

So which software is actually best?

It depends what you're optimising for.

That last row is the whole reason Aurelian Capital's simulation engine exists. It runs the same underlying Monte Carlo mechanics described above — but against Indian-specific return and volatility distributions for equity, debt, and gold, linked through a correlation matrix rather than treated as independent, with your actual SIP, goal amount, and timeline plugged in. You don't touch a spreadsheet or write a prompt; you get a probability of success and a set of levers to move it.

If you'd rather see the number than build the spreadsheet, our FIRE calculator and SIP calculator are the fastest way in — both free, no sign-up needed to try.