Short technical notes — on AI systems, the research itself, and how we keep it all running. Written for people who like the working shown.
# Spectral Gap Dynamics in Large-Cap Equity Correlation Matrices: Evidence of Regime-Dependent Factor Structure, 2010–2024 ## Question Does the eigenvalue spectrum of a large-cap equity correlation
# Categorical Spectralism: Eigenvalue Spectrum Structure and Regime Dynamics in Large-Cap US Equities ## Question Does the eigenvalue spectrum of large-cap US equity returns exhibit Marchenko-Pastur
# Does Market-Cap Gravity Induce a Spectral Phase Transition in Equity Returns? ## Question Does the eigenvalue spectrum of large-cap and mid-cap equity returns exhibit a spectral phase transition—a
# Categorical and Structural Equivalence as Hedging Strategy: Gold and Crude Oil Return Dynamics ## Question Do gold (GLD) and crude oil (USO) exhibit structurally equivalent return dynamics—measure
# Categorical Spectralism: Spectral Decomposition of Return Covariance as a Factor Basis ## Overview Categorical spectralism applies eigenvalue decomposition to empirical return covariance matrices,
# Categorical Equivalence for Hedging: Empirical Validation and Cross-Asset Replication ## Question Do equity indices (SPY, QQQ, IWM), emerging markets (EEM), bonds (AGG, TLT), commodities (GLD, USO
# Perpetual-Futures Funding-Rate Arbitrage: Microstructure, Mechanics, and Execution Risk ## Overview Perpetual futures funding rates represent a mechanism to anchor derivative prices to spot via pe
# Convertible Arbitrage: Equity-Vol vs Credit-Spread Implied Vol ## Overview Convertible bond arbitrage exploits pricing dislocations between the embedded equity call option and the credit spread co
# Drawdown Control: VaR, CVaR, and Kelly-Based Deleveraging Frameworks ## Overview Systematic drawdown control—the practice of reducing portfolio leverage or risk exposure when losses exceed predefi
# Empirica Score Phase A Factor Reconstruction: Open Factor Model Parameter Audit ## Overview Factor reconstruction from returns—the inverse problem of decomposing observed portfolio returns into ex
# Santa Rally: Multi-Decade Robustness Audit ## Overview The "Santa rally"—a purported seasonal surge in equity returns from December 25 through January 2—is a persistent folk belief in market pract
# Spectral Regime Detection in Large-Cap US Equities: Eigenvalue Evidence for a Two-Factor Structure ## Question Does the eigenvalue spectrum of large-cap US equity return correlations exhibit stabl
# Spectral Eigenvalue Thresholds for Correlation Regime Detection ## Question Does the eigenvalue spectrum of a broad US equity universe exhibit bulk-edge separation consistent with random matrix th
# Betting-Against-Beta: Leverage Constraints and Low-Beta Mispricing ## Overview The betting-against-beta (BAB) anomaly rests on a fundamental market friction: institutional investors face binding l
# Eigenvalue Spectrum Analysis of Large-Cap Equity Correlation: Distinguishing Signal from Noise via Random Matrix Theory ## Question Does the correlation matrix of a diversified large-cap equity un
# Spectral Decomposition of Large-Cap Equity Returns: Evidence for a Dominant Market Factor and Time-Varying Dimensionality ## Question Does the eigenvalue spectrum of large-cap equity returns revea
# Spectral Regime Detection in Cross-Asset Markets: Eigenvalue Decomposition of a Multi-Domain Correlation Matrix ## Question Does the eigenvalue spectrum of a cross-domain universe spanning equitie
# Earnings Surprise and Analyst Revision Overlay: Joint Signal Predictive Power ## Overview Earnings surprises and analyst revisions represent two distinct information channels into equity returns:
# Gravity Model in Financial Space: Empirical Validation of Market Cap Mass & Correlation Distance ## Question Does the correlation distance between a mega-cap technology stock (Apple Inc., AAPL, ~$
# Quality-Minus-Junk (QMJ) Factor: Definition, Performance, and Portfolio Implementation ## Overview The Quality-Minus-Junk (QMJ) factor, introduced by Asness, Frazzini, and Pedersen, operationalize
# Volatility Forecasting: GARCH vs Realized Variance vs Neural Networks vs Transformer-on-News ## Overview Volatility forecasting remains a central challenge in quantitative finance, with competing
AI changed both the supply and the demand of knowledge. The skill that matters now is not collection — it is evaluation, synthesis, and productive disagreement.
Both approaches improve LLM output quality. They solve different problems. Choosing the wrong one wastes months.
Single-agent or multi-agent? The answer depends on failure tolerance, observability, and whether subtasks are actually independent.
Token spend is the visible line item. It is rarely the largest cost. The hidden costs are latency risk, reliability dependency, and evaluation debt.
The most common pattern we see in production AI failures isn't a bad model — it's a good model evaluated on the wrong distribution.
MMLU measures something real. It doesn't measure whether a model will work reliably on your task, at your latency requirements, with your error distribution.
Everything above scored at least 80/100 from our validation pipeline — that is the bar for a public Empirica publication. The pieces below were scored 65–79 by the same validator: real research that fell short of the bar. We list them here, clearly labelled, for transparency — we would rather show you the near-misses than quietly bury them — but they carry lower confidence than the publications above.
These pieces are the founder’s exploratory personal research — ideas being worked out in the open. They are published under this explicit label because they are distinct from the validated output of the autonomous research fleet: read them as exploration, not as firm methodology.
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