Who this is for. Readers of the cross-sectional asset-pricing literature and anyone building a factor portfolio. Assumes familiarity with regressions and long-short portfolios.
How to use it. Read the steps in order; each one assumes the previous. The “why here” line says what the step adds. Where a step is a tool, run it on your own numbers before moving on. The exemplar papers at the end are the archive’s highest-rigor papers for the path’s methods; read two of them with the checklists in hand.
Steps
- How to Evaluate a Factor-Investing Paper
A checklist for evaluating factor-investing and cross-sectional asset-pricing papers: multiple-testing hurdles, portfolio construction choices, cost realism, and the questions that expose a factor-zoo entry.
Why here: The checklist.
- How to Interpret a Factor-Zoo Paper
Reading factor-zoo and replication meta-studies: what the multiple-testing corrections mean, why replication rates differ so wildly between studies, and what survives for practitioners.
Why here: Hundreds of factors, few survivors: how to read the meta-literature.
- Alpha, Beta, and Alternative Risk Premia: The Difference That Prices Everything
The alpha/beta/ARP taxonomy: what each actually is, the regression that sorts any return stream, why the boundaries move over time, and what each category should cost.
Why here: Is it alpha, beta, or a risk premium in disguise?
- Survivorship Bias in Quantitative Finance: How Dead Companies Fake Alpha
How survivorship bias inflates backtests, how large the effect is by asset class, the five-dead-tickers test for any dataset, and how to build survivorship-clean universes.
Why here: Dead companies fake alpha.
- Cross-Sectional vs Time-Series Predictability: Two Different Claims
The difference between cross-sectional and time-series predictability in finance: what each claim means, how evidence standards differ, and why conflating them produces phantom strategies.
Why here: Two different claims that papers blur.
- Cross-Sectional Momentum vs Time-Series Momentum
The two momentum families compared: construction, crash profiles, evidence bases, and why they are different strategies that happen to share a name.
Why here: The worked example of that distinction.
- What Makes a Factor Tradable?
The gap between a published factor premium and a tradable one: implementation costs, capacity, crowding, borrow reality, and the checklist that converts paper premia into honest expectations.
Why here: From a sorted portfolio to a position you can hold.
- Why Turnover Can Destroy Factor Returns
The turnover arithmetic that decides factor profitability: signal decay vs trading cost, the rebalance-frequency trade-off, turnover-reduction techniques, and the reporting gap in academic papers.
Why here: Turnover is the hidden fee.
- Capacity Constraints in Quantitative Strategies
How to estimate a strategy's capacity: the impact arithmetic, the capacity hierarchy by strategy type, self-competition effects, and why capacity is the number papers never report.
Why here: How much money the premium can absorb.
- Portfolio Optimization: Estimation Error and Regularization
Why mean-variance optimization amplifies estimation error, the error-maximization mechanism, the regularization toolkit (shrinkage, constraints, resampling), and the 1/N benchmark that keeps everyone honest.
Why here: Combining factors without amplifying noise.
- Risk Parity, Minimum Variance, and Maximum Diversification
The μ-free allocation family compared: what risk parity, minimum variance, and maximum diversification each optimize, the leverage that makes risk parity work, and the shared failure modes.
Why here: The construction choices that need no return forecast.
Related
All paths: learning paths. Methods, with their own guides and exemplars: methods pages. Programmatic access to the papers: agents page.