Featured
Merger Arbitrage: The Risk-Return Profile of Event-Driven Strategies
The Asset Growth Anomaly: Why Rapidly Expanding Firms Underperform
Calendar Anomalies: Sell in May, the January Effect, and 300 Years of Evidence
Latest→
Crowding in Quant Strategies: Detecting and Surviving the Unwind
In August 2007, a cluster of quantitative equity funds suffered simultaneous losses so severe that the episode is now known as the 'quant quake.
Khandani and Lo (2011)
Factor Investing in Fixed Income: Value, Momentum, and Carry in Bond Markets
Can the same value, momentum, and carry factors that drive equity returns explain the cross-section of corporate bond performance?
Israel, Palhares, Richardson (2018)
Financial Network Contagion: How Interconnectedness Amplifies Systemic Risk
Why did the collapse of a single investment bank nearly bring down the global financial system in 2008?
Acemoglu, Ozdaglar & Tahbaz-Salehi (2015)
Intermediary Asset Pricing: How Dealer Balance Sheets Drive Returns
He, Kelly, and show that the health of primary dealer balance sheets predicts risk premia across equities, bonds, currencies, commodities, and options.
He, Kelly, Manela (2017) 'Intermediary Asset Pricing: New Evidence from Many Asset Classes'
Insider Trading Signals: When Corporate Insiders Predict Returns
Comprehensive analysis of all insider trades on the NYSE, AMEX, and Nasdaq from 1975 to 1995 revealed that aggregate insider purchasing predicts market-wide returns and that insider buys in small…
Lakonishok & Lee (2001), Review of Financial Studies
Factor Investing→
The Asset Growth Anomaly: Why Rapidly Expanding Firms Underperform
The Share Buyback Anomaly: When Repurchases Signal Undervaluation
The Accruals Anomaly: Why Earnings Quality Predicts Stock Returns
The Short of It: Why Anomaly Profits Come from the Short Side
Portfolio Construction→
Minimum Variance Portfolios: Less Risk, No Less Return
Clarke, de Silva, and Thorley showed that minimum variance portfolios in U.S. equities delivered market-like returns with roughly 25% less volatility.
Clarke, de Silva & Thorley (2006), The Journal of Portfolio Management
Optimal Retirement Withdrawal Strategies: Beyond the 4% Rule
The 4% rule was derived from US-only data during the best century for equities. International evidence suggests 3.0-3.5% is safer, while dynamic withdrawal strategies improve outcomes by 15-30%.
Bengen (1994), Journal of Financial Planning
Sequence-of-Returns Risk: Why Order Matters More Than Average
Two portfolios with identical average returns can produce dramatically different outcomes when withdrawals are involved. The order of returns, not the average, determines whether a retirement portfolio survives 30 years.
Bengen (1994), Journal of Financial Planning
When Monte Carlo Fails: The Hidden Pitfalls of Retirement Simulations
Standard Monte Carlo retirement simulations assume normal distributions, constant correlations, and independent returns. All three assumptions are wrong.
Pfau (2010), Financial Analysts Journal
Dividend Irrelevance vs Dividend Investing: What the Data Shows
Miller-Modigliani proved dividends are irrelevant to firm value, yet dividend strategies manage trillions. The resolution lies in behavioral biases, tax considerations, and the hidden quality tilt embedded in dividend-paying stocks.
Miller & Modigliani (1961) / Hartzmark & Solomon (2019)
Systematic Strategies→
Crowding in Quant Strategies: Detecting and Surviving the Unwind
In August 2007, a cluster of quantitative equity funds suffered simultaneous losses so severe that the episode is now known as the 'quant quake.
Khandani and Lo (2011)
Insider Trading Signals: When Corporate Insiders Predict Returns
Comprehensive analysis of all insider trades on the NYSE, AMEX, and Nasdaq from 1975 to 1995 revealed that aggregate insider purchasing predicts market-wide returns and that insider buys in small…
Lakonishok & Lee (2001), Review of Financial Studies
Merger Arbitrage: The Risk-Return Profile of Event-Driven Strategies
Landmark 2001 study reveals that merger arbitrage returns resemble those of selling put options on the market — delivering steady gains in calm periods but suffering sharp losses during downturns.
Mitchell and Pulvino (2001)
The Overnight Return Anomaly: Where Stock Gains Actually Happen
Most of the equity premium accumulates when markets are closed.
Lou, Polk, and Skouras (2019)
Post-Earnings Announcement Drift: The Market's Slow Reaction to News
When companies report earnings surprises, prices should adjust immediately if markets are efficient.
Bernard & Thomas (1989) 'Post-Earnings-Announcement Drift: Delayed Price Response or Risk Premium?'
Behavioral Finance & Timing→
Calendar Anomalies: Sell in May, the January Effect, and 300 Years of Evidence
Two of the most durable puzzles in finance — the 'Sell in May and Go Away' Halloween effect and the January Effect — have resisted 300 years of scrutiny.
Bouman and Jacobsen (2002) 'The Halloween Indicator, Sell in May and Go Away: Another Puzzle'
Lottery Stocks and the MAX Effect: Why Investors Overpay for Skewness
Bali, Cakici, and show that stocks with the highest maximum daily returns over the prior month earn significantly lower future returns.
Bali, Cakici, and Whitelaw (2011)
From Euphoria to Panic: Quantitative Rules for Surviving Bull-to-Bear Transitions
Bull-to-bear transitions are where investor psychology becomes most destructive and where simple quantitative rules add the most value.
Moreira & Muir (2017), 'Volatility-Managed Portfolios', Journal of Finance
Overconfidence, Trading Volume, and Returns: Why More Trading Means Lower Performance
Landmark study of 66,465 household brokerage accounts revealed that the most active traders underperformed the least active by 7.1 percentage points annually.
Barber & Odean (2000), Journal of Finance
Momentum Crashes: Why Winners Become Losers Overnight
Revealed that momentum crashes are not random events but predictable consequences of market structure.
Daniel & Moskowitz (2016)
Risk & Measurement→
Financial Network Contagion: How Interconnectedness Amplifies Systemic Risk
Why did the collapse of a single investment bank nearly bring down the global financial system in 2008?
Acemoglu, Ozdaglar & Tahbaz-Salehi (2015)
Funding Liquidity and Market Liquidity: How Margin Spirals Amplify Crises
Foundational model reveals how funding constraints and market illiquidity feed on each other in a destabilizing loop.
Brunnermeier and Pedersen (2009)
The Idiosyncratic Volatility Puzzle: Why Risky Stocks Earn Less
Standard finance theory predicts that bearing more risk should deliver higher returns.
Ang, Hodrick, Xing & Zhang (2006), The Journal of Finance
Expected Shortfall: Why VaR Doesn't Tell the Whole Story
What happens when the risk metric regulators trusted for decades ignores the very losses that matter most?
Acerbi & Tasche (2002), Journal of Banking & Finance
Copulas and Tail Dependence: Why Correlations Lie in Crises
Linear correlation assumes joint normality and breaks down catastrophically during market crises.
Patton (2006), 'Modelling Asymmetric Exchange Rate Dependence', International Economic Review
Models & Frameworks→
Intermediary Asset Pricing: How Dealer Balance Sheets Drive Returns
He, Kelly, and show that the health of primary dealer balance sheets predicts risk premia across equities, bonds, currencies, commodities, and options.
He, Kelly, Manela (2017) 'Intermediary Asset Pricing: New Evidence from Many Asset Classes'
Limits to Arbitrage: Why Market Mispricings Can Persist Indefinitely
Paper overturned the assumption that rational arbitrageurs would quickly eliminate mispricings.
Shleifer and Vishny (1997)
The Heston Model: Stochastic Volatility and Why It Matters
The Black-Scholes model assumes constant volatility, producing systematic pricing errors and failing to explain the volatility smile.
Heston (1993), 'A Closed-Form Solution for Options with Stochastic Volatility', Review of Financial Studies
GARCH Models: Forecasting Volatility in Practice
GARCH(1,1) captures over 90% of conditional variance dynamics with just three parameters.
Bollerslev (1986), 'Generalized Autoregressive Conditional Heteroskedasticity', Journal of Econometrics; Hansen & Lunde (2005), Journal of Applied Econometrics
The Black-Scholes Model: How Options Are Really Priced
The Black-Scholes model gives options a closed-form price from five inputs, yet only one, volatility, truly matters.
Black & Scholes (1973), Merton (1973)
QD Research Originals→
Liquidity-Adjusted Momentum: How the Amihud Ratio Transforms Position Sizing
Quant original backtest shows that adjusting momentum position sizes by the Amihud illiquidity ratio improves the Sharpe ratio from 0.55 to 0.
Quant Decoded Research
Factor Crowding Index: Real-Time Measurement of Overcrowded Trades
A composite crowding index combining short interest concentration, ETF flow intensity, and pairwise factor correlation provides 2-4 weeks advance warning of factor dislocations. Crowding-adjusted portfolios improve Sharpe ratios by 0.12-0.
Quant Decoded Research
Sharpe Ratio Pitfalls: Why a Sharpe Above 2.0 Should Make You Suspicious
The Sharpe ratio is the most cited performance metric in finance, yet it is routinely gamed, inflated, and misunderstood.
Quant Decoded Research
Post-Earnings Announcement Drift by Market Cap: Size Matters
Quant original backtest (2000-2025) shows that post-earnings announcement drift in micro and small caps is approximately 3x larger than in mega caps and persists 60+ days versus ~20 days.
Quant Decoded Research
Options Skew as a Crash Predictor: What the VIX Term Structure Really Tells Us
Quant original backtest (2006-2025) examines whether combining the CBOE SKEW index with VIX term structure slope predicts equity drawdowns.
Quant Decoded Research