Recency Bias During Sell-Offs

Intermediate | Published: 2025-12-28

Why It Matters

In March 2020, the median investor forecast called for -15% returns over the next year. The actual return was +40% -- a 55 percentage-point miss driven almost entirely by the previous month's headlines. That gap illustrates recency bias at its most destructive: you extrapolate recent declines into the indefinite future, ignore century-long base rates, and sell near the bottom.

TL;DR: Recency bias causes investors to treat short-term market drops as permanent trends. Mechanical rules -- scheduled rebalancing, rolling-return dashboards, and written base-rate reminders -- beat willpower every time.

Definition and Core Concept

Recency bias is the tendency to overweight recent information when making predictions. Daniel Kahneman and Amos Tversky identified this pattern in their 1973 Psychological Review paper on prediction. In investing, a -30% month dominates your thinking while +10% average annual returns over 100 years feels abstract.

Two predictable distortions follow:

• After declines: "The trend is down; it will continue" (extrapolation of pain)
• After rallies: "The trend is up; easy gains ahead" (extrapolation of euphoria)

Both ignore mean reversion -- the statistical tendency for extreme outcomes to move back toward long-term averages.

Why Recent Pain Feels Permanent

Robin Greenwood and Andrei Shleifer demonstrated in their 2014 Review of Financial Studies paper that investor expectations strongly extrapolate recent returns. When the trailing one-year return is high, investors forecast high future returns -- yet actual subsequent returns tend to be low. Recent performance anti-predicts future performance.

A useful causal chain explains how this unfolds:

Recency bias (driver) --> Availability heuristic (mechanism) --> Extrapolation bias (behavior) --> Poor market timing (sell low, buy high)

Nicholas Barberis, Andrei Shleifer, and Robert Vishny showed in their 1998 Journal of Financial Economics model that investors underreact to fundamentals but overreact to short-term price patterns -- exactly backward from rational weighting.

KEY INSIGHT: The correlation between recent returns and future returns is negative. When everyone around you agrees the market will keep falling, that consensus itself is a contrarian signal, not wisdom.

How Recency Bias Shows Up in Portfolios

March 2020: Selling at the COVID Bottom

You hold a diversified $100,000 portfolio (60/40 stocks/bonds) entering 2020. Between February 19 and March 23, the S&P 500 falls from 3,386 to 2,237 -- a 34% drop in five weeks. Your portfolio sits at roughly $73,600.

Every number in your head is negative: one-month return -30%, three-month return -34%. Headlines scream "worst crash since 1929." You sell your entire stock position on March 25 to "stop the bleeding."

By August 2020, the S&P 500 passes pre-crash highs. By March 2021, it reaches 3,950 -- up 77% from the bottom. Your all-bond portfolio still sits near $73,600. A held 60/40 portfolio would be worth roughly $116,500. The opportunity cost: $42,900, or 37% of your starting capital.

The mechanical alternative: rebalance on schedule. Sell bonds (now above 40% of the portfolio), buy stocks (now below 60%). You buy at the bottom and capture the full recovery.

Note: This reflects a composite pattern. March 2020 equity mutual fund outflows hit record levels; millions of investors sold near the bottom.

2008-2009: When the World Felt Broken

From October 2007 to March 2009, the S&P 500 fell 57% over 17 months. Every trailing return was deeply negative: one-year -43%, two-year -50%, three-year -57%. Equity mutual fund outflows peaked at $30 billion per month. Investor surveys showed over 70% expected further declines.

The actual result from the March 2009 bottom: +132% over four years (23% annualized). By 2013, the same investors who sold at the bottom were buying back at higher prices, now extrapolating "easy gains ahead." Recency bias works both directions.

KEY INSIGHT: The worse recent returns look, the stronger recency bias becomes -- and the more valuable base rates are. Selling in March 2009 meant acting on a 3-year trailing return of -57% while ignoring the 100-year base rate of +10% annually.

Decision Rules to Counter Recency Bias

Rolling Return Reference Check

Before any major allocation change, review 10-year, 20-year, and 100-year rolling returns. Ask: "Am I making a 20-year decision based on 6-month data?" If yes, recency bias is driving the decision. In March 2020, the trailing one-year return of -30% sat at the 5th percentile historically -- an extreme likely to mean-revert, not persist.

Evaluation Period Lock

Set your portfolio review cadence before volatility hits: quarterly or annual only. Shlomo Benartzi and Richard Thaler showed in their 1995 Quarterly Journal of Economics paper that frequent evaluation increases perceived volatility, triggering excess risk aversion. If you are checking daily during a drawdown, you are feeding recency bias directly.

Base Rate Reminder

Before any panic-driven decision, write this down: "100-year stock return: +10% annual. My current fear is based on ___ months of data." Keep a base-rate card with worst-case historical ranges:

• Worst 1-year return: -43% (1931)
• Worst 10-year return: +0.5% annualized (1999-2009)
• Worst 20-year return: +6% annualized (1929-1949)

If the recent period falls within historical range, it is noise, not signal.

Contrarian Indicator Check

When over 70% of investors and media are uniformly bearish or bullish, assume recency bias is extreme. Track the AAII Sentiment Survey: when bulls drop below 20% or rise above 60%, reversals historically follow within 6-12 months.

Detection Signals

You are likely affected by recency bias when:

• Your 5-year forecast closely mirrors the last 3 months of returns
• Your risk assessment has dramatically shifted but fundamentals have not
• You cannot articulate a scenario where recent trends reverse
• You are checking your portfolio daily instead of quarterly

When Recent Trends Actually Matter

Not all recent data should be ignored. Structural regime changes (the internet in the 1990s, major monetary policy shifts like Volcker's rate hikes in the 1980s) can justify updating your model. The test: can you articulate why this period is fundamentally different from 100 years of history? If your answer is "the decline feels worse" or "everyone agrees," that is recency bias, not analysis.

Next Step

Calculate your return extrapolation error right now. Write down what you expect stocks to return annually over the next 10 years. Compare it to the historical average of roughly 10%. If your forecast deviates by more than 5 percentage points, recent market conditions are likely anchoring your thinking. Forecasting below 5% after a bear market or above 15% after a bull run both signal recency-driven extremes.

Related Articles

• Overconfidence Bias in Bull Markets
• Loss Aversion and How to Counter It
• Anchoring on Purchase Price Mistakes
• Building Rules-Based Rebalancing to Limit Emotion

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References

Barberis, N., Shleifer, A., & Vishny, R. (1998). A Model of Investor Sentiment. Journal of Financial Economics, 49(3), 307-343. DOI

Benartzi, S., & Thaler, R. H. (1995). Myopic Loss Aversion and the Equity Premium Puzzle. The Quarterly Journal of Economics, 110(1), 73-92. DOI

Greenwood, R., & Shleifer, A. (2014). Expectations of Returns and Expected Returns. The Review of Financial Studies, 27(3), 714-746. DOI

Kahneman, D., & Tversky, A. (1973). On the Psychology of Prediction. Psychological Review, 80(4), 237-251. DOI