Quantitative Research Desk

Systematic research for evidence-led investors.

lf0 publishes quantitative research on markets, factors, risk, allocation, and strategy design – with clear assumptions, transparent methodology, and practical takeaways.

No hype. No stock tips. Just structured quantitative research.

Independent quantitative research

Transparent assumptions

Backtest-aware analysis

Factor and allocation studies

Built for systematic investors

Featured research

Selected research notes that define the lf0 approach: hypothesis-driven, data-aware, and focused on what survives real-world implementation.

    New to lf0? Start here.

    A guided path through the core ideas behind systematic investing, quantitative testing, and research interpretation.

    What lf0 studies

    A map of the research themes covered on the site.

    Read the overview

    How to read a backtest

    Learn what a backtest can show, what it cannot prove, and where false confidence enters.

    Learn the framework

    Core factor concepts

    A plain-English guide to value, momentum, quality, carry, trend, and diversification.

    Explore factors

    Portfolio construction basics

    How individual signals become portfolios, and why sizing, turnover, and risk control matter.

    Read the guide

    Explore by research area

    Research is organized by question, not by publication date. Use the library to find ideas by topic, asset class, method, or implementation problem.

    Factor Investing

    Evidence on persistent return drivers across markets.

    Trend & Momentum

    Time-series and cross-sectional momentum research.

    Asset Allocation

    Portfolio construction, diversification, and regime-aware allocation.

    Risk Management

    Drawdowns, volatility, correlation, tail risk, and portfolio fragility.

    Macro Regimes

    Inflation, rates, growth, liquidity, and market regime studies.

    Market Anomalies

    Academic and practitioner anomalies tested with skepticism.

    Machine Learning

    Applied ML, forecasting, feature design, and model risk.

    Implementation

    Costs, turnover, slippage, capacity, taxes, and practical constraints.

    How lf0 evaluates research ideas

    Every research note should make its assumptions visible. The goal is not to prove that a strategy works, but to understand when, why, and under what constraints it may fail.

    • Hypothesis: What economic or behavioral mechanism should explain the result?
    • Data: What data is used, what is excluded, and where bias may enter?
    • Test: How does the result behave across time, markets, parameters, and regimes?
    • Implementation: What happens after costs, turnover, liquidity, taxes, and execution limits?
    • Failure modes: What would make the idea stop working?

    Latest research

    Recent notes from the lf0 research archive.

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