Overconfidence in the Options Market: Lessons from Chen & Sabherwal

Overconfidence in the Options Market: Lessons from Chen & Sabherwal

In the trading world, most so-called “research” is little more than a blog post with a chart, a backtest on five years of cherry-picked data, and a hunch dressed up as certainty. You’ve seen it, someone finds a pattern, proclaims it a discovery, and ignores the 50 times it didn’t work.

This paper wasn’t that.

Chen and Sabherwal didn’t start with a pet theory and hunt for data to prove it. They built a dataset covering 16 full years of market history, January 1996 through December 2011, spanning multiple market cycles: the dot-com bubble, the 2008 financial crisis, and the post-crisis recovery. They included 2,779 unique NYSE/AMEX-listed companies and millions of option contracts. That’s not just “big data” for bragging rights; it’s the kind of sample that forces your theory to survive both bull and bear markets, calm periods and volatility spikes, retail booms and institutional dominance.

They tested the question from every angle:

• Time series – Does overconfidence show up in the same stocks over time?

• Cross-sectional – Does it show up in different stocks at the same time?

• Institutional vs. retail – Is the pattern stronger when retail is more involved?

• Volatility metrics – How do spreads, skew, and smirk shift when turnover jumps?

And here’s the rare thing they found: hard evidence that many option price moves are not driven by superior information, what Wall Street likes to call “smart money”, but by human overconfidence. Traders aren’t always seeing something the rest of us don’t. They’re often just betting bigger because they think they’re right.

For you, as a trader, that’s not just an academic point, it’s a map to where mispricings happen. If price is being pushed by confidence instead of probability, the odds tilt in your favor when you take the other side.

Why Overconfidence Creates a Trading Edge

Overconfidence isn’t just a personality quirk, it’s a pricing problem. And in the options market, pricing problems are opportunities.

When traders are convinced they’re right, they behave in ways that push certain contracts out of balance with reality:

• They overpay for low-probability bets. A trader who “knows” a stock will go to $120 buys the $120 call at a premium that assumes a higher probability of success than history would justify.

• They underpay for protection. If they’re sure the market won’t drop, they ignore puts, letting their prices fall relative to calls.

• They crowd into the same strikes and expirations. Liquidity and attention cluster in one area of the chain, creating lopsided implied volatility (IV) patterns.

Here’s why that matters: options are priced based on probability and volatility. When overconfidence distorts demand, it distorts IV. And when IV is wrong, the option is mispriced.

Example of the Mispricing Mechanism

1. Step 1: Stock Rally – ABC rallies 12% in two weeks.

2. Step 2: Call Frenzy – Traders rush into the $110 OTM calls. Demand pushes their IV from 22% to 29%.

3. Step 3: Skew Shift – OTM calls are now disproportionately expensive compared to ATM calls, while puts flatten out.

4. Step 4: Probability Gap – The $110 calls are priced as if there’s a 25% chance the stock gets there by expiration, but the actual historical probability is closer to 12%.

5. Step 5: The Opportunity – Selling a call spread here lets you collect premium that reflects their belief, not the real odds.

Why the Edge is Sustainable

Markets are efficient when traders are rational. But overconfidence is systematic and repeatable, it shows up in every market cycle, every asset class, and, according to Chen & Sabherwal’s data, across both time and cross-section.
That means it’s not a one-off anomaly. It’s a structural bias.

Even better:

• Overconfidence often builds gradually (chasing winners) but unwinds quickly (sharp reversals or sideways drift).

• Time decay (theta) works in your favor when you sell overpriced premium, the crowd is paying you every day they’re wrong.

• Because the cause is behavioral, not informational, it doesn’t require a new “signal” each time, it’s the same human flaw, repeating.

How to Exploit It Without Predicting Direction

The key is to remember: you don’t need the stock to crash. You just need the overpriced options to lose value faster than your risk increases.
That’s why overconfidence setups are perfect for:

1. Credit Spreads – Define risk, collect inflated premium.

2. Iron Condors – When skew is lopsided but both sides are rich.

3. Broken-Wing Butterflies – Targeted skew harvest with credit and defined risk.

This isn’t about being smarter than the crowd, it’s about letting their confidence fund your trades.

Probabilities over predicitons,

Andy Crowder

Citation:
Chen, H.-S., & Sabherwal, S. (2023). The Effects of Option Trading Behavior on Option Prices. Journal of Risk and Financial Management, 16(7), 337. https://doi.org/10.3390/jrfm16070337

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