Key takeaways
– Regret theory says people anticipate feeling regret about future outcomes and let that anticipation influence their choices. (Investopedia)
– Anticipated regret can make investors either overly risk-averse (selling to avoid loss) or overly risk-seeking (chasing missed winners / FOMO). (Investopedia)
– Recognizing and structuring around the influence of regret — with rules, automation, and deliberate decision processes — reduces its harmful effects on long-term returns. (Investopedia; Diecidue & Somasundaram, 2017)
What is regret theory?
Regret theory (in behavioral economics) holds that people take into account how much they expect to regret possible outcomes when choosing among options. Anticipated regret can therefore change preferences: people may avoid actions that could lead to regret, or pursue actions aimed at avoiding the regret of omission (missing out). In financial contexts that anticipation affects whether and how investors buy, hold, or sell assets. (Investopedia; Diecidue & Somasundaram, 2017)
Regret theory and psychology: how it operates
– Anticipated emotion: decision-making incorporates imagined emotional outcomes (e.g., “If I sell now and it rallies, I’ll feel awful” or “If I don’t buy and it doubles, I’ll regret it”).
– Omission vs. commission: people often judge bad outcomes from actions (commission) more harshly than bad outcomes from inaction (omission), which can bias choices.
– Interaction with other biases: regret feeds loss aversion and FOMO, and can contribute to herd behavior or “irrational exuberance” in bubbles. (Investopedia; Kahneman & Tversky, 1979; Greenspan remarks)
How regret contributes to market behavior and crashes
– During extended bull markets, fear of missing out (FOMO) and anticipated regret about missed gains can push investors to buy at ever-higher prices, inflating bubbles. Alan Greenspan famously highlighted “irrational exuberance” as an example of excessive optimism that drives prices beyond fundamentals. When bubbles burst, panic selling can follow. Historical crashes (1929, 1987, the dotcom collapse, 2007–08) illustrate how emotional and imitative behavior amplify cycles. (Investopedia; Greenspan)
Regret theory in the investment process: typical investor patterns
– Regret-averse selling: locking in losses quickly to avoid the pain of watching further declines (realizing losses rather than rebalancing).
– Regret-induced chasing: buying late into rallies to avoid missing gains after earlier “misses.”
– Overreaction to friends/experiences: adopting strategies based on prior regret (e.g., always following a friend who once picked a winner or always avoiding their tips after a loss), regardless of fundamentals. (Investopedia)
Practical steps to reduce regret-driven mistakes
Below are concrete, implementable steps investors and traders can use to limit the harmful effects of anticipated regret.
1) Increase awareness and keep a decision journal
– Before making trades, briefly note why you’re doing it, your time horizon, and what outcome would make you feel regret. After the trade, record outcomes and whether emotion affected your decision-making.
– Practical format: date, ticker, rationale, investment horizon, decision rules followed, expected outcomes, post-trade notes. Reviewing this monthly reveals patterns of regret-driven behavior.
2) Use rules-based (formula) investing and pre-commitment
– Predefine entry/exit rules, position sizes, and rebalancing schedules so your future choices aren’t driven by emotion in the moment. Example rules: dollar-cost average monthly into target funds; rebalance annually to target allocations within ±5%; limit any single stock to X% of portfolio.
– Formula investing and rules-based approaches remove much discretionary emotion; they can be automated or run through robo-advisors. (Investopedia; Morningstar; Grand View Research)
3) Automate where practical
– Automate contributions (e.g., recurring transfers), rebalancing, and trade execution. Automation reduces the chance of chasing or panicked selling. Robo-advisors and automated platforms are one route; algorithmic execution is another for active strategies. (Investopedia; Morningstar; Grand View Research)
4) Backtest and paper-trade your rules
– Backtesting helps you see whether a rules-based approach historically produces acceptable outcomes and highlights overfitting to “regretful” single events. Paper-trading new strategies for a trial period prevents immediate emotional reactions to single losses.
5) Use pre-mortem and checklist techniques
– Pre-mortem: before committing, imagine the strategy failed and list everything that could have caused it — this exposes unrealistic optimism and clarifies risk.
– Checklist: short, consistent questions to answer before any trade (e.g., “Does this fit my plan? If it goes down 20%, what will I do? Am I reacting to news or to peers?”). Checklists slow decisions and reduce impulsive, regret-driven choices.
6) Define and document your investment goals and horizons
– Concrete goals (retirement date, target savings, income needs) and time horizons make it easier to judge short-term volatility as normal and avoid emotionally motivated trades.
7) Limit social and news-driven influence
– Reduce impulsive trades driven by social chatter. Before acting on a tip, require one objective check (fundamental data, valuation, or a rule-based screen).
– Consider temporary “cooling-off” periods (e.g., wait 48 hours before acting on hype).
8) Position sizing, risk limits, and loss planning
– Use strict risk controls (e.g., cap any single position at a small % of portfolio, or risk no more than 1–2% of portfolio value on an aggressive trade).
– Predefine acceptable loss thresholds or trailing stop rules that are part of the plan, not emotional reactions after a trade moves against you.
9) Diversify and rebalance
– Diversification reduces single-event regret (one missed winner or one losing position matters less). Scheduled rebalancing enforces buying low and selling high mechanically.
10) Use professionals when needed
– Financial advisors or fiduciaries can provide a dispassionate second opinion. For investors who find emotion dominates decisions, a professional (or hybrid robo/human solution) can impose discipline. (Investopedia; Morningstar)
11) After a loss: structured reflection, not reaction
– If a trade goes wrong, pause. Ask: Did I follow my rules? Is the thesis broken or has volatility merely hit a planned drawdown? If your rules were followed, avoid impulsive revenge trading; if rules were violated, treat it as learning and update the rules.
12) Consider regret-minimization frameworks
– Instead of asking “Will I regret selling now?” ask “Given my long-term goals, which choice will minimize regret over 5–10 years?” Framing decisions by long-term regret can reduce short-term FOMO.
Examples of concrete rule sets (templates)
– Conservative, long-term investor: dollar-cost average 5% of monthly income into diversified index funds; cap single-stock exposure at 5% of portfolio; annual rebalancing to target allocation.
– Active, rules-based trader: limit risk per trade to 1% of account, use mechanical entry/exit signals (e.g., crossovers, volatility filters), backtest strategy to at least a 5-year period, paper-trade for 3 months before live capital.
– Robo-advisor + human review: use robo for core allocation and automatic rebalancing; keep a small satellite account for discretionary ideas, with a documented checklist for each trade.
Limitations and tradeoffs
– Rules reduce regret-driven errors but are not infallible: they can be mis-specified or poorly backtested. Over-automation can also produce complacency.
– Some intuition and discretionary judgment remain valuable, particularly in unusual market conditions; the goal is to make those discretionary actions deliberate and documented, not reflexive.
Further reading and sources
– Investopedia. “Regret Theory.”
– Diecidue, Enrico & Somasundaram, Jeeva. “Regret Theory: A New Foundation.” Journal of Economic Theory, vol. 172, November 2017, pp. 88–119.
– Kahneman, Daniel & Tversky, Amos. “Prospect Theory: An Analysis of Decision under Risk.” Econometrica, 1979. (for context on loss aversion and related biases)
– Remarks by Chairman Alan Greenspan, “The Challenge of Central Banking in a Democratic Society” (on “irrational exuberance”).
– Morningstar. “2023 Robo-Advisor Landscape.” (discussion of automated advisory growth and usage).
– Grand View Research. “Robo Advisory Market Size, Share & Trends Analysis Report,” 2022–2030.
Bottom line
Anticipated regret is a powerful, predictable influence on investor choices. The practical antidote is a combination of self-awareness and structure: identify how regret has affected your past decisions, adopt clear rules and risk limits, automate where appropriate, and document/reflect on decisions. Those steps reduce emotional reactions (both panic selling and FOMO-driven chasing) and help align behavior with long-term investment goals.