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Everyone’s trading politics, memes etc. on Polymarket. Meanwhile, there’s a whole category of markets that 99% of traders ignore:

Stock price markets.

“What will Microsoft (MSFT) close at in 2025?” “What will Tesla (TSLA) close at by March 31?” “What will NVIDIA close at by end of Q1?”

These markets let you bet on whether a stock will be above or below a certain price by a specific date.

Sound familiar?

If you’ve ever traded options — it should. Because these are literally binary options.

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A Polymarket stock market works like this:

Each contract pays $1 if correct, $0 if wrong.

That 34¢ price? It’s the market’s implied probability — 34% chance MSFT closes above $420.

Now here’s the thing. There’s a formula that’s been calculating exactly these probabilities for 40+ years.

It’s called Black-Scholes.

Before I show my strategy, let me explain WHY it works. Not bro-science — actual math.

Black-Scholes in 60 seconds

The model was published in 1973 by Fischer Black, Myron Scholes, and Robert Merton. It won a Nobel Prize in Economics (1997). Every options desk on Wall Street uses it as baseline.

At its core, Black-Scholes answers one question:

“What is the probability that a stock will be above price K at time T?”

That’s literally what Polymarket stock markets are asking.

The model assumes stock prices follow a Geometric Brownian Motion:

dS = μS dt + σS dW

Where:

Translation: stock price changes = a drift component (trend) + a random component (volatility).

In the risk-neutral framework, the premium of a binary option is interpreted as the expected value — the probability of being in-the-money, discounted to present value. This is exactly what a Polymarket YES share represents.

This isn’t new — academia confirms it

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In October 2025, researchers published “Toward Black-Scholes for Prediction Markets: A Unified Kernel and Market Maker’s Handbook” (arXiv: 2510.15205). The paper explicitly addresses the gap I’m exploiting:

Prediction markets aggregate dispersed information into tradable probabilities, but they still lack a unifying stochastic kernel comparable to what options gained from Black-Scholes.

The paper proposes a logit jump-diffusion model that treats the traded probability as a martingale — in plain English, they’re building the exact same mathematical bridge between tradfi options and prediction markets that I’ve been using intuitively.

Separately, research on “Modeling Volatility in Prediction Markets” (NYU) showed that if a prediction market is efficient, contract prices follow a diffusion process — the same type of stochastic process underlying Black-Scholes.

What about crypto?

GBM has been tested on Bitcoin. Research from 2020–2024 found that the model provides moderate predictive accuracy with a mean absolute error of 10% and captures the general trend, though it underestimates extreme moves.

Here’s what’s interesting: one study found that GBM produces some of the most accurate directional change predictionsfor crypto with a 63.6% experimental probability — which was unexpected because crypto is more volatile than stocks.

Why? Because crypto’s fundamental IS price action. There’s no earnings, no margins, no P/E ratio. GBM is built on price dynamics. For crypto, that’s not a limitation — it’s a feature.

Two important disclaimers

Here’s what I actually did:

Step 1: Pick a Polymarket stock market (e.g., MSFT closing price by end of month)

Step 2: Go to a tradfi options chain for the same stock (CBOE, Yahoo Finance, or any broker). Find the implied volatility (IV) for options expiring on the same date.

Step 3: Plug into Black-Scholes:

Step 4: Calculate N(d2) — this gives you the probability of the stock being above K at expiration.

Step 5: Compare your calculated probability with the Polymarket price.

If the difference is 5–7%+ → that’s your trade.

Everything below 5–7% I consider noise. Transaction costs, spreads, and model uncertainty eat it up.

Let’s say MSFT is trading at $415. Polymarket has:

Plugging into Black-Scholes, I get: probability = 41%

Polymarket says 34%. My model says 41%. That’s a 7% gap — above my threshold.

I buy YES at 34¢.

If I’m right, I make $0.66 per share profit (buy at 34¢, receive $1). That’s a 194% return.

If I’m wrong, I lose 34¢.

But the key insight: I don’t need to be right every time. I just need the expected value to be positive. If my model is accurate, buying at 34¢ when fair value is 41¢ is +EV over many trades.

Sometimes Polymarket is perfectly priced.

I saw this once with MSFT — every single strike had probabilities that matched Black-Scholes almost exactly. The distribution was textbook perfect.

When that happens, there’s zero edge. It’s pure gambling. I close my laptop and walk away.

Let me be honest about the limitations:

Liquidity. Polymarket stock markets are thin compared to politics markets. You might not be able to size up even if you find edge.

Model assumptions. Black-Scholes assumes constant volatility and log-normal distribution. Reality is messier — fat tails, volatility clustering, earnings events.

I’m simplifying. There are things I’m glossing over — the Greeks, early resolution risk, funding costs. But for a baseline framework, it works.

To understand why this works on Polymarket, you need to understand how it works on Wall Street.

Every time you see an options chain on your broker — those prices are derived from Black-Scholes (or its extensions like the Binomial model). Market makers don’t sit there guessing. They plug in volatility, time, and strike → the model spits out fair value → they quote around it.

The entire $12+ trillion options market is built on this infrastructure.

When Citadel, Jane Street, or Susquehanna quotes an option, they’re calculating the exact same N(d2) probability I’m comparing against Polymarket. The difference? They trade against other professionals with the same models. On Polymarket, you’re trading against retail degens who price by gut feeling.

That’s the edge. Not the formula itself — but the fact that Polymarket participants don’t use it.

The tradfi options → Polymarket arbitrage

Here’s the key insight that makes this more than just theory:

Tradfi options and Polymarket stock markets are pricing THE SAME EVENT. “Will MSFT be above $420 on March 31?” — the CBOE has options for this. Polymarket has a market for this. They should converge to the same probability.

But they don’t always converge. Because:

When the gap is 5–7%+ → that’s free money left on the table.

I mentioned that Black-Scholes assumes Geometric Brownian Motion. For stocks, this is an approximation — earnings reports, M&A, dividends all create non-GBM behavior.

But for crypto? GBM is arguably a better fit.

Research on Bitcoin prices (2020–2024) showed that GBM achieves 63.6% directional accuracy — higher than expected for such a volatile asset. The reason: crypto has no fundamental anchors that break the stochastic model. No quarterly earnings surprises. No dividend ex-dates. Just pure price dynamics — which is exactly what GBM models.

If Polymarket scales crypto price markets with decent liquidity, the same Black-Scholes framework could work even better than it does for stocks. The model’s assumptions are actually MORE valid for crypto, not less.

At expiration, every option becomes binary. You get $0 or $1.

Polymarket stock markets ARE binary options.

The entire options industry has been pricing these for 40+ years using Black-Scholes.

If you can calculate fair value better than the average Polymarket trader — you have edge.

The formula is public. The data is public. The markets are open 24/7.

You just have to do the math.

For those who want to go deeper:

Disclaimer: This is not financial advice. I’m sharing my approach for educational purposes. Do your own research. Prediction markets carry risk of total loss.