Statistical Modeling
Development and validation of statistical models for financial data analysis. Spans regression frameworks, distributional modeling, and multi-factor decomposition with emphasis on out-of-sample robustness.
Research & Methodology
Systematic, data-driven research on quantitative methodology, market structure, and screening frameworks. All published research represents historical analysis — not investment advice.
Methodology Notes
Deep dives into the assumptions, biases, and validation techniques that determine whether systematic research holds up outside the backtest.
Methodology Notes··~11 min
Sample Size and Statistical Power: When Your Backtest Doesn't Have Enough Data
Most backtests draw conclusions from fewer independent observations than the researcher assumes. This note examines why sample size is routinely overstated in financial research, how statistical power governs the reliability of any conclusion, and practical methods for determining whether a dataset can support the claims being made.
Methodology Notes··~10 min
Parameter Sensitivity: How to Tell If Your Edge Is Robust
A strategy that only works at one specific parameter setting is not a strategy -- it is a coincidence. This note examines how to distinguish genuine robustness from narrow optima, how to detect cliff effects, and how to build a parameter sensitivity workflow into the research process.
Methodology Notes··~12 min
Execution Gap: Why Your Live Results Never Match the Backtest
Backtested performance almost always exceeds live results. This note examines the structural reasons behind that gap -- slippage, market impact, fill assumptions, and timing delays -- and outlines practical approaches to modeling and measuring execution quality.
Methodology Notes··~9 min
Data Snooping: When Your Research Process Generates Its Own Evidence
How repeated interaction with the same dataset contaminates research results, why hold-out samples don't fully solve it, and practical standards for maintaining research integrity.
Methodology Notes··~9 min
Overfitting Diagnostics: When Your Backtest Learns the Past Instead of the Pattern
A backtest that fits the data too well is not a good backtest — it is a good mirror. This note covers how to detect overfitting before it costs you, with practical diagnostics that separate genuine edge from memorized noise.
Methodology Notes··~10 min
Regime Detection: Why Your Strategy Works Until It Doesn’t
Markets shift between regimes — trending vs. mean-reverting, low-vol vs. high-vol, risk-on vs. risk-off. A strategy built for one regime will underperform or fail in another. This note examines how to detect shifts and what to do about them.
Methodology Notes··~9 min
False Discovery Risk: When Your Backtest Finds Something That Isn’t There
If you test enough strategies, some will look significant by accident. False discovery risk is the reason a disciplined researcher treats statistical significance as a starting point, not a conclusion.
Methodology Notes··~8 min
Look-Ahead Bias: The Hardest Bug to Find in Your Backtest
Look-ahead bias is the use of information that was not yet available at the time a trading decision was supposedly made. It is the single most common reason a strategy that looks excellent in research fails in production.
Methodology Notes··~12 min
Transaction Cost Modeling Beyond Flat Assumptions
A flat cost assumption is better than nothing — but not by as much as most researchers assume. This article examines how transaction cost modeling affects backtest reliability and outlines practical approaches for building more realistic estimates.
Methodology Notes··10 min
Walk-Forward Validation in Systematic Research: Why One Backtest Window Is Not Enough
A strategy that looks strong in one backtest window can still fail in live trading. Walk-forward validation helps reduce false confidence by testing a system across multiple unseen periods under changing conditions.
Methodology Notes··14 min
Survivorship Bias in Systematic Research: Practical Handling, Trade-offs, and Common Failure Modes
Survivorship bias can make backtests look more stable, more profitable, and less fragile than they really are. This note outlines what the problem is, how it enters a research pipeline, and practical ways to contain it.
Market Structure Weekly
Weekly observations on volatility, breadth, rotation, and correlation. Scanner-informed, not prediction-based.
Market Structure Weekly··~3 min
Market Structure Weekly -- Week of April 13, 2026
Week of April 13 market structure analytics: VIX settling into the 18-20 transitional range, breadth showing incremental improvement, sector participation broadening modestly, and correlation regime continuing its gradual normalization toward baseline.
Market Structure Weekly··~3 min
Market Structure Weekly -- Week of April 6, 2026
Week of April 6 market structure analytics: VIX partial mean-reversion from prior week spike, breadth stabilization, sector rotation broadening tentatively, and correlation regime cooling from elevated levels.
Market Structure Weekly··~3 min
Market Structure Weekly — Week of March 30, 2026
VIX surges to 26.78 with front-end backwardation. Breadth collapses to 19% above the 50-day moving average, reversing three weeks of recovery. The cyclical rotation narrows to Energy-only dominance. Sixteen of 25 industry groups are in correction territory.
Market Structure Weekly··~3 min
Market Structure Weekly — Week of March 23, 2026
Implied-realized spread compresses for the third consecutive week. Breadth edges higher to 52%. The cyclical rotation that began two weeks ago extends into a third week, led by Energy follow-through. Single-stock dispersion remains elevated.
Market Structure Weekly··~3 min
Market Structure Weekly — Week of March 16, 2026
Volatility term structure re-steepened after last week’s flattening. Breadth stabilized but did not recover. Cyclicals showed early relative strength while Technology remained mixed. Dispersion is the theme.
Market Structure Weekly··~3 min
Market Structure Weekly — Week of March 10, 2026
Inaugural issue. Volatility term structure flattened after two weeks of compression. Breadth participation narrowed to a 3-month low. Sector rotation showed defensive tilt. This is what we observed.
Research Areas
Core research domains spanning quantitative finance, statistical inference, and systematic methodology.
Time Series Analysis
Research into temporal dependencies, regime detection, and forecasting methodologies applied to financial time series. Includes autoregressive models and non-stationary process identification.
Market Microstructure
Quantitative study of market mechanics, order flow dynamics, and liquidity measurement. Focuses on execution costs, price formation, and structural characteristics of electronic markets.
Systematic Methodology
Design and evaluation of rules-based research frameworks. Includes backtesting methodology, walk-forward validation protocols, and statistical robustness assessment.
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