Resources for Quant & Asset Pricing Research

How to learn the craft of empirical asset pricing and quantitative strategy research: data libraries, code on-ramps, backtesting discipline, free PhD-level courses, the Brazilian toolkit, and the writing craft.

1 · Start here: the craft of research

Read these before writing a line of code or a page of prose. They compound for a career.

• Writing Tips for PhD Students (John Cochrane). The standard finance paper-writing guide: one central contribution, triangular structure, results first.
• Four Steps to an Applied Micro Paper (Jesse Shapiro). The tightest description in economics of how to go from idea to paper.
• How to Give an Applied Micro Talk (Jesse Shapiro). “Unauthoritative notes” that became the authoritative guide to presenting empirical work.
• Code and Data for the Social Sciences (Gentzkow & Shapiro). Still the reference for organizing research code, data, and automation; required reading at many RA programs.
• How to Write an Effective Referee Report (Berk, Harvey & Hirshleifer, JEP 2017). What finance journals expect from a report: read it to referee, and to anticipate referees.
• Preparing a Referee Report: Guidelines and Perspectives (Berk, Harvey & Hirshleifer). The longer companion piece, with journal-specific perspective.
• Writing and Presentation Advice (Amanda Agan, collection). One page collecting the genre: Goldin–Katz JMP rules, Shapiro notes, Cochrane tips, and more.
• Structured Research Mentoring for Junior Faculty (Jesse Shapiro). Advice for the junior-faculty stage: managing a pipeline, not just a paper.
• Notes and Lectures (Jesse Shapiro, hub page). The full craft page the items above live on; new notes appear there first.

2 · Foundational courses & lecture notes

PhD-level asset pricing and empirical methods, all free.

• Asset Pricing: the full online PhD course (John Cochrane). The complete ex-Coursera class: lecture videos by module, notes, and problem sets alongside the book.
• Notes on Empirical Asset Pricing (Koijen & Van Nieuwerburgh). Topic-by-topic PhD notes on the empirical cross-section and time series, updated through 2024.
• Efficiently Inefficient: course materials (Lasse Heje Pedersen). Hedge-fund strategies (momentum, value, carry, liquidity provision) with exercises; teaching hub at lhpedersen.com/teaching.
• A Demand System Approach to Asset Pricing: course syllabus (Koijen & Yogo). Materials for the demand-system agenda; paper at SSRN, more at Yogo’s page.
• QuantEcon Lectures (Sargent & Stachurski). Open computational-economics lectures in Python and Julia: dynamic programming, time series, finance applications.
• Computational Methods for Economists (Jesús Fernández-Villaverde). A best-practices masterclass in scientific computing for economists, regardless of field.
• The Missing Semester of Your CS Education (MIT CSAIL). Shell, git, editors, data wrangling: the everyday tooling a degree assumes you picked up somewhere.
• Causal Inference: The Mixtape (Scott Cunningham). Full causal-inference book free online, with R/Stata/Python code throughout.
• Mixtape Sessions workshop repos (Cunningham, Roth, Callaway et al.). Free workshop materials: Advanced DiD (Roth), Frontiers in DiD (Callaway), machine learning, and more.
• Causal Inference for the Brave and True (Matheus Facure). Free Python causal-inference book by a Brazilian author; PT-BR translation exists.
• NBER lectures & Methods Lectures videos (NBER). Annual Summer Institute methods lectures: 2023 linear panel event studies, 2013 high-dimensional methods.
• Finance course videos & PhD notes (Luke Stein). Valuation/CAPM/risk-return course videos plus the Stanford econ-PhD core cheat sheet half the profession has a copy of.

3 · Data: factor & anomaly libraries

The free datasets serious cross-sectional research runs on.

• Kenneth French Data Library (Ken French). Where everyone starts: FF3/FF5 + momentum, US/developed/emerging, plus hundreds of sorted portfolios. Construction details here.
• Open Source Asset Pricing (Chen & Zimmermann). Data + code reproducing ~320 cross-sectional predictors, updated monthly. GitHub · CFR paper · Fed WP.
• Global Factor Data (Jensen, Kelly & Pedersen). 153 factors across 93 countries (Brazil included), free downloads; code at bkelly-lab/jkp-data.
• q-Factors Data Library (Hou, Xue & Zhang). The q and q⁵ factor series plus the “Replicating Anomalies” testing portfolios.
• Momentum portfolios & DMRS data (Kent Daniel). Daniel–Moskowitz momentum deciles with construction notes, directly useful for momentum research.
• Goyal–Welch equity-premium predictor data (Amit Goyal). The predictability workhorse dataset, updated through 2025; RFS 2024 re-examination.
• Liquidity & mispricing factors (Robert Stambaugh). Pástor–Stambaugh liquidity series and Stambaugh–Yuan mispricing factors.
• Novy-Marx data library (Robert Novy-Marx). Gross profitability and anomaly/trading-cost datasets (with Velikov).
• AQR Data Sets (AQR). Monthly-updated TSMOM, Betting-Against-Beta, Quality-Minus-Junk, HML Devil, century-of-premia data.
• GKX firm-characteristics dataset (Gu, Kelly & Xiu). The firm-characteristics panel behind the RFS 2020 ML paper; community build code at EquityCharacteristics.
• 1-minute factor returns (Saketh Aleti). Free high-frequency factor return series (no longer regularly updated, still rare and useful).

4 · Code on-ramps & reproducible practice

From raw CRSP to a factor backtest, and how to organize the project around it.

• Tidy Finance (R & Python) (Frey, Scheuch, Voigt & Weiss). The fastest way in to reproducible empirical asset pricing: WRDS access, CRSP/Compustat cleaning, factor construction, sorts, backtests. WRDS chapter · source.
• Empirical Asset Pricing via ML: code (Gu, Kelly & Xiu). Replication code for the RFS 2020 paper (hub on Xiu’s page).
• Machine Learning for Trading (3rd ed.) code (Stefan Jansen). End-to-end workflow across 27 chapters: data, features, models, backtests, costs; site at ml4trading.
• JMSLab research project template (Shapiro lab). The Gentzkow–Shapiro-lab reproducible project skeleton, with build automation.
• Data Recipes (Shapiro lab). Patterns for cleaning and organizing datasets, written as recipes.
• The Good Research Code Handbook (Patrick Mineault). Project organization, modules, documentation, testing: Python-leaning, field-agnostic.
• Python/R/Stata guides for empirical research (Sean Higgins). Practical guides for empirical workflows and managing large collaborative projects.
• Data Science for Economists (Grant McDermott). Shell, git, R, cloud, webscraping: a full course, largely language-agnostic.
• Combining Git with Dropbox (Kristoffer Bjärkefur). How to safely mix version control and synced folders on big-data projects.
• Stata–LaTeX workflows (Luke Stein). Overview of every package that gets Stata output into LaTeX cleanly.

5 · Backtesting discipline & multiple testing

The credibility toolkit. With enough backtests something always “works”; this literature is the antidote, and the spine of any honest strategy pipeline.

• …and the Cross-Section of Expected Returns (Harvey, Liu & Zhu, RFS 2016). The multiple-testing reckoning: why a new factor needs t > 3.0, not 2.0.
• A Backtesting Protocol in the Era of Machine Learning (Arnott, Harvey & Markowitz, JFDS 2019). A concrete seven-point research protocol against overfitting; adopt it wholesale.
• Backtesting (haircut Sharpe ratios) (Harvey & Liu, JPM 2015). How to haircut a Sharpe ratio for the number of strategies you tried.
• The Deflated Sharpe Ratio (Bailey & López de Prado). Corrects for selection bias, backtest overfitting, and non-normality; companion “Deflating the Sharpe Ratio”.
• Backtesting (probability of backtest overfitting) (López de Prado). PBO and minimum backtest length: how long a track record has to be before you believe it.
• Backtest Overfitting: an interactive demonstration (Bailey, López de Prado et al., LBNL). Live tool: tune parameters on random data, get a beautiful in-sample backtest, watch it fail out-of-sample. Tutorial.
• Pseudo-Mathematics and Financial Charlatanism (Bailey, Borwein, López de Prado & Zhu). The AMS Notices classic on what backtest overfitting does to out-of-sample performance.
• A Census of the Factor Zoo (Harvey & Liu). Database of 400+ published factors; check it before claiming novelty.
• The Pitfalls of Asset Management Research (Campbell Harvey). Incentives and p-hacking in practitioner research: a mirror worth looking into.
• Harvey’s research page (Campbell Harvey). One-stop index for the whole multiple-testing-in-finance literature.
• quantresearch.org (Marcos López de Prado). Lectures, seminar slides, and software in the “Advances in Financial Machine Learning” line.
• Bailey’s free paper archive (David H. Bailey). Open PDFs of the backtest-overfitting series, incl. overfitting tools.

6 · Financial machine learning

• Financial Machine Learning (survey) (Kelly & Xiu). The field survey monograph; free versions via BFI and NBER.
• Empirical Asset Pricing via Machine Learning (Gu, Kelly & Xiu, RFS 2020). The benchmark paper for ML return prediction; pair with the code and data above.
• The Virtue of Complexity in Return Prediction (Kelly, Malamud & Zhou, JF 2024). Why big models can beat small ones even with little data; seminar video.
• Deep Learning for Economists (Melissa Dell, JEL). Survey plus companion teaching site EconDL.
• ML in Economics resource list (Dario Sansone). A long-running collection of ML-in-econ papers, courses, and reading lists, more econ than finance.
• Qlib (Microsoft). Open-source platform for ML-driven quant research; takes a model from signal idea through portfolio construction to simulated execution.

7 · Backtesting engines & libraries

• vectorbt (polakowo). Vectorized NumPy/Numba backtesting: test thousands of parameter combinations fast (then deflate the Sharpe).
• zipline-reloaded (Stefan Jansen). Maintained fork of Quantopian’s event-driven backtester.
• QuantConnect LEAN (QuantConnect). The engine behind QuantConnect’s cloud platform, open-sourced; backtests and live-trades the same strategy code in Python or C#.
• Awesome Quant (Wilson Freitas). The curated mega-list of quant libraries and resources, maintained by a Brazilian quant.
• The Python Backtesting Landscape (2026) (python.financial). A current guide to choosing among the backtesting frameworks above.

8 · Replication & credibility in finance

• Replicating Anomalies (Hou, Xue & Zhang, RFS 2020). 452 anomalies re-tested under common procedures; most fail with NYSE breakpoints and value-weighting. NBER version.
• #fincap: Non-Standard Errors (Menkveld et al., JF). 164 teams, identical data, identical hypotheses, wildly different answers: researcher degrees of freedom, quantified.
• Critical Finance Review (Ivo Welch, ed.). The journal that publishes replications and re-examinations in finance, with open paper PDFs.
• Production of U.S. Rm-Rf, SMB, and HML in the Fama-French Data Library (Fama & French 2023). How “the” factors are actually produced, and how data revisions change them.

9 · Brazil: data, factors, institutions

The toolkit for taking any idea to B3.

• Empirical Finance Lab (my lab). Equity factor data for Brazil, with other Brazilian datasets to follow.
• NEFIN: Brazilian risk factors (USP). Fama–French-style factors for Brazil (Rm-Rf, SMB, HML, WML, IML) built with B3-appropriate filters; factor downloads · Python/R helper.
• B3 historical quotes (COTAHIST) (B3). Free daily price files for all listed equities since 1986; file layout · Python parser.
• Banco Central SGS time series (BCB). 18,000+ macro-financial series (Selic, CDI, FX, inflation) with a free JSON API; python-bcb · GetBCBData (R).
• SBFin: Sociedade Brasileira de Finanças (SBFin). The professional society: organizes the annual Encontro Brasileiro de Finanças (EBFin) and edits RBFin.
• RBFin: Brazilian Review of Finance (SBFin / FGV). Open access, no fees, EconLit-indexed; legacy site at FGV periodicals.

10 · Paper flow: repositories & conferences

Where new work appears first; the sources a weekly literature radar should sweep.

• SSRN Financial Economics Network (SSRN). The main working-paper firehose in finance; eJournal list for topic subscriptions.
• arXiv q-fin (arXiv). Where ML-quant preprints land (PM, TR, ST, CP subfields); about.
• NBER Asset Pricing Program (NBER). Working papers plus semiannual program-meeting papers.
• AFA Annual Meeting (American Finance Association, January). Program archive at past meetings; field-wide deadline list at the conference calendar.
• WFA Annual Meeting (Western Finance Association, June). The other top general-finance meeting; programs posted with papers.
• SFS Cavalcade North America (Society for Financial Studies, May). The RFS/RCFS/RAPS joint conference; yearly program pages with papers.
• EFA Annual Meeting (European Finance Association, August). Full programs on the year sites (e.g. EFA 2026).

11 · WRDS & data documentation

The unglamorous documentation that separates clean empirics from subtle data bugs.

• WRDS (Wharton). The platform most published empirics run on: CRSP, Compustat, IBES, TAQ, and host of the JKP Global Factor Data.
• Using the CRSP/Compustat Merged Database (WRDS Classroom). The tutorial to read before touching CCM.
• Linking CRSP with Compustat (WRDS). PERMNO/GVKEY linking done right; code examples here.
• CCM Database Guide (official) (CRSP). The reference document for the merged database.
• Fama–French portfolios & factors on WRDS (WRDS). Vendor documentation; also Pástor–Stambaugh liquidity factors.
• Teaching with WRDS (NYU Libraries). Classroom accounts and assignment patterns, useful for course design.

12 · AI-assisted research workflows

• My Claude Code Setup (Pedro Sant’Anna). How a working econometrician runs research and teaching through Claude Code: skills, agents, context management.
• Intro to Claude Code for Academics (slide deck). Concepts translate across coding agents; a gentle on-ramp for colleagues.
• VS Code Live: Agent Sessions (Microsoft VS Code team). Playlist on customizing agents and agentic coding.
• awesome-copilot (GitHub). Community-curated custom prompts and agents; patterns portable to any assistant.

13 · More lists like this one

The genre this page belongs to; each worth mining for its own specialty.

• Resources for Economists (Raul Guarini Riva). The seed of this page: short, opinionated, macro-and-technical-finance flavored.
• DiD Resources (Pedro Sant’Anna). The model methods page: a free 14-lecture DiD course, the JEL practitioner’s guide, replication code, and every package (R/Stata/Python).
• Recent Advances in Applied Micro Methods (Christine Cai). The continuously compiled annotated bibliography of frontier empirical methods; everyone’s PDF is a version behind.
• awesome-causal-inference (Matteo Courthoud). Causal-inference courses, books, libraries, and blogs in one list; ~130 pages of notes at his site.
• The DiD tracker (Asjad Naqvi). Tracks the entire DiD literature and every software package, updated continuously; plus The Stata Guide and visualization library.
• DiD resources (Jonathan Roth). Course materials plus HonestDiD, pretrends, and staggered packages.
• Resources and Public Goods (Luke Stein). Researcher tools, PhD notes, finance course videos, and the ABDC journal-quality viewer.
• Research resources (Ryan B. Edwards). Another well-kept example of the genre, strong on writing guides.