Volatility Management & Position Sizing
The empirical case for scaling exposure to volatility rather than to conviction. Anchors the entire Arsenal family's position-sizing logic.
Moreira, A. & Muir, T. (2017)
Volatility-Managed Portfolios
Journal of Finance, 72(4), 1611–1644
The empirical cornerstone for vol-targeted sizing. Scaling exposure inversely to recent realized volatility lifts risk-adjusted returns across equities, bonds, currencies, and commodities. The single most cited paper behind Arsenal BTC's three-state position map and the drawdown-management thesis.
Kelly, J. L. (1956)
A New Interpretation of Information Rate
Bell System Technical Journal, 35(4), 917–926
The original derivation of the Kelly criterion. Why position sizing isn't a detail — it's the whole game. Optimal growth-rate sizing trades off expected return against variance in exactly the way every modern risk-of-ruin calculation depends on.
Trend Following & Momentum
The three decades of evidence — across asset classes, across centuries — that trend-following carries a real, persistent edge. Anchors the 8th Rule and Arsenal BTC architectures.
Moskowitz, T. J., Ooi, Y. H. & Pedersen, L. H. (2012)
Time Series Momentum
Journal of Financial Economics, 104(2), 228–250
The trend-following equivalent of the Jegadeesh-Titman cross-sectional momentum result, generalised across 58 futures markets and multiple horizons. The academic backbone for Arsenal BTC's momentum stack and the 8th Rule's two-engine design.
Hurst, B., Ooi, Y. H. & Pedersen, L. H. (2017)
A Century of Evidence on Trend-Following Investing
Journal of Portfolio Management, 44(1), 15–29
137 years of evidence that trend-following works across centuries, asset classes, and macro regimes. The strongest available "this isn't a modern-era artifact" case for the 8th Rule's long-term edge.
Lempérière, Y., Deremble, C., Seager, P., Potters, M. & Bouchaud, J.-P. (2014)
Two Centuries of Trend Following
arXiv preprint 1404.3274
A 200-year empirical study showing trend-following's Sharpe ratio is roughly time-invariant, including pre-1900 commodity and currency data. Independent confirmation of the Hurst/Ooi/Pedersen result by a separate research group.
Jegadeesh, N. & Titman, S. (1993)
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
Journal of Finance, 48(1), 65–91 — paywalled (Wiley)
The foundational momentum paper. Established that past 3-to-12 month winners continue to outperform past losers — a result that has held up across 30 years of out-of-sample data and is the conceptual ancestor of every modern trend system.
Asness, C. S., Moskowitz, T. J. & Pedersen, L. H. (2013)
Value and Momentum Everywhere
Journal of Finance, 68(3), 929–985
Demonstrates that momentum (and value) are robust across 8 international markets and 4 asset classes. The academic case for treating momentum as a fundamental risk factor — not an equity-market anomaly.
Macro Regimes & Business Cycle
The econometric and applied lineage behind MRE v06's regime classification framework.
Ang, A. & Bekaert, G. (2002)
International Asset Allocation With Regime Shifts
Review of Financial Studies, 15(4), 1137–1187
The canonical regime-switching asset allocation paper. Shows that regime-aware portfolios meaningfully outperform unconditional ones, especially in tail events. Direct anchor for MRE v06's binary risk-on / risk-off framework.
Hamilton, J. D. (1989)
A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle
Econometrica, 57(2), 357–384
The Markov-switching econometrics paper that underlies every modern macro regime model, including MRE v06's hysteresis logic. The standard cite for "regime model" in any quant macro paper since.
Stock, J. H. & Watson, M. W. (1989)
New Indexes of Coincident and Leading Economic Indicators
NBER Macroeconomics Annual, Volume 4
The blueprint for composite leading-indicator construction. MRE v06's 26-voter design draws directly on this lineage of cross-asset, cross-indicator regime aggregation.
Liquidity & Intermediation
Why the global M2 series moves Bitcoin. The academic lineage for treating liquidity as the dominant macro driver of risk-asset prices.
Brunnermeier, M. K. & Pedersen, L. H. (2009)
Market Liquidity and Funding Liquidity
Review of Financial Studies, 22(6), 2201–2238
The model behind "liquidity spirals" — why a small funding-side shock can collapse market liquidity for risk assets in a self-reinforcing loop. The theoretical anchor for why drawdown management is a liquidity-regime question, not a momentum question.
Adrian, T., Etula, E. & Muir, T. (2014)
Financial Intermediaries and the Cross-Section of Asset Returns
Journal of Finance, 69(6), 2557–2596
Shows that broker-dealer leverage is a stronger asset-pricing factor than aggregate consumption. Establishes the empirical case for treating banking-sector liquidity as the leading macro driver — directly relevant for any Bitcoin-vs-liquidity framework.
Bitcoin & Cryptocurrency
The peer-reviewed treatment of crypto as an asset class — what factors drive its returns, what risks it carries, what makes it different.
Nakamoto, S. (2008)
Bitcoin: A Peer-to-Peer Electronic Cash System
bitcoin.org (the original whitepaper)
The genesis document. Nine pages. Read once, then never need to read again — but always cite. Defines proof-of-work, the chain structure, and the incentive design that makes everything downstream possible.
Liu, Y. & Tsyvinski, A. (2021)
Risks and Returns of Cryptocurrency
Review of Financial Studies, 34(6), 2689–2727
The canonical academic treatment of crypto as an asset class. Establishes that crypto returns are driven by momentum and investor attention — not the macro factors that explain equities. The empirical case for treating Bitcoin with its own dedicated signal stack.
Liu, Y., Tsyvinski, A. & Wu, X. (2022)
Common Risk Factors in Cryptocurrency
Journal of Finance, 77(2), 1133–1177
The follow-up that identifies three principal risk factors driving the cross-section of crypto returns: market, size, and momentum. The theoretical anchor for cross-coin systems like the Altcoin Scanner.
Performance Metrics & Forward Testing
The papers behind every Sharpe ratio you read on this site — and the statistical traps that make most published backtests worthless.
Sharpe, W. F. (1966)
Mutual Fund Performance
Journal of Business, 39(S1), 119–138 — paywalled (JSTOR)
The original derivation of the Sharpe ratio. Still the standard one-number summary for risk-adjusted return across the asset management industry, 60 years later.
Bailey, D. H. & López de Prado, M. (2014)
The Deflated Sharpe Ratio: Correcting for Selection Bias, Backtest Overfitting, and Non-Normality
Journal of Portfolio Management, 40(5), 94–107
The most important quant-finance paper of the 2010s. Demonstrates why a Sharpe ratio you optimised over many trials is almost certainly inflated, and provides the correction. Required reading before believing any backtested Sharpe — including DurdenBTC's.
Harvey, C. R., Liu, Y. & Zhu, H. (2016)
… and the Cross-Section of Expected Returns
Review of Financial Studies, 29(1), 5–68
Catalogues 316 published "anomalies" in asset pricing and argues that, accounting for multiple-testing bias, the bar for a real discovery should be t > 3 — not t > 2. The statistical foundation for why DurdenBTC runs seven independent forward tests, not one.
Kahneman, D. & Tversky, A. (1979)
Prospect Theory: An Analysis of Decision Under Risk
Econometrica, 47(2), 263–292 — paywalled (JSTOR)
The behavioural foundation for why drawdown management matters more than expected return. Establishes that humans feel losses roughly 2× as much as equivalent gains — the cognitive bias that turns drawdowns into capitulation and capitulation into permanent capital impairment.
Why a curated list? Quant finance has produced thousands of papers. These 18 are the ones that directly shape what's published on this site — nothing more, nothing aspirational. If a paper is here, the methods on DurdenBTC actually depend on it. If a paper is missing, it's not a slight; it's that those methods aren't part of what's in production yet.
Spotted a missing cornerstone? Reach out on X — this list is maintained.