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Reading list — the ten anchor papers

Two halves, both required:

  • Five classics — the empirical-finance papers that defined the field's methods (Weeks 5 and 6).
  • Five Gao papers — Prof. Gao's own publications, which thread the program's theme of fair lending, innovation, and rigorous identification (referenced from Weeks 3 – 8).

Stable links are JSTOR, SSRN, the publisher's DOI, or the journal page. Where a paper is paywalled and the author's working-paper version is publicly hosted, both links are given.

The five classics

  •   Fama & French (1992) — The Cross-Section of Expected Stock Returns

    Journal of Finance 47(2): 427–465. JSTOR

    Why it matters: the paper that killed beta and put size + book-to-market on the asset-pricing map. Read first, before FF93. Week 5.1.

  •   Fama & French (1993) — Common Risk Factors in the Returns on Stocks and Bonds

    Journal of Financial Economics 33(1): 3–56. DOI

    Why it matters: the three-factor model itself, with the GRS test, time-series regressions, and the template that every multi-factor paper since 1993 has copied. Week 5.2.

  •   Jegadeesh & Titman (1993) — Returns to Buying Winners and Selling Losers

    Journal of Finance 48(1): 65–91. JSTOR

    Why it matters: momentum is the anomaly that wouldn't die. The overlapping-portfolio construction is a methods lesson on its own. Week 5.3.

  •   Petersen (2009) — Estimating Standard Errors in Finance Panel Data Sets

    Review of Financial Studies 22(1): 435–480. DOI

    Why it matters: the paper everyone cites but few read carefully. We read it as a paper, not as a methods note — the empirical exhibit driving the t-stat from 5.70 to 2.19 is the point. Week 5.4 + cited throughout Week 2.

  •   Bertrand, Duflo & Mullainathan (2004) — How Much Should We Trust Differences-in-Differences Estimates?

    Quarterly Journal of Economics 119(1): 249–275. DOI

    Why it matters: the placebo-law experiment that exposed serial correlation in DiD inference. Set the standard for clustered standard errors. Week 5.5.

  •   Kogan, Papanikolaou, Seru & Stoffman (2017) — Technological Innovation, Resource Allocation, and Growth

    Quarterly Journal of Economics 132(2): 665–712. DOI

    Why it matters: market-based patent value — the construct that turned patent counts into something economists could actually use as a variable. Week 6.1.

  •   Hoberg & Phillips (2016) — Text-Based Network Industries and Endogenous Product Differentiation

    Journal of Political Economy 124(5): 1423–1465. DOI

    Why it matters: the paper that legitimized text-as-data in finance and gave us the TNIC industry classification still used today. Week 6.2.

  •   Loughran & McDonald (2011) — When Is a Liability Not a Liability? Textual Analysis, Dictionaries, and 10-Ks

    Journal of Finance 66(1): 35–65. DOI

    Why it matters: the finance sentiment dictionary that replaced Harvard-IV in every serious 10-K text paper since. Read it, then use the dictionary in Lab 6. Week 6.3.

Five Gao papers

These five trace the methodological arc of the program — fair lending, innovation, disclosure, and rigorous identification — through Prof. Gao's own work.

  •   Sun & Gao (2019) — Lending practices to same-sex borrowers

    Proceedings of the National Academy of Sciences 116(19): 9293–9302. DOI

    Why it matters: the program's fair-lending flagship (Financial Times "Top 100 Business-School Research with Social Impact"). The over-controlling trap in HMDA studies and the design-beats-controls lesson, made concrete — the Week 4 mentor session and the Week 6.4 Reader's Guide both build on it.

  •   Gao, Han, Li & Zhou (2018) — Market Intraday Momentum

    Journal of Financial Economics 129(2): 394–414. ScienceDirect

    Why it matters: return predictability and honest out-of-sample testing — a core piece of the Weeks 5–6 empirical-finance canon.

  •   Gao, Han, Pan & Zhang (2023) — Individualistic CEO and Corporate Innovation: Evidence from U.S. Frontier Culture

    Research Policy (2023). SSRN

    Why it matters: innovation measured with a hand-built cultural instrument — 19th-century frontier exposure linked to CEO birthplaces — identification from history rather than kitchen-sink controls. Week 6 reading.

  •   Elnahas, Gao, Hossain & Kim (2024) — CEO Political Ideology and Voluntary Forward-Looking Disclosure

    Journal of Financial and Quantitative Analysis 59(8): 3671–3707. Cambridge Core

    Why it matters: a disclosure study that survives the measurement-then-selection referee gauntlet — the Week 8 mentor session's worked case in defending an endogenous regressor.

  •   Gao, Liu & Wang — The Rainbow of Municipal Credit: Evidence from Same-Sex Marriage Bans

    Working paper (AEA 2025). Prof. Gao's research page

    Why it matters: fair lending travels from mortgages to municipal-bond markets — read in Week 7 as a model of how a research question turns into a registered design, the standard we want your capstone to imitate.

Citations verified

Each citation above is the paper's real published form (authors, year, journal, volume, pages), verified against the publisher, PNAS, SSRN, and Prof. Gao's research page.

How to access these papers

  • JSTOR — free through your GMU library proxy. Sign in with your @gmu.edu account.
  • SSRN — public; no auth needed.
  • Publisher DOIs (Elsevier, OUP, Univ. Chicago Press, Wiley) — most are open through the GMU library; if you hit a paywall, sign in via the Fenwick Library off-campus access.
  • Ken French Data Library — public; no auth. Used in Lab 5 (FF93 replication).

Read the Reader's Guide chapter before the paper. The chapter is the trail map; the paper is the trail.