The bottom line upfront: In March 2020, Pershing Square Capital Management converted $27 million in credit default swap premiums into $2.6 billion in realized gains — a 96x return executed in approximately one month. This wasn’t luck or market timing alone. It was asymmetric risk architecture, derivative mechanics, and flawless execution converging at a structural market dislocation.
For quantitative researchers: This trade demonstrates convexity capture, spread dynamics, and tail-risk monetization at scale. For discretionary traders: It shows how conviction, instrument selection, and exit discipline create outsized returns. For both: It’s a masterclass in how professional capital actually makes money during crises.
This analysis deconstructs the exact P&L mechanics, examines the mathematical drivers of returns, and extracts replicable principles for systematic and discretionary strategies.
I. Market Setup: The Mispricing That Created the Opportunity
The Structural Context
By February 19, 2020, the S&P 500 reached an all-time high of 3,386.15 — capping an 11-year bull market that delivered 403.5% returns from the 2009 financial crisis low. Credit markets reflected extreme complacency: investment-grade credit default swap spreads traded near decade lows at approximately 50 basis points annually.
What this meant: Investors were paying just 0.5% per year to insure against corporate bond defaults — pricing that assumed essentially zero probability of systemic credit stress over the next five years.
Meanwhile, COVID-19 was spreading beyond China. The WHO declared it a Public Health Emergency on January 30, 2020, yet U.S. equity markets gained 3.2% in January and continued climbing into mid-February. Credit spreads remained compressed.
The Asymmetry Ackman Identified
Bill Ackman recognized a critical disconnect: containing COVID-19 in Western countries would require economic lockdowns similar to China’s response. Yet credit markets priced zero probability of this scenario.
The key insight wasn’t predicting a pandemic — it was recognizing that if lockdowns occurred, credit spreads would widen dramatically, while the cost of protection remained at historic lows. The risk/reward was profoundly asymmetric:
Downside: Premium payments of $27 million (approximately 0.3% of Pershing Square’s ~$8 billion AUM)
Upside: Potentially unlimited as spreads widened to distress levels
Catalyst timing: Uncertain but increasingly probable
On February 27, 2020 — one day before the WHO raised COVID-19’s threat to “very high” — Ackman sent his entire team to work from home. This wasn’t panic; it was signal recognition.
II. Trade Architecture: Why Credit Default Swaps
Instrument Selection: CDS vs. Alternatives
Ackman had multiple ways to express a bearish view: sell equities, buy put options, buy volatility, or use credit derivatives. He chose credit default swaps on major indices for strategic reasons.
Position Structure:
According to Pershing Square’s March 2020 investor letters, the hedge consisted of protection purchased on various investment-grade and high-yield credit indices, with the vast majority concentrated in North American and European investment-grade exposures (CDX IG and ITRAXX Main indices), supplemented by a smaller allocation to high-yield debt.
These index CDS contracts offered four critical advantages:
1. Defined Risk, Convex Upside
Unlike equity positions that can decline 100%, CDS premium payments were capped at $27 million total. But gains were theoretically unlimited — if IG spreads widened from 50 bps to 500 bps (a 10x move), the position’s value would multiply accordingly. This is fundamentally different from linear positions.
2. Extreme Leverage Without Margin Calls
With spreads at approximately 50 bps on investment-grade debt, Ackman’s $27 million in annual premiums purchased protection on an estimated $65–75 billion notional of credit exposure.
The calculation: $27M annual premium ÷ 0.005 spread ≈ $54B for one year of protection. Accounting for typical 5-year contract structure and present value discounting suggests total notional in the range of $65–75 billion.
This leverage — approximately 2,400–2,800:1 (notional / premium at risk) — came without forced liquidation risk. In contrast, equity short positions or leveraged derivatives face margin calls that can force exits at the worst possible times.
3. Direct Credit Exposure
Unlike equity volatility products that depend on multiple transmission mechanisms, CDS values respond directly and immediately to credit spread changes. No Greeks to manage, no implied volatility skew, no time decay concerns beyond the premium burn rate.
4. Deep Liquidity
Index CDS contracts trade in institutional markets with narrow bid-ask spreads and continuous pricing. This enabled:
Rapid position accumulation without market impact
Real-time mark-to-market transparency
Frictionless exit at optimal timing
III. The P&L Cascade: How $27M Became $2.6B
Understanding CDS Valuation
The fundamental CDS pricing relationship:
CDS Value = PV(Protection Leg) — PV(Premium Leg)
Where:
Protection Leg = (1 — Recovery Rate) × Default Probability × Notional × Discount Factor
Premium Leg = Contracted Spread × Risky Duration × Notional
When credit spreads widen, the market’s implied default probability increases, raising the protection leg’s value while the premium leg (based on the contracted spread at entry) remains relatively constant. This creates the convex payoff.
For practical P&L estimation:
ΔV ≈ CS01 × ΔSpread
Where CS01 is the dollar value change per 1 basis point spread move.
The Timeline of Returns
Late February 2020: Entry
IG spreads: ~50 bps
HY spreads: Near historic lows (~400 bps)
Total premium commitment: $27M
Estimated notional exposure: $65–75B
March 3, 2020: First Public Disclosure
Pershing Square announced in a press release: “We have acquired large notional hedges which have asymmetric payoff characteristics; that is, the risk of loss from these hedges is limited, while their potential upside is many multiples of our capital at risk.”
March 9, 2020: “Black Monday I”
S&P 500 plunged 7.6%, triggering circuit breakers
IG spreads widened from ~50 bps toward 150+ bps
HY spreads surged past 700 bps
CDS position mark-to-market: approximately $1.8 billion
Unrealized gain: ~$1.77 billion (approximately 66x return in ~10 days)
March 12, 2020: “Black Thursday”
Dow plummeted 2,352 points (-9.9%), largest point drop ever at the time
IG spreads continued widening toward 200 bps
Corporate credit lines drawn down en masse
CDS values continued appreciating
March 16, 2020: Peak Panic
S&P 500 fell 9.5% — worst daily decline since Black Monday 1987
IG spreads exceeded 200 bps; HY spreads surged above 800 bps
Credit markets approaching crisis dysfunction
March 23, 2020: Exit at the Bottom
Ackman closed the entire position, as disclosed in his March 26, 2020 investor letter:
Total proceeds: $2.6 billion
Net profit: $2.573 billion (after $27M in premiums and commissions)
Return: 9,526% (96.3x)
Critical observation: March 23 was the exact market bottom. The Fed announced unlimited QE that same week, causing spreads to begin tightening. Ackman’s exit timing was objectively optimal — to the day.
Why Returns Were So Extreme
Three technical factors amplified returns beyond linear spread widening:
1. Non-Linear Convexity
CDS don’t appreciate linearly. As default probability increases, the relationship between spread widening and CDS value follows a convex curve. This occurs because:
Higher spreads imply higher default probability (non-linear relationship)
Duration effects become more pronounced at wider spreads
Correlation risk premiums spike during systemic events
2. Liquidity Premium Explosion
During March 2020, credit markets experienced severe dislocations. Even fundamentally strong companies saw spreads widen to levels inconsistent with actual default risk due to:
Forced selling from redemptions
Dealer balance sheet constraints
Flight to quality (everyone buying Treasuries simultaneously)
Research confirms that during the COVID-19 crisis, investment-grade bonds traded at significant discounts to their CDS-implied values, creating temporary liquidity-driven mispricings.
3. Systemic vs. Idiosyncratic Risk
By using broad credit indices rather than single-name CDS, Ackman captured correlation risk repricing. During panics, correlations between credits spike toward 1.0, causing index spreads to widen more than the weighted average of individual constituents would suggest.
Academic research confirms that CDS index activity nearly doubled during March 2020, with US and European investment-grade indices seeing the most dramatic movements — exactly where Ackman’s exposure was concentrated.
IV. The Controversy: Ethics, Timing, and Market Impact
The CNBC Interview
On March 18, 2020 — five days before markets bottomed — Ackman appeared on CNBC in what became one of the most controversial hedge fund interviews in history.
With visible emotion, he warned: “Hell is coming” and called for an immediate 30-day national economic shutdown. He stated hotels were “going to zero” and warned America could “end as we know it” without intervention.
What happened next:
Markets dropped sharply during and after the segment
Within days, news broke that Ackman had already exited his CDS hedge at peak value
He was simultaneously buying equities — including hospitality stocks he’d just warned about
The accusation: “Talking his book” — using public platforms to manipulate markets while taking the opposite position privately.
Ackman’s Defense
In his March 26, 2020 investor letter, Ackman addressed the controversy directly:
“At the time of my CNBC interview, we had already sold our hedges. My interests were 100% aligned with a rapid market recovery… Some have questioned whether my warnings were in my fund’s interests. The answer is unequivocally yes — I wanted markets to recover, not fall further.”
The timeline defense:
CDS position fully exited by March 23
CNBC interview on March 18 (5 days before final exit)
By interview date, already net long equities
Financial incentive was for markets to rise, not fall
Ethical Analysis for Traders
This raises important questions about information asymmetry and public statements:
What was legal:
CDS positions don’t require real-time public disclosure
Expressing views on CNBC, even extreme ones
Redeploying capital after exiting hedges
What troubled critics:
Timing of public warnings relative to private positioning
Emotional intensity of warnings while repositioned
Advocating government action that would benefit his portfolio
The trader’s perspective: Ackman’s actions were legal but highlight the advantage sophisticated investors have in capital markets. He could build positions quietly, exit without disclosure requirements, redeploy capital before public awareness, and use his platform to advocate policies benefiting his positions.
V. Capital Redeployment: The Complete Two-Phase Strategy
The brilliance of Ackman’s approach extended beyond the CDS trade to how he redeployed the proceeds. On March 23, 2020 — the exact market bottom — Pershing Square invested $2.3 billion of the $2.6 billion hedge proceeds into equities at generational valuations.
Positions Initiated and Increased
According to his March 26, 2020 investor letter, Pershing Square added to existing investments and initiated new positions:
Increased Holdings:
Agilent Technologies
Berkshire Hathaway
Hilton Worldwide (hospitality sector, devastated by travel shutdown)
Lowe’s (home improvement)
Restaurant Brands International (fast food chains facing closure pressures)
New Position:
Starbucks (reestablished position sold in January)
The Complete Strategy Return
Phase 1 (Hedge): $27M → $2.6B = +9,526% return
Phase 2 (Redeployment): $2.3B invested at March 23 bottom
Full Year 2020: Pershing Square returned +70.2% — its best year ever
The strategy: Without the CDS hedge, Pershing Square’s equity portfolio would have declined with the market (-34% at worst). Instead:
Protected capital with minimal cost hedge
Generated massive liquidity at the bottom
Redeployed into quality assets at maximum discount
This two-phase approach delivered returns impossible through either “buy and hold” or “go to cash” strategies.
VI. Technical Deep Dive: The Mathematics of CDS Profitability
CS01 and Spread Sensitivity
CS01 (Credit Spread 01) measures dollar P&L change per 1 basis point spread move:
CS01 = Notional × Risky Duration × 0.0001
For an estimated position of this size:
Estimated notional ≈ $70 billion
Typical duration ≈ 4.5 years (for 5-year CDS)
CS01 ≈ $31.5 million per basis point
Implications:
50 bps widening ≈ 50 × $31.5M ≈ $1.575 billion gain
100 bps widening ≈ 100 × $31.5M ≈ $3.15 billion gain
The actual realized gain of $2.6B suggests an effective spread widening capture of approximately 82–85 bps across the portfolio, weighted by notional and accounting for entry/exit timing.
Verification check:
IG spreads widened from ~50 bps to peak ~200 bps = 150 bps move
HY spreads widened from ~400 bps to peak ~850 bps = 450 bps move
Weighted average (given predominantly IG concentration): ~160 bps
Accounting for convexity effects and timing: 80–85 bps effective capture is reasonable
Time Decay Analysis
CDS premiums represent ongoing costs. For a $27 million annual premium:
Daily Premium ≈ $27M / 365 ≈ $74,000 per day
Over the approximately 30-day trade duration:
Total time elapsed premium cost: ~$2.22 million
Plus transaction costs and commissions
Total outlays: approximately $27 million (as stated)
Risk analysis: If spreads had remained unchanged for 90 days, the position would have incurred approximately $6.75M in premium payments before facing pressure to exit.
Present Value Mechanics
The protection leg value increased as spreads widened, calculated as:
PV(Protection) = (1 — Recovery Rate) × ∑[PD(t) × DF(t)]
Where:
Recovery Rate ≈ 40% for investment-grade bonds
PD(t) = probability of default at time t (derived from spreads)
DF(t) = discount factor at time t
As spreads widened from 50 bps to 200+ bps, implied default probabilities increased non-linearly, creating the convex appreciation in CDS values.
VII. Replicable Framework: Systematic Principles for Quants and Traders
For Quantitative Researchers
1. Asymmetric Payoff Identification
Build systematic frameworks to identify instruments offering:
Capped downside (premium, option cost, defined loss)
Convex upside (exponential gains as market moves)
Positive expectancy despite negative carry
Signal framework:
IF (implied_volatility < historical_volatility_percentile_20) AND
(credit_spreads < historical_percentile_15) AND
(fundamental_risk_indicators > threshold)
THEN asymmetric_opportunity_signal = TRUE2. Convexity Capture Models
Returns followed a convex curve, not linear. Model this:
Expected Value = Probability(Event) × Convex_Payoff(Spread) — Premium
Where Convex_Payoff grows exponentially with spread widening due to:
Non-linear default probability relationship
Duration amplification effects
Correlation risk premiums
3. Cross-Asset Divergence Signals
The trade exploited mispricing between markets:
Credit market slow to price COVID risk
Equity market pricing gradually then violently
CDS market offering protection at multi-year lows
Systematic detection:
Monitor correlation breakdowns between typically correlated assets
Identify lead-lag relationships (which market prices risk first)
Structure positions in lagging markets
4. Position Sizing for Asymmetry
Kelly Criterion for asymmetric bets:
f = (p × b — q) / b*
Where:
p = probability of success
q = 1 — p
b = odds received (upside/downside ratio)
For a hypothetical setup similar to Ackman’s trade:
p = 0.4 (40% probability of credit crisis)
b = 96 (observed return ratio)
q = 0.6
f = (0.4 × 96–0.6) / 96 = 37.8 / 96 ≈ 0.39%*
Ackman risked ~0.34% of capital — almost exactly Kelly optimal for this risk/reward profile.
For Discretionary Traders
1. Instrument Selection Decision Framework
Instrument Max Risk Leverage Time Decay Liquidity Best For CDS Index Premium Extreme Moderate Good Systemic credit events Put Options Premium High High Excellent Short-term moves Short Stock Unlimited Moderate None Excellent Sustained downtrends VIX Calls Premium High High Good Volatility spikes
Key insight: Ackman’s genius wasn’t predicting COVID severity — many did. His edge was choosing CDS over alternatives for optimal asymmetry.
2. Opportunity Recognition Checklist
Use this framework to evaluate potential asymmetric setups:
✅ Is downside risk clearly defined and acceptable?
Can you survive maximum loss 10 times in a row?
✅ Is upside convex (exponential, not linear)?
Does payoff accelerate as market moves?
✅ Is the catalyst identifiable?
Can you articulate what needs to happen?
✅ Is timing flexible?
Can you afford time decay or carry costs?
✅ Is liquidity sufficient?
Can you exit at mark-to-market prices?
✅ Is counterparty risk manageable?
Are you exposed to counterparty failure?
If YES to all six: Potential asymmetric opportunity.
3. Exit Discipline Protocol
Define exit conditions before entry:
Profit targets:
Scale out at 5x, 10x, 20x returns
Never let convex gains reverse to losses
Time stops:
Exit after 90 days if no movement (avoid excessive carry burn)
Catalyst stops:
Exit if fundamental thesis invalidated
Exit if policy response changes market structure (Fed intervention)
Ackman’s discipline: Exited March 23 — the exact day the Fed announced unlimited QE, signaling spread widening was over.
4. Two-Phase Strategy Execution
Most traders think: hedge OR go long. Professional capital does both sequentially:
Phase 1: Protect
Buy asymmetric hedge when risk is mispriced
Capture windfall when dislocation occurs
Phase 2: Attack
Redeploy hedge proceeds into discounted assets
Double-down when maximum fear creates opportunity
Allocation approach: Dedicate 0.5–2% of capital to tail-risk hedges continuously. When they pay off, aggressively redeploy proceeds into core holdings at depressed prices.
VIII. Risk Analysis: What Could Have Gone Wrong
Every trade has failure modes. Understanding these prevents overconfidence:
Scenario 1: Rapid Policy Response
Risk: If governments/central banks acted immediately (mid-February), credit spreads might have remained compressed.
Impact: Premium eroding while spreads unchanged. After 3–6 months, pressure to exit with losses.
Mitigation: Time-defined stop loss. Accept loss if spreads unchanged after 60–90 days. At $27M risk (0.34% capital), this was an acceptable outcome.
Scenario 2: No Economic Lockdowns
Risk: If COVID remained Asia-focused or governments chose herd immunity strategies, systemic credit stress might not materialize.
Impact: Modest equity volatility without credit crisis. CDS generates minimal profit while premium erodes.
Actual probability in February 2020: Moderate (30–40%). This was the consensus view.
Why the trade still made sense: Risk was capped at 0.34% capital. If wrong, try again. Asymmetric bets allow multiple attempts.
Scenario 3: Counterparty Default
Risk: Protection sellers (major banks) fail during crisis, making CDS contracts worthless despite favorable marks.
Impact: Billions in theoretical gains evaporate if counterparties can’t pay.
Mitigation:
Transacting with multiple counterparties (diversification)
ISDA Credit Support Annexes requiring collateral posting
Daily variation margin
Exit before peak systemic stress
Scenario 4: Forced Liquidation Timing
Risk: If Pershing Square faced redemption requests, forced to liquidate at suboptimal timing.
Impact: Exiting March 9 ($1.8B) vs March 23 ($2.6B) = $800M foregone.
Why this didn’t happen: Pershing Square Holdings’ closed-end structure prevented forced redemptions, giving Ackman complete control over exit timing.
Lesson: Fund structure matters. Open-ended vehicles face redemption risk at the worst times.
Scenario 5: Regulatory Intervention
Risk: Emergency regulations freeze CDS markets or mandate position unwinding.
Impact: Forced exit at artificially low prices.
Actual outcome: CDS markets remained functional throughout March 2020.
Key insight: Political risk is unquantifiable and unhedgeable. Accept it as part of the game.
IX. Historical Context: Ackman’s Pattern of Asymmetric Trades
The COVID trade wasn’t isolated — it reflected a systematic approach:
MBIA Short (2002–2008)
Setup: Bond insurer MBIA had massive exposure to subprime mortgages but maintained AAA rating.
Position: Bought CDS on MBIA credit while shorting equity.
Outcome: MBIA’s credit collapsed during 2008 financial crisis, generating substantial profits.
Pattern: Identify systemic risk mispriced by rating agencies; use CDS for convex exposure.
Post-COVID Interest Rate Hedges (2020–2022)
Setup: December 2020. Fed maintains near-zero rates despite massive fiscal stimulus.
Thesis: Inflation will surge, forcing Fed to hike rates aggressively.
Position: Purchased interest rate hedges (likely swaptions/Treasury options) for $384 million.
Outcome: Closed 2022–2023, generating $2.7 billion profit.
Return: 7x return (approximately 603% gain)
Pattern: Recognize mispriced volatility; structure convex payoff through derivatives.
The Common Thread
Ackman’s highest-returning trades share characteristics:
Identify underpriced tail risk (credit crisis, interest rate volatility)
Use derivatives for convexity (CDS, swaptions, options)
Size for asymmetry (small downside, massive upside)
Exit when repricing occurs, not when event fully plays out
Redeploy proceeds into fundamental positions
Track record across these trades:
MBIA short: Substantial profits
COVID CDS: $2.573B profit on $27M
Interest rate hedges: $2.7B profit on $384M
Total asymmetric gains (documented): $5+ billion
This isn’t luck — it’s a replicable systematic strategy.
X. Implementation Guide: Practical Applications
What Retail Traders Can Replicate
While institutional CDS trading isn’t accessible, the principles are:
Accessible instruments offering asymmetric payoffs:
Credit ETF put options (HYG, LQD) — express credit spread widening views
Volatility products (VXX calls, VIXY) — capture vol spikes
Deep OTM puts on equity indices during complacency
Structured notes with embedded credit protection (if accredited)
What you can replicate:
Asymmetric position structuring (defined risk, convex upside)
Optimal sizing (0.3–0.5% capital risk for 10–100x potential)
Exit discipline frameworks (profit targets, time stops, catalyst stops)
Two-phase strategies (hedge then redeploy proceeds)
Execution Protocol for Traders
Step 1: Opportunity Identification
Monitor for setups meeting asymmetric criteria:
Volatility compression (VIX < 20th percentile)
Credit spread compression (near historic tights)
Cross-asset divergence (correlation breakdowns)
Catalyst on horizon (geopolitical, policy, economic)
Step 2: Position Construction
Define parameters before entry:
Maximum acceptable loss (dollar amount)
Time horizon (decay tolerance)
Profit targets (scale out levels: 5x, 10x, 20x)
Catalyst invalidation (what proves you wrong)
Step 3: Execution
Enter positions during liquidity (avoid gaps)
Document thesis and exit conditions in writing
Set alerts for profit targets and stop conditions
Review position daily (asymmetric trades move fast)
Step 4: Exit Management
Scale out as profits accumulate (never all-at-once)
Move stops to protect gains once >5x return
Exit immediately if catalyst invalidated
Don’t let greed reverse convex gains
Step 5: Capital Redeployment
When asymmetric hedge pays off:
Redeploy 80–90% of proceeds into quality assets
Maintain 10–20% for next opportunity
Size core positions aggressively when assets are discounted
XI. Lessons for Building Systematic Strategies
Core Principles
1. Volatility ≠ Risk
Traditional risk management focuses on volatility. But asymmetric positions can be “high volatility, low risk.”
Ackman’s position:
Volatility: Extreme (daily mark swings of hundreds of millions)
Risk: Minimal (maximum loss capped at $27M)
Better risk metrics:
Focus on worst-case loss (downside risk)
Value-at-Risk at extreme quantiles (99.9%)
Maximum drawdown in crisis scenarios
2. Carry Costs Are Strategy Tax
Asymmetric positions often have negative carry:
CDS: Pay premiums while waiting
Options: Theta decay
Volatility: Contango bleed
The trade-off: Accept negative carry for convex payoff. Position size must account for surviving multiple false starts.
3. Market Microstructure Creates Alpha
Returns came from temporary mispricing between:
Credit market (slow to price COVID risk)
Equity market (declining but not crashed)
Rates market (Treasuries pricing flight to quality)
Systematic opportunity: Monitor cross-asset correlation breakdowns. When typically correlated markets diverge, one is mispricing risk.
4. Exit Discipline Is the Strategy
Entry is 20% of the trade. Exit is 80%.
Ackman’s precision:
Could have exited March 9 ($1.8B) — left $800M on table
Could have held through April — risked reversal when Fed intervened
Exited March 23 — exact day Fed announced unlimited QE
That’s discipline, not luck.
XII. Addressing Common Critiques
“This Trade Was Just Lucky Timing”
Counterargument:
Examine the precision:
Started accumulating late February (before pandemic declared)
Sized at 0.34% capital (optimal for asymmetric bet via Kelly)
Chose CDS over alternatives (optimal instrument for credit events)
Exited March 23 (exact market bottom)
Probability analysis: If each decision was random 50/50, odds of getting all correct = 0.5⁴ = 6.25%. But these weren’t random — each followed from systematic analysis.
Verdict: Skill + favorable circumstances. The framework was identifiable prospectively.
“CDS Trading Isn’t Accessible”
True, but principles are:
Retail traders can access similar asymmetry through:
Credit ETF options (HYG puts, LQD puts)
Volatility products (VXX calls during compression)
Deep OTM index puts (SPY, QQQ)
What’s replicable:
Asymmetric structuring (defined risk, convex upside)
Optimal sizing (0.3–0.5% risk for high multiple potential)
Exit discipline (profit targets, catalyst monitoring)
Two-phase approach (protect then attack)
“This Analysis Uses Hindsight”
Partially true for magnitude, false for framework:
Knowable in advance:
CDS spreads at historic lows (observable)
COVID spreading globally (observable)
Lockdowns probable based on China response (inferable)
Asymmetric payoff structure (mechanical)
Unknowable in advance:
Exact spread widening magnitude (turned out to be ~150–200 bps IG)
Precise bottom timing (March 23)
Fed response speed and scale (unlimited QE)
Verdict: Trade thesis was identifiable prospectively. Exceptional execution required skill AND favorable circumstances.
Conclusion: From Case Study to Systematic Edge
Bill Ackman’s COVID credit default swap trade demonstrates that exceptional returns don’t require predicting the future — they require exploiting structural mispricings with asymmetric position architecture.
Key Takeaways for Quantitative Researchers
Build early warning systems for volatility compression and spread anomalies
Develop convexity capture models identifying exponential payoff opportunities
Create dynamic position sizing using Kelly Criterion adapted for asymmetry
Monitor cross-asset correlations for microstructure arbitrage signals
The infrastructure exists to systematize this approach. What’s needed is disciplined framework implementation.
Key Takeaways for Discretionary Traders
Instrument selection > Direction prediction — the vehicle matters more than the view
Risk 0.3–0.5% for 10–100x opportunities — asymmetric sizing enables multiple attempts
Exit at repricing, not resolution — capture gains when market adjusts
Deploy two-phase strategies — protect capital, then attack when fear peaks
The Depth You Were Missing
The interviewer said: “You lack depth in concepts.”
This analysis demonstrates depth isn’t memorizing formulas — it’s understanding how money is actually made:
Not: “Credit spreads widened due to COVID”
But: “50 bps entry at historic lows + convex CDS structure + 150 bps widening + 4.5 duration + $70B notional = $2.6B via CS01 mechanics and correlation repricing”
That’s depth.
Final Thought: Replicability
Since 2008, Ackman has generated over $5 billion from asymmetric trades. Three instances over 15 years isn’t luck — it’s systematic strategy:
Identify structural mispricing (spreads too tight, vol too low)
Structure convex exposure (derivatives, defined risk)
Size for survival + asymmetry (0.3–0.5% capital)
Exit at repricing (policy response, catalyst)
Redeploy proceeds (compounding capital)
The question isn’t whether you can predict the next pandemic. It’s whether you can build systems identifying and exploiting the next mispricing with the same precision.
The concepts are deep. The execution is learnable. The returns are possible.
Sources & References
Primary Sources
Pershing Square Capital Management, “Letter to Investors,” March 26, 2020
Pershing Square Capital Management, “Letter to Investors,” March 25, 2020
Pershing Square Holdings, Press Release, March 3, 2020
Pershing Square Holdings, Press Release, March 9, 2020
Pershing Square Holdings 2020 Annual Report
Pershing Square Holdings 2023 Letter to Shareholders
Academic Research
Siriwardane, E., Viceira, L., Xu, D., & Baker, L. (2021). “Pershing Square’s Pandemic Trade,” Harvard Business School Case Study 222–007
Journal of Alternative Investments: “CDS Market Activity During COVID-19” (2024)
SSRN: “Credit Default Swap Dynamics in Pandemic Markets” (2022)
Federal Reserve Board: “Credit Default Swaps,” FEDS Notes (2022)
Market Data
Bloomberg Terminal: S&P 500 historical data, CDS spread data
Markit (IHS Markit): CDX and ITRAXX index methodology and pricing
Federal Reserve Economic Data (FRED): Treasury yields, financial indicators
S&P Dow Jones Indices: Index constituent data
News & Analysis
Gara, A. (2020). “Bill Ackman’s 100-Fold Return On Coronavirus Hedge,” Forbes, March 25
“The $27 Million Trade That Made $2.6 Billion,” Sahi.com (2025)
CNBC Interview: Bill Ackman, March 18, 2020 (archived video and transcript)
Technical References
CFA Institute: “Credit Default Swaps” (Level III curriculum)
BIS Working Papers №181: “Explaining CDS Spreads”
Cambridge University Press: “Credit Default Swap Trading Evidence”
Author’s Note: This analysis is for educational purposes. Past performance doesn’t guarantee future results. Credit derivatives involve substantial risk including total loss. Consult qualified professionals before implementing strategies.
Disclosure: No positions in discussed securities. Analysis based on public information and academic research.
Cover photograph: Thomas J. O'Halloran, public domain, via Wikimedia Commons.