Herd Behavior During Market Manias

Intermediate | Published: 2025-12-28

Why It Matters

Herd behavior—the tendency to follow the crowd rather than independent analysis—shows up in portfolios as buying assets because 'everyone else is', ignoring valuation when social momentum peaks, and holding through collapses because selling alone feels worse than losing with the crowd. In real market data, retail investors exhibit strong herding in IPO markets, with first-day trading volume concentration predicting subsequent underperformance of -15% to -25% over the next year (Griffin et al., 2011).

The practical antidote isn't avoiding all trends (trends can be real). It's measuring social signals (Google Trends spikes, casual conversation mentions, Reddit activity) to identify when trend becomes late-stage mania—and refusing to enter at mania peaks.

Definition and Core Concept

Herd behavior is the tendency to follow the crowd's actions rather than your own independent judgment, even when your private information suggests the crowd is wrong (Banerjee, 1992). In investing, you buy GameStop at $300 not because of valuation analysis, but because everyone you know is buying and not participating feels like missing out.

Two mechanisms drive this:

• Social proof: You use others' behavior as a validity signal ("If everyone is buying, it must be right")
• Regret asymmetry: Being wrong alone (not buying, stock keeps rising) hurts more than being wrong with the crowd (buying at top, everyone loses together)

The result: you enter manias late (after the crowd is already in), pay mania prices (when valuation is most extreme), and exit too late (because selling before the crowd feels like quitting early).

The Information Cascade Mechanism (Why Rational People Create Irrational Crowds)

Herd behavior isn't System 1 emotion overriding logic—it's often rational at the individual level but irrational collectively. Rules based on social signal measurement help you distinguish between legitimate trends (worth following) and late-stage manias (guaranteed reversals).

The mechanism (Bikhchandani et al., 1992): information cascades occur when early movers' actions carry more weight than later movers' private signals. You see 10 people buy Tesla, you think "They must know something I don't," so you ignore your private skepticism (valuation looks extreme) and follow the crowd. Each new buyer reinforces the cascade—until the cascade collapses when a critical mass stops buying.

Shiller (2015) shows herd behavior is driven by social proof and fear of regret—not because investors believe the crowd is right, but because being wrong alone is psychologically costlier than being wrong with everyone else.

Related Concepts (Use These to Think Clearly)

• Herd behavior: the observable pattern—following the crowd's actions
• Information cascade: the mechanism—ignoring private information to follow others' actions
• FOMO (Fear of Missing Out): the emotional driver—anxiety from seeing others profit while you don't participate

A useful causal chain: Social proof (driver) → Information cascade (mechanism) → FOMO (emotion) → Herd behavior (action) → Mania pricing (outcome)

Shiller (2015) documents how herd behavior creates feedback loops: rising prices attract attention, attention attracts buyers, buyers push prices higher, creating narrative of "easy gains" that attracts more buyers—until the cascade reverses and everyone sells simultaneously.

How Herd Behavior Shows Up in Portfolios

Example 1: GameStop Mania (January 2021—when everyone you knew was buying)

Scenario: GameStop (GME) rallies from $20 to $483 in two weeks (January 2021), driven by Reddit WallStreetBets "short squeeze" narrative. Your coworkers, friends, Twitter feed all discuss GME gains.

Phase 1: The Rally (January 13-27)

• Jan 13: GME trading at $20 (fundamentals: struggling retail chain, negative earnings)
• Jan 22: GME at $65 (+225% in 9 days)—mainstream media coverage begins
• Jan 25: GME at $150 (+650%)—CNBC, Bloomberg cover story non-stop
• Jan 27: GME at $350 (+1,650%)—trading halts, Robinhood restricts buying
• Jan 28: Peak: $483 (+2,315% from Jan 13)

How herd behavior manifests:

• Social proof:
  • "Everyone is making money—I'm missing out" (your coworker shows $5K → $40K gain)
  • Reddit WallStreetBets subscribers surge: 2M → 8M in one week
  • Google Trends for "GameStop stock" hits all-time high (100x normal search volume)
• Regret asymmetry:
  • Not buying, GME keeps rising: "I'm an idiot for sitting out" (regret is personal, isolating)
  • Buying at top, GME crashes: "Everyone lost money, it's not my fault" (regret is shared, diffused)
  • You enter at $300 (Jan 27) because not participating while everyone else profits feels unbearable
• Information cascade:
  • Early buyers ($20-$40) post gains on Reddit—you think "They must know something"
  • You ignore your private skepticism ("GameStop has no path to profitability at $300")
  • You follow the crowd: buy at $300
• Narrative contagion:
  • Story spreads virally: "Stick it to Wall Street hedge funds" (social movement framing)
  • Fundamental analysis feels outdated: "This isn't about valuation, it's about the squeeze"
  • Buying GME becomes identity signal ("part of the movement," "diamond hands")

Your position:

• Entry: $300 (Jan 27)
• Position size: (10{,}000 ÷ 300 = 33.3) shares
• Peak (next day): $483 → value $16,100 (+61% unrealized gain)
• Psychological state: "I was right to buy! Easy money!"

Phase 2: The Collapse (January 29-February 19)

• Jan 29: GME drops to $225 (-53% from peak in one day)
• Feb 2: GME at $90 (-81% from peak)
• Feb 19: GME at $40 (-92% from peak)
• Your position: (33.3 × 40 = 1{,}332)
• Loss from entry: $8,668 (-87%)

Why you didn't sell during collapse:

• Herd behavior in reverse: Reddit crowd says "Diamond hands! Hold the line!"
• Social pressure: Selling feels like betraying the movement
• Regret asymmetry persists: Selling at $200 then watching it go back to $400 (hypothetical) feels worse than holding to $40 with everyone else
• Narrative lock-in: "They're manipulating the stock, we just need to hold"

The practical point: You entered because of social signals (everyone buying, Reddit hype, media saturation), not fundamental analysis. Herd behavior drove you to buy at late-stage mania ($300, two days before peak $483). The same herd behavior kept you holding through the collapse (social pressure, narrative identity).

Mechanical alternative (social signal filter):

• Social mention spike detection: Google Trends for "GameStop" was 100x baseline (mania warning)
• Anecdotal evidence filter: If 3+ non-investor friends mention GME in one week → peak mania signal
• Rule: When social signals spike >10x baseline, do not buy (wait for reversion)
• Result: Avoid entry at $300; if already in from earlier, exit at first >10x social signal spike ($150-$200 range)

Note: This represents a composite pattern. Millions of retail investors entered GME between Jan 25-28 at $200-$400 range, driven by identical social proof and FOMO dynamics.

Example 2: Dot-Com IPO Frenzy (1999-2000—when not buying felt irrational)

Scenario: You watch internet IPOs double, triple on first day of trading (1999-2000). Pets.com, eToys, Webvan all soar 100%+ on Day 1. Friends brag about IPO allocation gains. You feel like you're missing the opportunity of a lifetime.

Phase 1: The Build (1998-early 1999)

• Early IPO successes: Amazon (1997 IPO, +900% by 1999), eBay (1998 IPO, +500% by 1999)
• Narrative forms: "Internet changes everything," "Traditional valuation doesn't apply"
• Average IPO first-day pop: 40% (vs historical 10-15%)
• Social proof: Your neighbor made $50K flipping an IPO—"I need to get in"

Phase 2: The Mania (mid-1999-early 2000)

• IPO first-day pops reach 100-200%
• TheGlobe.com (Nov 1998): first-day gain +606% (largest in history at the time)
• Everyone wants allocation—retail investors buy on first day at any price
• Regret asymmetry: Missing IPO gains (while everyone else profits) feels worse than losing in IPO crash (losing with the crowd)

Phase 3: Peak Mania (February 2000)

• Pets.com IPO (Feb 11, 2000):
  • IPO price: $11
  • First-day close: $14 (+27%)
  • You buy on first day at $14 (because first-day pops have been reliable)
  • Position: (10{,}000 ÷ 14 = 714) shares
• Information cascade:
  • You see: Early IPO buyers made fortunes (Amazon, eBay, Yahoo)
  • You think: "Every internet IPO is a winner, I'm late but not too late"
  • You ignore private skepticism: "Pets.com has no path to profitability, but neither did Amazon initially"
• Social validation: Buying internet IPOs becomes status signal ("forward-thinking," "tech-savvy")

Phase 4: The Collapse (March 2000-November 2000)

• March 2000: NASDAQ peaks at 5,048 (dot-com bubble top)
• Pets.com trajectory:
  • March 2000: $14 (your entry)
  • May 2000: $3 (-79%)
  • November 2000: Company bankrupt, stock delisted
  • Final value: $0
• Your position: (714 × 0 = 0)
• Total loss: $10,000

Why you didn't sell:

• Herd behavior persists: Media still bullish ("temporary correction"), analysts maintain "strong buy" ratings
• Greater fool theory: "Even if overvalued, I can sell to someone else tomorrow" (works until everyone stops buying)
• Narrative lock-in: "Internet is the future—this is just noise"
• Social pressure: Selling feels like admitting you were wrong (everyone else is holding)

The durable lesson: IPO first-day pop of +27% should have been warning signal (mania pricing), not validation. By February 2000, social signals were extreme: Google Trends (if it existed then) would have shown massive spikes, casual conversations saturated with IPO talk, mainstream media coverage non-stop. You followed the herd into late-stage mania.

Quantified cost: (10{,}000 → 0) = $10,000 total loss (followed IPO mania herd into bankruptcy).

Mechanical alternative:

• IPO first-day pop threshold: If first-day gain >50%, avoid buying (mania pricing, likely reversal)
• Social signal check: If 3+ non-investors mention IPOs in one week, it's late-stage mania
• Result: Pets.com first-day pop +27% (below 50% threshold but Feb 2000 was peak social mania)—avoid all IPO buying in Feb-March 2000 window

Note: NASDAQ fell -78% from March 2000 to October 2002. Average internet IPO from 1999-2000 declined -95% from peak. Millions of retail investors followed identical herd behavior into IPO mania.

Quantified Decision Rules (Defaults, not prescriptions)

These are starting points to counter measurable herd behavior. Adjust for your risk tolerance, but maintain the discipline of social signal measurement.

Social Mention Spike Detection (default starting point)

If social media mentions (Twitter, Reddit, Google Trends) >10x baseline in one week → high herd risk, avoid buying.

Rationale: Late-stage manias produce exponential social signal growth. When Google Trends spikes 10x-100x, you're in the final inning—not the first.

Professional-grade upgrade:

• Track Google Trends for stock ticker + company name (set baseline = 3-month average)
• Track Reddit mentions (use subredditstats.com for r/WallStreetBets, r/investing)
• Track Twitter mentions (use social listening tools or manual tracking)
• Threshold: If any metric >10x baseline in 1 week → mania warning, do not buy

Interpretation:

• Healthy: Mentions 1-2x baseline (normal interest, trend could be early)
• Warning: Mentions 3-5x baseline (elevated attention, monitor closely)
• Critical: Mentions >10x baseline (late-stage mania, do not enter)

Example: GameStop Jan 2021: Google Trends hit 100x baseline (Jan 27). That was the mania peak—price crashed 2 days later.

Anecdotal Evidence Filter (behavioral circuit breaker)

If ≥3 non-investor acquaintances mention stock unprompted in one week → peak herd behavior, avoid buying.

Rationale: When taxi drivers, barbers, family members ask about a stock, retail mania has peaked. Institutional money is exiting while retail is entering.

Professional-grade upgrade:

• Track casual conversations (not with investors—with non-finance friends, family, service workers)
• Count unprompted mentions (they bring it up, not you)
• Threshold: ≥3 mentions in one week = mania peak signal

Interpretation:

• Healthy: 0-1 mentions (stock not viral, could be early trend)
• Warning: 2 mentions (building momentum, monitor)
• Critical: ≥3 mentions (late-stage mania, retail FOMO peaked, avoid buying)

Customization: The "taxi driver indicator" isn't a joke—it's measurable. When people who don't follow markets start asking you about a stock, the smart money is already selling to the dumb money (you).

IPO First-Day Pop Threshold (mania filter)

If IPO first-day gain >50% → avoid buying (mania pricing, statistically likely to reverse).

Rationale: Griffin et al. (2011) show IPOs with extreme first-day pops predict subsequent underperformance of -15% to -25% over next year. First-day pop >50% is herd-driven, not fundamental.

Professional-grade upgrade:

• Check IPO first-day performance (close vs IPO price)
• Calculate: (First-day close - IPO price) ÷ IPO price = First-day pop %
• Threshold: If pop >50%, do not buy (even if you want to "get in on the action")

Interpretation:

• Healthy: First-day pop <20% (rational pricing, fundamental demand)
• Warning: First-day pop 20-50% (some mania, proceed cautiously, expect volatility)
• Critical: First-day pop >50% (pure mania, extremely likely to reverse—avoid entirely)

Practical note: Missing IPO mania gains feels bad (FOMO), but preserving capital is better than being the last buyer before collapse.

Mitigation Checklist (tiered)

Essential (high ROI on avoiding mania peaks)

• □ Social signal tracking: Monitor Google Trends, Reddit mentions—if >10x baseline, avoid buying
• □ Anecdotal evidence log: Count unprompted stock mentions from non-investors—if ≥3/week, peak mania
• □ IPO first-day filter: If first-day pop >50%, do not buy (mania pricing, likely reversal)
• □ Contrarian calendar rule: Never buy asset that's up >100% in one month (parabolic = late-stage mania)

High-impact (workflow + structural protection)

• □ Google Trends alerts: Set alerts for tickers you watch—if spike >10x, sell (don't buy)
• □ Reddit activity monitoring: Track r/WallStreetBets mentions using subredditstats—spikes predict mania peaks
• □ Media saturation indicator: If stock appears on CNBC/Bloomberg >3x in one day, it's late-stage (avoid)
• □ Pre-commitment rule: Write "I will not buy assets in late-stage mania (defined as >10x social signals)" and sign it

Optional (good for FOMO-prone investors)

• □ Social media blackout during mania: Unfollow r/WallStreetBets, mute stock tickers on Twitter during parabolic moves
• □ FOMO journal: When you feel urge to buy into mania, write down feelings—review later to see pattern
• □ Accountability partner: Text friend before buying into social-driven rally—ask "Am I following the herd?"

Detection Signals (how you know it's affecting you)

• Your investment thesis is "everyone else is buying" (not fundamentals, not valuation—just social proof)
• You can't articulate why stock is worth current price, only that "it keeps going up"
• You feel FOMO (anxiety from missing out) more than conviction (confidence from analysis)
• You're checking social media (Reddit, Twitter) more than financial statements
• You use phrases like "I'll sell to the next guy" or "it's a momentum trade" (greater fool theory)
• You're buying assets up >100% in one month (parabolic moves = late-stage mania, not early trends)

Measurement Framework (make it measurable)

Social Mention Multiplier

Formula: (Current week mentions) ÷ (3-month average weekly mentions)

Interpretation:

• Healthy: 1-2x (normal interest, could be legitimate trend)
• Warning: 3-5x (elevated social attention, monitor for acceleration)
• Critical: >10x (late-stage mania, do not buy—if you own, consider selling)

Example: GameStop Jan 27, 2021: Google Trends = 100x baseline → mania peak (price crashed 2 days later).

Anecdotal Mention Count

Method: Count unprompted mentions from non-investor acquaintances per week.

Interpretation:

• Healthy: 0-1 mentions/week (stock not viral)
• Warning: 2 mentions/week (building retail awareness, monitor)
• Critical: ≥3 mentions/week (retail mania peaked, smart money exiting, avoid buying)

Practical note: This is qualitative but powerful. When your barber, Uber driver, or parent asks about a stock, it's not early—it's late.

IPO First-Day Pop Percentage

Formula: (First-day close - IPO price) ÷ IPO price × 100

Interpretation:

• Healthy: <20% (rational demand, fair pricing)
• Warning: 20-50% (some mania, expect volatility)
• Critical: >50% (pure mania, statistically likely to underperform -15% to -25% over next year)

Example: TheGlobe.com (Nov 1998): first-day pop +606% → stock eventually went to $0 (bankruptcy).

When Herd Behavior Might Be Acceptable (the nuance)

Herd behavior explains most mania losses, but early-stage trend following isn't always herd behavior. Following trends can be rational when:

Legitimate reasons:

• Early-stage trend (not mania): Social signals 2-3x baseline (not 10x+), asset up <50% (not parabolic), narrative is new (not saturated)—you're joining trend early, not mania late
• Momentum investing with rules: You have explicit exit rules (sell if down 10%, sell if social signals spike >10x), not just "hold because everyone else is holding"
• Diversification into trend: Small position size (5-10% of portfolio), not concentrated bet driven by FOMO

The test: Can you articulate your exit criteria independent of what the crowd does?

If your answer is "I'll sell when everyone else sells," that's herd behavior (and you'll be last). If your answer is "I'll sell if price drops 15%, or if Google Trends spikes above 10x baseline, or if fundamentals deteriorate," that's rules-based trend following (defensible).

Case Studies (Herd Behavior at Extreme Scale)

The Dutch Tulip Mania (1636-1637—the original herd behavior case study)

Manifestation: Tulip bulbs in the Netherlands became speculative assets during 1636-1637. Prices for rare bulbs rose 20x-30x in months. Classic information cascade: early buyers profited, late buyers followed based on social proof ("If they're paying these prices, prices must be justified").

The mania peak (February 1637):

• Single tulip bulb (Semper Augustus variety) sold for 5,200 guilders
• Equivalent to ~$50,000 today or 10x annual craftsman salary
• Buyers justified prices via social proof alone: "Others are paying these prices, so they must be rational"
• No fundamental value change (tulips still produce same flowers, same utility)

The collapse (March 1637):

• Prices collapsed -95% in March 1637 when buyers stopped appearing
• Late entrants (February 1637 buyers) lost entire investment
• Market never recovered—tulips returned to normal commodity pricing
• Estimated thousands of investors bankrupted; Dutch economy disrupted for years

The lesson: Herd behavior has existed for centuries. When prices disconnect from fundamentals and social proof becomes the only justification, collapse is inevitable—just unpredictable in timing. The challenge is recognizing mania while you're in it, not after it collapses.

Source: Historical accounts well-documented in economic history literature. Tulip Mania is debated in details but core dynamic (social proof driving prices, collapse) is consensus.

Bitcoin Rally to $69K (2020-2021—when everyone became a crypto expert)

Manifestation: Bitcoin rallied from $10K (Sept 2020) to $69K (Nov 2021), driven by institutional adoption narrative (Tesla, MicroStrategy purchases), social media momentum, and retail FOMO.

Social signals spiked:

• Google Trends for "Bitcoin" hit all-time high (Nov 2021)—20x baseline
• Reddit r/Bitcoin subscribers grew from 1.5M (Jan 2020) to 4M (Nov 2021)—2.7x growth in 2 years, accelerating in 2021
• Twitter "laser eyes" profile pics signaled Bitcoin maximalism—social identity tied to asset
• Anecdotal peak indicator: Non-investors asking "Should I buy Bitcoin?" at Thanksgiving dinners (Nov 2021)

Crowd psychology phases:

• Early phase (2020): Institutional adoption (MicroStrategy, Tesla) provides legitimacy, price $10K-$30K
• Mid-phase (early 2021): Coinbase IPO (April 2021), mainstream media coverage, price $30K-$60K
• Late phase (Oct-Nov 2021): Parabolic rise ($40K → $69K in 6 weeks), social media saturation, retail FOMO peaks

Outcome:

• Bitcoin peaked $69K (Nov 9, 2021)
• Collapsed to $16K (Nov 2022) = -77% decline
• Median retail entry point (estimated via Coinbase app downloads spike, Google Trends peak, social media activity): $50K-$60K range
• Average late entrant lost -68% to -77%

Quantified impact: On (10{,}000) position entered at (60{,}000): (10{,}000 × (16{,}000 ÷ 60{,}000) = 2{,}667). Loss: $7,333 (joined herd at mania peak).

The lesson: Bitcoin wasn't wrong as an asset (it recovered later). $60K-$69K was wrong as an entry point—driven by herd behavior, not fundamental analysis. When Google Trends, social media mentions, and casual conversation all spike simultaneously, you're in late-stage mania. Social signals (20x Google Trends, Thanksgiving dinner mentions) were measurable warning signs that late-stage mania had arrived.

Source: Google Trends, Reddit subscriber data, Coinbase app download rankings all publicly available. Price data from CoinMarketCap.

Common Rationalizations and Reality Checks

"But everyone else is making money—I'm the only one not in"

Reality: Late-stage manias create survivorship bias. You only hear from winners (early entrants), not losers (late entrants, which will be the majority).

Counter: When >50% of your social circle is talking about a stock, you're not early—you're late. The people making money entered when <10% were talking about it.

"This time is different—it's not a mania, it's a paradigm shift"

Reality: Every mania believes "this time is different." Dot-com: "Internet changes everything." Bitcoin 2021: "Institutional adoption is different." GameStop: "We're fighting hedge funds."

Counter: Paradigm shifts exist, but they don't justify parabolic price moves in 2 weeks. Real paradigm shifts (internet, mobile, AI) play out over decades, not days. If price is up 500% in one month, it's mania—not paradigm shift.

"I'll just sell before everyone else does"

Reality: You can't time mania peaks. GameStop crashed -50% in one day. By the time you decide to sell, the herd is already selling and you're late to exit just like you were late to enter.

Counter: If your strategy is "sell to a greater fool," you're relying on herd behavior continuing. When the herd reverses, it reverses fast—faster than you can react.

"It's not a mania—there's a real story here"

Reality: Manias always have real stories (internet revolution 1999, crypto adoption 2021, short squeeze 2021). The story is real; the price is mania. Price moves faster than fundamentals can justify.

Counter: Measure social signals, not narrative plausibility. Compelling narratives with >10x Google Trends spikes and 3+ casual mentions/week = late-stage mania, regardless of story quality.

Next Step (educational exercise)

Audit social signals for a stock you're considering buying right now:

1. Google Trends check: Search stock ticker + company name on Google Trends (trends.google.com)
   • Compare current week to 3-month average
   • Calculate multiplier: Current week ÷ 3-month average
2. Anecdotal evidence count: How many non-investors mentioned this stock to you in past week? (count unprompted mentions)
3. Reddit activity (if relevant): Check r/WallStreetBets or r/investing for mention frequency
4. Calculate herd risk score:
   • Google Trends >10x baseline = 50 points
   • Anecdotal mentions ≥3 = 30 points
   • Reddit front-page mentions ≥3/week = 20 points
   • Total score >50 = high herd risk, avoid buying

Interpretation:

• Score 0-20: Low herd risk (could be legitimate early trend)
• Score 20-50: Moderate herd risk (proceed cautiously, small position only)
• Score >50: High herd risk (late-stage mania, do not buy—if you own, consider selling)

Action item: If score >50 and you're considering buying, wait 2 weeks and re-check social signals. If still elevated, it's mania—avoid entirely.

Related Articles

• Recency Bias During Sell-Offs
• Confirmation Bias in Stock Research
• Overconfidence Bias in Bull Markets
• Loss Aversion and How to Counter It

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References

Banerjee, A. V. (1992). A Simple Model of Herd Behavior. The Quarterly Journal of Economics, 107(3), 797-817. (Rational individuals can engage in herd behavior when they observe others' actions but not their private information—following the crowd can be individually rational even when collectively irrational)

Bikhchandani, S., Hirshleifer, D., & Welch, I. (1992). A Theory of Fads, Fashion, Custom, and Cultural Change as Informational Cascades. Journal of Political Economy, 100(5), 992-1026. (Information cascades occur when early movers' actions carry more weight than later movers' private signals, causing rational individuals to ignore their own information and follow the crowd)

Griffin, J. M., Harris, J. H., & Topaloglu, S. (2011). Why Are IPO Investors Net Buyers Through Lead Underwriters? Journal of Financial Economics, 99(2), 518-532. (Retail investors exhibit strong herding in IPO markets, with first-day trading volume concentration predicting subsequent underperformance of -15% to -25% over the next year)

Shiller, R. J. (2015). Irrational Exuberance (3rd ed.). Princeton University Press, pp. 148-172. (Herd behavior in markets is driven by social proof and fear of regret—investors follow the crowd not because they believe the crowd is right, but because being wrong alone is psychologically costlier than being wrong with everyone else)