Why We Don’t Treat Gains and Losses the Same
In investing, losses count more than gains — in psychology, in mathematics, and in professional accountability.
Behavioural: Losing money feels roughly twice as painful as making the same amount feels good (Kahneman & Tversky, 1979).
Mathematical: Recovering from losses is harder:
-5% needs 5.3% to recover
–10% needs +11%
–25% needs +33%
–50% needs +100%
Agency: Managers are judged more harshly for losses than for missed upside. A significant drawdown can appear as “excessive risk” relative to expectations or benchmarks, potentially exposing them to client redemptions or even litigation.
Together, these factors make portfolio evaluation fundamentally asymmetric: avoiding losses matters more than chasing gains.
The Behavioural Side: Why Losses Matter More
Decades of behavioural finance research confirm that losses dominate the way investors experience risk:
Prospect Theory (1979): Kahneman & Tversky showed people value losses about twice as heavily as equivalent gains. This was the birth of “loss aversion.”
Myopic Loss Aversion (Benartzi & Thaler, 1995): Investors who check their portfolios too often see more losses and end up taking less risk, which explains why many stay underexposed to equities.
Disposition Effect (Odean, 1998): Investors hold on to losing stocks too long and sell winners too quickly, because realising a loss feels worse than locking in a gain.
Field & Experimental Studies (Camerer, 2005 review): Across lab experiments and fundamental markets, people systematically avoid bets with potential losses even when the odds are favourable.
Neuroscience (Tom et al., 2007, Science): Brain scans show that losses activate stronger responses in pain-related areas than gains do in reward areas. Loss aversion is wired into us.
Modern Evidence (Barberis, 2013): Both professional managers and individuals become more risk-averse after downturns, even when fundamentals haven’t changed.
These findings all point in the same direction: losses loom larger than gains, and investors’ behaviour consistently reflects this asymmetry.
The Mathematical Side: Compounding Hates Drawdowns
On top of psychology, the math reinforces the need for asymmetry:
Losses erode the base on which future gains are calculated.
The deeper the drawdown, the harder it is to climb back.
This is why volatility is not neutral — it builds the risk of significant, damaging losses that compound badly over time.
It is also why convexity is beneficial. Limiting losses and participating in gains is a real benefit for long-term returns, and Private Wealth managers who achieve that payoff should be rewarded.
The Agency Side: Asymmetric Accountability
Professional managers face an additional asymmetry: they are accountable to clients, peers, and regulators.
Asymmetric blame: After a loss, managers risk being accused of taking excessive risk against benchmarks, effectively having written a “legal put” for clients.
Conservatism as insurance: To reduce legal or reputational risk, managers tilt conservative.
Peer pressure: But leaning too far into safety risks, lagging peers — and losing clients for underperformance.
This creates a powerful need for peer awareness: managers must know how others are performing and what level of risk they are taking. Without that context, it’s impossible to judge whether their stance is defensible.
How the Perfometer Builds This In
The Perfometer’s Weather model uses an asymmetric curve to score portfolios relative to its reference index (typically its Performance Watcher Index - PWI+):
Relatively high-volatility portfolios are penalised even if returns look good, because higher volatility creates a greater risk of steep drawdowns.
In down markets, portfolios that protect capital by keeping volatility low are rewarded — because shallow drawdowns help compounding.
The result is a non-linear scoring pattern, where downside risk weighs more heavily than upside capture.
Weather as a Simple Language for Complexity
Instead of technical ratios, the Weather metaphor makes portfolio quality easy to understand at a glance:
Radiant – Major outperformance while risk is in check.
Sunny – Significant outperformance with risk well managed.
Sunny Spells – Slight outperformance or well-balanced performance.
Cloudy – Underperformance with manageable risk deviation.
Overcast – Significant underperformance.
Rainy – Major underperformance and/or excessive risk.
This six-step scale helps managers and clients immediately “see the weather” of a portfolio without needing to interpret ratios or formulas.
The Perfometer’s Asymmetric Design isn’t Arbitrary
By combining these truths into a single framework — and uniquely embedding peer-relative performance and risk — Performance Watcher offers a transparent and fair way to evaluate portfolio quality.
Selected References
Kahneman, D., & Tversky, A. (1979). Prospect Theory: An Analysis of Decision under Risk. Econometrica, 47(2), 263–291.
Tversky, A., & Kahneman, D. (1992). Advances in Prospect Theory: Cumulative Representation of Uncertainty. Journal of Risk and Uncertainty, 5(4), 297–323.
Benartzi, S., & Thaler, R. (1995). Myopic Loss Aversion and the Equity Premium Puzzle. Quarterly Journal of Economics, 110(1), 73–92.
Odean, T. (1998). Are Investors Reluctant to Realize Their Losses? Journal of Finance, 53(5), 1775–1798.
Camerer, C. (2005). Three Cheers—Psychological, Theoretical, Empirical—for Loss Aversion. Journal of Marketing Research, 42(2), 129–133.
Tom, S. M., Fox, C. R., Trepel, C., & Poldrack, R. A. (2007). The Neural Basis of Loss Aversion in Decision-Making under Risk. Science, 315(5811), 515–518.
Barberis, N. (2013). Thirty Years of Prospect Theory in Economics: A Review and Assessment. Journal of Economic Perspectives, 27(1), 173–196.