Loss Aversion — CIO Academy

Loss Aversion: asymmetry in the perception of gains and losses

Loss Aversion (loss aversion) is a fundamental cognitive bias discovered by Daniel Kahneman and Amos Tversky. People experience the pain of loss about twice as strongly as the joy from an equivalent gain.

Kahneman–Tversky Experiment

Choice between:

A: Guaranteed to receive $50
B: 50% chance to receive $100, 50% chance to receive $0

Mathematically: EV(A) = EV(B) = $50.
But most people choose A.

Now the choice between:

C: Guaranteed to lose $50
D: 50% chance to lose $100, 50% chance to lose $0

Most people choose D (risk) to avoid a guaranteed loss!

Loss aversion coefficient $\lambda \approx 2.0$–$2.5$

Losing $100 feels like losing $200\text{–}250$ in terms of utility.

Consequences for Investors

Behavior	Description	Result
Disposition Effect	Sell winners too early, hold losers too long	"Locking in profits" but not losses
Panic Selling	Sell during crisis at the lows	Realize losses, miss recovery
Risk Aversion increases after losses	Become too conservative	Miss out on recovery
Over-trading	Desire to "win back losses"	Transaction costs, worse timing

Disposition Effect: Data

Study by Terrance Odean (UC Berkeley):

Investors sell winning positions 50% more often than losing ones
At the same time, sold winners continue to rise
Held-on losers continue to fall

Result: -3.4% annual underperformance

Market-wide effects

Phenomenon	Mechanism
Momentum	Winners sold too early → underreaction
Crashes	Panic selling → cascade → overshooting
Recovery speed	Fear keeps investors out → slow recovery participation

Defensive mechanisms for CIO

Mechanism	Description	Effectiveness
Pre-commitment	Sale rules in IPS before events	High
Investment Committee	Collective decision-making	High
Automatic Rebalancing	Rules-based, removes emotion	High
Cooling-off periods	24-48 hours before major decisions	Medium
Devil's Advocate	Someone always argues against	Medium

Reframing: changing perspective

Loss Aversion Framing	Rational Reframing
"I lost $50,000"	"Portfolio dropped by 5% — this is normal volatility"
"I need to sell to stop the pain"	"Selling at the bottom — worst possible timing"
"The market will never recover"	"Historically markets have always recovered"

Pre-mortem analysis

Before investing, imagine that it failed. Why?
Document potential reasons for failure
Define in advance when you will sell
Coordinate with committee before the emotional moment

CIO Recommendations

Know thyself — admit that you are subject to this bias
Rules-based approach — IPS with clear rules
Avoid checking portfolio daily — less noise = fewer emotions
Long-term framing — think in years, not days
Stress inoculation — discuss crisis scenarios in advance