Behavioral Economics Applied to Trading#

Traditional economics assumes investors are rational. Reality is quite different. Daniel Kahneman (Nobel Prize, 2002) and Amos Tversky demonstrated that we are systematically irrational, and that our cognitive biases are expensive in the markets.

Prospect Theory#

Kahneman and Tversky showed that we feel the pain of a loss roughly 2.5 times more intensely than the pleasure of an equivalent gain. The consequence in trading: we hold losing positions too long (hoping for a rebound) and sell winners too early (fearing we will give the gains back).

The Major Cognitive Biases in Trading#

• Loss aversion: holding a losing position in the hope it recovers, rather than cutting it and reinvesting
• Disposition effect: selling winners too early and holding losers too long
• Anchoring bias: fixating on a purchase price or a past quote when making decisions
• Confirmation bias: seeking only the information that supports your existing view
• Recency effect: overweighting recent events and ignoring long-term history
• Overconfidence: overestimating your skill after a winning streak
• Survivorship bias: seeing only the success stories ("this teenager made $1m in crypto") and ignoring the millions who lost

The Framing Effect#

The way information is presented shapes our decisions. "This stock has a 60% probability of gain" sounds attractive. "This stock has a 40% chance of loss" sounds risky. Yet it is the same information. Financial media exploit framing constantly.

Mental Accounting#

We treat money differently depending on where it came from. Market gains are often reinvested more aggressively ("it's free money"), while salary is handled more cautiously. In reality, EUR 1 is EUR 1, whatever its origin.

Herding#

The herd instinct pushes us to do what everyone else is doing, particularly under uncertainty. In the markets this creates bubbles (everyone buys) and panics (everyone sells). Social media amplifies the effect: meme stocks (GME, AMC) are a textbook case of herding.

How Do You Counter Your Own Biases?#