Empirical Finance from the Ground Up — ASSIP 2026 Cohort
  • Home
  • Front Matter
  • Weeks
    • Week 1 — Probability & Inference
    • Week 2 — OLS & Inference
    • Week 3 — Causal Inference
    • Week 4 — Quasi-Experiments
    • Week 5 — Reading Finance Papers
    • Week 6 — Text, AI, Alt Data
    • Week 7 — Your Project I
    • Week 8 — Your Project II
  • Capstones
  • Instructor Manual
  • Assignments
  • Cohort Projects
  • About
  • Twitter
  • LinkedIn
  1. Empirical Finance from the Ground Up — ASSIP 2026 Cohort
  • Empirical Finance from the Ground Up — ASSIP 2026 Cohort
  • Front Matter
    • Preface
    • Articulation Matrix: The 8-Week Camp and the ASSIP 2026 Calendar
    • How to Use This Book
    • Prerequisite Self-Test
    • The Cohort and the Mentorship Structure
    • Table of Contents — Empirical Finance from the Ground Up
  • Week 1 — Probability, Inference, and the Logic of Estimation
    • Week 1 — Probability, Sampling, and the Logic of Inference
    • Ch 1.1 — Joint, Conditional, and the Two Laws That Run Everything
    • Ch 1.2 — Expectations, Variance, Covariance as Geometry
    • Chapter 1.3 — Estimators and Their Sampling Distributions
    • Ch 1.4 — The LLN and the CLT, Shown Not Asserted
    • Chapter 1.5 — Hypothesis Testing Done Right
    • Lab 1 — Build a Coin-Flip Universe
    • Mentor Session 1 — “What Is a Finding?”
    • PS 1.1 — Conditional Probability & LIE/LTV Drills
    • PS 1.2 — Expectation/Variance Algebra & Correlation Geometry
    • Problem Set 1.3 — Bias, Variance, and MSE of Estimators
    • Problem Set 1.4 — The LLN and the CLT, by Hand and by Simulation
    • Problem Set 1.5 — Power, Size, and Confidence Intervals
    • Week 1 Assessment — Probability, Estimation, and Inference
    • nb1.1 — LIE / LTV by Simulation
    • nb1.2 — Covariance Matrices & Correlation Heatmaps
    • nb1.3 — The Sampling-Distribution Explorer
    • nb1.4 — The LLN and the CLT, Shown Not Asserted
    • Notebook 1.5 — Power Curves and the t-Test From Scratch
  • Week 2 — OLS, Geometry, and Inference
    • Week 2 — The OLS Engine
    • Ch 2.1 — OLS in Matrix Form
    • Chapter 2.2 — Gauss–Markov and the Meaning of “Best”
    • Ch 2.3 — The Frisch–Waugh–Lovell Theorem
    • Chapter 2.4 — When the Errors Misbehave: Heteroskedasticity, Clustering, HC/HAC
    • Chapter 2.5 — Misspecification: OVB, Measurement Error, Functional Form
    • Lab 2 — Replicate a Textbook Fama–MacBeth on CRSP
    • Mentor Session 2 — “Why your standard error is the whole ballgame.”
    • PS 2.1 — Matrix-OLS Derivations & Numerical Inversion
    • PS 2.2 — Gauss–Markov: Breaking the Contract on Purpose
    • PS 2.3 — Frisch–Waugh–Lovell: Partialling-Out by Hand
    • Problem Set 2.4 — Robust, Clustered, and HAC Standard Errors
    • Problem Set 2.5 — OVB Sign Predictions and the Measurement-Error Simulation
    • Week 2 Assessment — The OLS Engine
    • nb2.1 — OLS from Scratch with NumPy vs. statsmodels
    • nb2.2 — Gauss–Markov by Monte Carlo: Seeing “BLUE”
    • nb2.3 — FWL Residualization, Visualized
    • Notebook 2.4 — Standard-Error Flavors on a Clustered Panel
    • nb2.5 — The Biased-Estimator Lab
  • Week 3 — Causal Inference Foundations
    • Week 3 — Causal Inference I: Potential Outcomes, Selection-on-Observables, and Instruments
    • Chapter 3.1 — Potential Outcomes, SUTVA, and the Fundamental Problem of Causal Inference
    • Chapter 3.2 — Selection on Observables I: Matching & Propensity Scores
    • Chapter 3.3 — Selection on Observables II: Entropy Balancing & Doubly-Robust Estimation
    • Ch 3.4 — Instrumental Variables
    • Chapter 3.5 — Reading IV in the Wild + The Weak-IV Pathology
    • Lab 3 — Reproduce a Weak-IV Pathology
    • Mentor Session 3 — “Natural experiments: finding the lever nature pulled.”
    • PS 3.1 — Potential-Outcomes Algebra & the Selection-Bias Decomposition
    • Problem Set 3.2 — Hand-Matching, Propensity Scores, and the Limits of Selection-on-Observables
    • PS 3.3 — Entropy-Balancing Weights & AIPW by Hand
    • Problem Set 3.4 — Instrumental Variables: 2SLS Derivation and Weak-IV Diagnostics
    • Problem Set 3.5 — Anderson–Rubin Inference and the Instrument-Validity Critique
    • Week 3 Assessment — Causal Inference I
    • nb3.1 — Potential Outcomes & Selection Bias: Playing God with the Science Table
    • nb3.2 — Propensity-Score Matching with Balance Diagnostics
    • Notebook 3.3 — Entropy Balancing vs. IPW vs. AIPW
    • nb3.4 — 2SLS with linearmodels; the First-Stage F
    • nb3.5 — The Weak-Instrument Pathology, Reproduced
  • Week 4 — Quasi-Experiments in Finance
    • Week 4 — Causal Inference II: DiD, RD, Synthetic Control, and Shift-Share
    • Chapter 4.1 — Difference-in-Differences and Event Studies
    • Ch 4.2 — The Staggered-Adoption Crisis
    • Ch 4.3 — Regression Discontinuity
    • Ch 4.4 — Synthetic Control & Synthetic DiD
    • Ch 4.5 — Bartik / Shift-Share Designs
    • Lab 4 — A Clean DiD on HMDA + a State Policy Shock
    • Mentor Session 4 — “Detecting discrimination with a clean design.”
    • PS 4.1 — The 2×2 Difference-in-Differences and Event-Study Construction
    • Problem Set 4.2 — Goodman-Bacon by Hand, and the Negative-Weights Crisis
    • Problem Set 4.3 — Regression Discontinuity: Local-Polynomial Fitting and Bandwidth Experiments
    • PS 4.4 — Synthetic Control Weights & Placebo Inference
    • Problem Set 4.5 — Shift-Share Exposure Weights and the Identification Critique
    • Week 4 Assessment — Causal Inference II
    • nb4.1 — Event Studies & Parallel Trends: Playing God with Priya's Climate-Insurance Shock
    • nb4.2 — Staggered DiD: TWFE vs. Callaway–Sant’Anna
    • nb4.3 — RD with rdrobust; CCT bandwidths
    • nb4.4 — Synthetic Control & Synthetic DiD: Building the Counterfactual You Wish You Had
    • nb4.5 — Bartik / Shift-Share Instrument Decomposition
  • Week 5 — Reading the Empirical Finance Canon
    • Week 5 — Reading the Frontier I: Decoding Empirical Papers
    • Chapter 5.1 — Reader's Guide: *The Cross-Section of Expected Stock Returns* (Fama & French 1992)
    • Ch 5.2 — Reader's Guide: Fama & French (1993), "Common Risk Factors in the Returns on Stocks and Bonds"
    • Ch 5.3 — Reader's Guide: Jegadeesh & Titman (1993)
    • Chapter 5.4 — Reader’s Guide: Petersen (2009), “Estimating Standard Errors in Finance Panel Data Sets”
    • Chapter 5.5 — Reader's Guide: Bertrand, Duflo & Mullainathan (2004)
    • Reading Guide Pack 5 — How to Read an Empirical Paper
    • Mentor Session 5 — “Anatomy of a JF paper.”
    • PS 5.1 — Sorting, Double-Sorting, and the Fama–MacBeth Horse Race in Fama & French (1992)
    • Problem Set 5.2 — FF93 Factor Construction & Time-Series Regressions
    • PS 5.3 — Momentum Portfolio Replication and the Transaction-Cost Critique
    • Problem Set 5.4 — Re-running Petersen’s Clustering Comparison on a Panel
    • Problem Set 5.5 — Reproduce a BDM Placebo-DiD False Positive
    • Week 5 Assessment — Reading the Frontier I
    • nb5.1 — FF92 Portfolio Sorts: the Size and Book-to-Market Cross-Section
    • nb5.2 — FF93 Factor Regressions: Loadings, R^2, \alpha, and the GRS Test
    • Notebook 5.3 — Momentum Backtest (Jegadeesh–Titman)
    • Notebook 5.4 — Replicating Petersen (2009): The Clustering Taxonomy
    • nb5.5 — The BDM Placebo-Law Simulation
  • Week 6 — Text, AI, and Alternative Data
    • Week 6 — Reading the Frontier II: Text, Modern Empirics, and the AI Co-Pilot
    • Chapter 6.1 — Reader's Guide: *Technological Innovation, Resource Allocation, and Growth* (Kogan, Papanikolaou, Seru & Stoffman 2017)
    • Chapter 6.2 — Reader's Guide: Hoberg & Phillips (2016), "Text-Based Network Industries and Endogenous Product Differentiation"
    • Chapter 6.3 — Reader's Guide: Loughran & McDonald (2011), "When Is a Liability Not a Liability?"
    • Chapter 6.4 — Reader's Guide: Fair Lending in the Algorithmic Era (Bartlett et al. 2022; Bhutta, Hizmo & Ringo)
    • Chapter 6.5 — The AI Co-Pilot for Research (LLM-in-the-Loop)
    • Reading Guide Pack 6 + AI Lab Manual — When the Data Is Words, and the Tool Has Opinions
    • Mentor Session 6 — “Text as data, and AI without fooling yourself.”
    • PS 6.1 — Building the KPSS Patent-Value Measure: Construction, Validation, and Critique
    • Problem Set 6.2 — 10-K Cosine Similarity & TNIC Mini-Build
    • PS 6.3 — The LM Dictionary vs. a Naive Dictionary: When a Liability Is Not a Liability
    • Problem Set 6.4 — Fair-Lending Decomposition and the Disparate-Impact Critique
    • Problem Set 6.5 — An LLM Classifier as a Measurement Instrument: Held-Out Validation and a Leakage Audit
    • Week 6 Assessment — Reading the Frontier II: Build and Validate a Text Classifier
    • nb6.1 — Building a Patent-Value Panel: Reading a Patent’s Worth Off the Stock Price (KPSS-style)
    • nb6.2 — 10-K Text Vectorization & Similarity (Hoberg–Phillips, TNIC-style)
    • nb6.3 — The Loughran–McDonald Sentiment Pipeline
    • nb6.4 — Mortgage-Disparity Decomposition: How Much of Maya’s Gap Is Real Risk?
    • nb6.5 — The AI Co-Pilot Lab
  • Week 7 — Your Project, Part I
    • Week 7 — Independent Research Project I: From Question to Pre-Analysis Plan
    • Chapter 7.1 — Idea-Generation Workshop
    • Chapter 7.2 — Data Acquisition in Practice
    • Chapter 7.3 — The Pre-Analysis Plan (Short Form)
    • Chapter 7.4 — Building the Analysis Dataset
    • Ch 7.5 — The Identification Memo
    • Lab 7 — Your Data, Reproducibly
    • Mentor Session 7 — “How I pick a project — and kill one.”
    • PS 7.1 — Three Candidate Questions, Scored: A Feasibility-and-Novelty Deliverable
    • PS 7.2 — A Working Data-Pull Script and Data Card for One Project Source
    • PS 7.3 — Write and Register a Complete Pre-Analysis Plan for Your Project
    • PS 7.4 — A Reproducible Merged Analysis Dataset, With Diagnostics
    • PS 7.5 — Your Identification Memo + Threats Table
    • Week 7 Assessment — Independent Research Project I: The Design Half
    • nb7.1 — The Idea-to-Spec Template
    • nb7.2 — The Multi-Source Data-Pull Harness
    • Notebook 7.3 — PAP Companion: A Power Calculation for Your Design
    • nb7.4 — Building the Analysis Dataset: Merge, Lag, and Validate
    • nb7.5 — First-Look Regressions: Frozen Until the PAP Is Filed
  • Week 8 — Your Project, Part II
    • Week 8 — Independent Research Project II: Execution, Robustness, Poster, and Defense
    • Chapter 8.1 — Execution & the Specification Curve
    • Ch 8.2 — Robustness & Inference Stress-Tests
    • Chapter 8.3 — Writing the Empirical Paper (and the Abstract Your Poster Carries)
    • Ch 8.4 — Peer Review & Revision
    • Ch 8.5 — The 8-Minute Poster Talk & the Replication Packet
    • Lab 8 — Final Poster + Repo + Symposium Defense
    • Mentor Session 8 — “Defending a result: what a referee actually asks.”
    • PS 8.1 — Specification Curve for Your Main Result
    • PS 8.2 — The Full Robustness Battery + Write-Up
    • PS 8.3 — Draft Your JSSR Abstract and Poster Results Narrative Against the Style Guide
    • PS 8.4 — A Referee Report + Your R&R Memo
    • PS 8.5 — Your 8-Minute Poster Talk + the One-Click Replication Packet
    • Week 8 Assessment — The Capstone (Terminal Assessment of ASSIP)
    • nb8.1 — The Specification-Curve Generator
    • nb8.2 — The Robustness Battery: Try to Kill Your Own Result
    • nb8.3 — Publication-Quality Tables & Figures: Turning Maya's Frozen Results into a Paper
    • nb8.4 — The Reproducibility Check: Can a Stranger Regenerate Your Number?
    • nb8.5 — The Final Manuscript Build: Markdown/LaTeX → PDF
  • Capstone Projects
    • Capstone Gallery — Exemplar Papers Behind the ASSIP Poster Deliverable
    • Capstone 1 (Exemplar) — Fair Lending on HMDA: How Much of the Mortgage-Denial Gap Survives the Controls?
    • Capstone 2 (Exemplar) — Common Ownership from 13F
    • Capstone 3 — Innovation from USPTO PatentsView
    • Capstone 4 — Reading the Tape: An Out-of-Sample-Validated Text Classifier for SEC 8-K Events and the Short-Window Return Reaction
    • Capstone 5 — FRED Macro Event Study
  • Instructor Manual
    • Instructor’s Manual — ASSIP 2026
    • IM-1 — Pacing Guide
    • IM-2 — Grading Rubrics (Consolidated)
    • IM-3 — Common Student Pitfalls, by Week
    • IM-4 — Mentor-Session Facilitation & Guest Lectures
    • IM-5 — Answer Keys & Anchor Work
    • IM-6 — Equity, Access & Compliance
  • Appendices
    • Appendix A — Math Toolkit
      • Appendix A — Math Toolkit (stub). See ../../TOC.md.
      • A.1 — Matrix Algebra (just enough)
      • A.2 — Optimization (just enough)
      • A.3 Asymptotics
      • A.4 Probability Distributions Reference
    • Appendix B — Python & LaTeX Setup
      • Appendix B — Python + LaTeX Setup (stub). See ../../TOC.md.
      • B.1 Toolchain
      • B.2 GitHub & GitHub Classroom
      • B.3 WRDS Cloud
      • B.4 — GMU Hopper SLURM
      • B.5 — Overleaf + the AEA LaTeX Template
    • Appendix C — Data Dictionary
      • Appendix C — Data Dictionary Master
      • Data Cards — Appendix C source dictionary
      • Data Card — U.S. Bureau of Economic Analysis (BEA)
      • Data Card — U.S. Bureau of Labor Statistics (BLS)
      • Data Card — Capital IQ (S&P Capital IQ via WRDS)
      • Data Card — U.S. Census Bureau: ACS + County Business Patterns
      • Data Card — CFPB Consumer Complaint Database
      • Data Card — Compustat (S&P Global Fundamentals)
      • Data Card — CRSP (Center for Research in Security Prices)
      • Data Card — Crypto / On-Chain: Etherscan / CoinGecko / DeFiLlama
      • Data Card — SEC EDGAR 10-K / 10-Q (Annual & Quarterly Reports)
      • Data Card — SEC EDGAR 13F (Institutional Holdings, the Free Path)
      • Data Card — SEC EDGAR 8-K (Material Events)
      • Data Card — SEC EDGAR DEF 14A (Proxy Statements)
      • Data Card — SEC EDGAR N-PORT (Fund Portfolio Holdings)
      • Data Card — FDIC (Institution Directory, SDI financials, and bank failures)
      • Data Card — Call Reports (FFIEC)
      • Data Card — FOMC Text (statements / minutes / speeches) + GDELT Global News
      • Data Card — FR Y-9C (Bank Holding Company Financials)
      • Data Card — FRED (Federal Reserve Economic Data)
      • Data Card — Free Equity Price APIs: yfinance / Stooq / Tiingo / Alpha Vantage
      • Data Card — HMDA (Home Mortgage Disclosure Act Loan Application Register)
      • Data Card — IBES (Institutional Brokers’ Estimate System)
      • Data Card — International Macro: ECB SDW · BIS · IMF IFS · World Bank WDI · OECD.Stat
      • Data Card — Loughran–McDonald Master Dictionary (Finance Sentiment Word Lists)
      • Data Card — Mergent FISD (Fixed Income Securities Database)
      • Data Card — MSRB EMMA (Municipal securities disclosures and trades)
      • Data Card — NBER / KPSS Patent–Compustat Links
      • Data Card — NOAA Billion-Dollar Disasters / Storm Events + FEMA Disaster Declarations
      • Data Card — OptionMetrics (IvyDB US)
      • Data Card — Thomson Reuters / SEC 13F Institutional Holdings
      • Data Card — TRACE (Transaction Reporting and Compliance Engine)
      • Data Card — U.S. Treasury (TreasuryDirect + Daily Yield Curve) and FINRA TRACE
      • Data Card — USAspending.gov (Federal Awards)
      • Data Card — USPTO PatentsView (now USPTO Open Data Portal)
    • Appendix D — Style Guide
      • Appendix D — Empirical-Finance Style Guide (stub). See ../../TOC.md.
      • D.1 — Table Craft
      • D.2 — Reporting Regressions
      • D.3 — Robustness Sections That Persuade
      • D.4 — The Replication Packet Standard
      • D.5 — Prose Style
    • Appendix E — Solutions Manual
      • Appendix E — Solutions Manual
      • Solutions — PS 1.1 (Conditional Probability & LIE/LTV Drills)
      • Solutions — PS 1.2 (Expectation/Variance Algebra & Correlation Geometry)
      • Solutions — Problem Set 1.3 (Bias, Variance, and MSE of Estimators)
      • Solutions — Problem Set 1.4 (The LLN and the CLT)
      • Solutions — Problem Set 1.5 (Power, Size, and Confidence Intervals)
      • Solutions — PS 2.1 (Matrix-OLS Derivations & Numerical Inversion)
      • Solutions — PS 2.2 (Gauss–Markov: Breaking the Contract on Purpose)
      • Solutions — PS 2.3 (Frisch–Waugh–Lovell: Partialling-Out by Hand)
      • Solutions — Problem Set 2.4 (Robust, Clustered, and HAC Standard Errors)
      • Solutions — Problem Set 2.5 (OVB Sign Predictions and the Measurement-Error Simulation)
      • Solutions — PS 3.1 (Potential-Outcomes Algebra & the Selection-Bias Decomposition)
      • Solutions — Problem Set 3.2 (Hand-Matching, Propensity Scores, and the Limits of Selection-on-Observables)
      • Solutions — PS 3.3 (Entropy-Balancing Weights & AIPW by Hand)
      • Solutions — Problem Set 3.4 (Instrumental Variables: 2SLS Derivation and Weak-IV Diagnostics)
      • Solutions — Problem Set 3.5 (Anderson–Rubin Inference and the Instrument-Validity Critique)
      • Solutions — PS 4.1 (The 2×2 Difference-in-Differences and Event-Study Construction)
      • Solutions — Problem Set 4.2 (Goodman-Bacon by Hand, and the Negative-Weights Crisis)
      • Solutions — Problem Set 4.3 (Regression Discontinuity: Local-Polynomial Fitting and Bandwidth Experiments)
      • Solutions — PS 4.4 (Synthetic Control Weights & Placebo Inference)
      • Solutions — Problem Set 4.5 (Shift-Share Exposure Weights and the Identification Critique)
      • Solutions — PS 5.1 (Sorting, Double-Sorting, and the Fama–MacBeth Horse Race in Fama & French 1992)
      • Solutions — Problem Set 5.2 (FF93 Factor Construction & Time-Series Regressions)
      • Solutions — PS 5.3 (Momentum Portfolio Replication and the Transaction-Cost Critique)
      • Solutions — Problem Set 5.4 (Re-running Petersen’s Clustering Comparison on a Panel)
      • Solutions — Problem Set 5.5 (Reproduce a BDM Placebo-DiD False Positive)
      • Solutions — PS 6.1 (Building the KPSS Patent-Value Measure: Construction, Validation, and Critique)
      • Solutions — Problem Set 6.2 (10-K Cosine Similarity & TNIC Mini-Build)
      • Solutions — PS 6.3 (The LM Dictionary vs. a Naive Dictionary: When a Liability Is Not a Liability)
      • Solutions — Problem Set 6.4 (Fair-Lending Decomposition and the Disparate-Impact Critique)
      • Solutions — Problem Set 6.5 (An LLM Classifier as a Measurement Instrument: Held-Out Validation and a Leakage Audit)
      • Model Deliverable — PS 7.1 (Three Candidate Questions, Scored)
      • Solutions — PS 7.2 (A Working Data-Pull Script and Data Card for One Project Source)
      • Model Deliverable — PS 7.3 (A Complete Pre-Analysis Plan Draft)
      • Solutions — PS 7.4 (A Reproducible Merged Analysis Dataset, With Diagnostics)
      • Model Deliverable — PS 7.5 (Identification Memo + Threats Table)
      • Model Deliverable — PS 8.1 (Specification Curve for Your Main Result)
      • Model Deliverable — PS 8.2 (The Full Robustness Battery + Write-Up)
      • Model Deliverable — PS 8.3 (An A-Grade Introduction + Headline-First Results Excerpt)
      • Model Deliverable — PS 8.4 (Referee Report + R&R Memo)
      • Model Deliverable — PS 8.5 (8-Minute Deck + One-Click Replication Packet)
    • ASSIP 2026 Cohort
      • Assignments
      • Week of July 10 — AI and the Labor Market, and Reading a Finance Paper
      • Cohort Research Projects

Empirical Finance from the Ground Up — ASSIP 2026 Cohort

Eight full-time weeks of research-grade empirical finance, built for the GMU ASSIP 2026 group

Author
Affiliation

Lei Gao

Costello College of Business, George Mason University

Published

2026-06-08

George Mason University · College of Science · Aspiring Scientists Summer Internship Program (ASSIP) 2026

Empirical Finance from the Ground Up

ASSIP 2026 Cohort · Eight full-time weeks · A research poster at the symposium on Aug 12, 2026

A textbook-and-lab system for the 14 advanced high-school and pre-graduation undergraduate researchers in the ASSIP 2026 Empirical Finance group — empirical finance the way it is actually done: with code, with data, and with intellectual honesty about what we can and cannot conclude.

Start here → How to use the book View source on GitHub

Faculty mentor: Prof. Lei Gao, Associate Professor of Finance, Costello College of Business, George Mason University. 2026 ASSIP slot: REMOTE only. Program director: Dr. Amanda Haymond Still · cosassip@gmu.edu.

8 Weeks

40 Chapters

40 Notebooks

34 Data cards

5 Capstones

1 Instructor manual


Two interlinked sites

This project ships as two sites on a single GitHub Pages domain. Use the wiki for cohort logistics (calendars, mentors, templates, FAQ, symposium info); use the textbook for the chapters, problem sets, notebooks, capstones, and instructor’s manual.

Cohort wiki — /

Operational dashboard: week-by-week schedule, project templates (PAP, poster), mentor roster, code of conduct, FAQ, symposium logistics, reading list, data access, setup guide.

Open the wiki →

Textbook — /textbook/ (you are here)

The 8-week curriculum: probability and inference, OLS, causal inference I & II, paper-reading guides, text/AI, two project weeks, plus appendices A–E, the capstone gallery, and the instructor’s manual.

Read the preface →


The 8-week arc — ASSIP 2026 calendar

Orientation is Thursday Jun 18, 2026. The cohort works Mon–Fri 9 am – 5 pm from Mon Jun 22 through the ASSIP Research Symposium on Wed Aug 12. Holidays off: Fri Jun 19 (Juneteenth) and Fri Jul 3 (Independence Day observance).

Week 1

Probability, Inference, and the Logic of Estimation

The two laws, expectations as geometry, sampling distributions, LLN/CLT, the mechanics of a t-test. Lab: simulating the coin-flip universe. Jun 22 – Jun 26, 2026.

Week 2

OLS, Geometry, and Inference

OLS in matrix form, Gauss-Markov, Frisch-Waugh-Lovell, robust and clustered standard errors, omitted-variable bias. Lab: Fama-MacBeth on CRSP. Jun 29 – Jul 2, 2026 (Jul 3 off).

Week 3

Causal Inference Foundations

Potential outcomes and SUTVA, matching and propensity scores, entropy balancing, IV and weak-IV pathology. Lab: making weak instruments lie. Jul 6 – Jul 10, 2026.

Week 4

Quasi-Experiments in Finance

Event studies, staggered DiD (TWFE vs. Callaway-Sant’Anna), regression discontinuity, synthetic control, Bartik shift-share. Lab: HMDA DiD. Jul 13 – Jul 17, 2026.

Week 5

Reading the Empirical Finance Canon

Reader’s guides to Fama-French 1992 & 1993, Jegadeesh-Titman 1993, Petersen 2009, Bertrand-Duflo-Mullainathan 2004. What does a JF paper actually do? Jul 20 – Jul 24, 2026.

Week 6

Text, AI, and Alternative Data

Patent value, 10-K similarity, Loughran-McDonald sentiment, fair-lending decomposition, and an AI copilot lab — without fooling yourself. Jul 27 – Jul 31, 2026.

Week 7

Your Project, Part I

Idea generation, data acquisition, pre-analysis plan, dataset construction, identification memo. The week you stop being a student. Aug 3 – Aug 7, 2026.

Week 8

Your Project, Part II — Symposium

Specification curve, robustness battery, poster build, peer review, defense. Poster session at the ASSIP Research Symposium, Wed Aug 12. Aug 10 – Aug 12, 2026.


Who this is for

This site is built for the ASSIP 2026 Empirical Finance cohort — a group of 14 advanced high-school students (15+) and pre-graduation undergraduates selected by GMU’s College of Science to work full-time, Monday through Friday 9 am – 5 pm, from June 22 through the ASSIP Research Symposium on August 12, 2026.

The group is at the high end of typical ASSIP group size (2–15), so we run a layered mentorship model: Prof. Gao (faculty mentor, remote slot) over the whole cohort, with PhD / RA sub-leaders working closely with sub-pods on data engineering, identification, and writing. See Cohort & Mentorship Model for how that scales for 14 students.

The methodological bar is real. You should be comfortable with strong precalculus or calculus, ready to learn Python quickly if you have not yet, and willing to read math that begins:

\hat{\boldsymbol{\beta}}_{\text{OLS}} = (\mathbf{X}'\mathbf{X})^{-1}\mathbf{X}'\mathbf{y}.

If that equation is intimidating but interesting, you are exactly the intended reader. Appendix A is a math toolkit; Appendix B is a setup guide; the prerequisite self-test will tell you which gaps to close before orientation on Jun 18.

Take the prerequisite self-test → Cohort & mentorship model →


What you’ll build — and what ASSIP requires

The required ASSIP 2026 deliverable is a research poster presented at the ASSIP Research Symposium on Wed Aug 12, 2026 (in-person poster session for on-site interns + virtual breakouts for remote interns — which is all of us, since our 2026 slot is remote). Beyond that, by the end of Week 8 you will have:

  • A research poster for the Aug 12 symposium, audience includes Mason faculty, mentors, families, College of Science leadership, and Mason News press.
  • A research abstract submitted to the Journal of Student-Scientists’ Research (JSSR, GMU Mason Publishing).
  • An optional full paper if your project supports it.
  • A public replication packet: code, data references, README, environment file, one-command rebuild.
  • 3 GMU College of Science credits for completion, plus participation in the ASSIP scientific-writing and career forum.
  • Reading fluency in five canonical empirical-finance papers and five more from the text/AI tradition.

How to use the book

For students

Read each week’s chapters in order. Work the problem sets before opening the solutions. Run every notebook. The labs are the part where you actually learn. Anchor every analysis decision to the identification memo you will write in Week 7.

For instructors & mentors

The instructor manual has the ASSIP-specific pacing guide, grading rubrics, common pitfalls, and answer keys. The capstone gallery gives five fully-specified projects with synthetic illustrative results.

For independent readers

You can run every notebook on Colab or Binder directly from each notebook page — look for the buttons at the top. The environment.yml pins the conda environment.

For everyone

Every chapter that makes a quantitative claim flags the claim with a [CHECK] tag if it has not yet been verified against a primary source. This is on purpose. Research is a process, not a posture.


Acknowledgements

This curriculum is built for the ASSIP 2026 (Aspiring Scientists Summer Internship Program) at George Mason University’s College of Science — director Dr. Amanda Haymond Still, program contact cosassip@gmu.edu. ASSIP makes research-grade methods accessible to high-school students and pre-graduation undergraduates who are ready for them, regardless of GPA, and offers full tuition waivers for Pell-eligible and reduced-lunch students. The articulation matrix maps every learning objective to the ASSIP symposium deliverable and to downstream undergraduate / PhD-level competencies, with sources cited.

The textbook was drafted in a multi-agent workflow that pairs planning, writing, and review agents under human supervision. See About for the methodology and the editorial standard.


Ready to start?

Read the preface → Browse the capstones → Open on GitHub →

Preface
Source Code
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description-meta: >
  An 8-week, full-time empirical finance research curriculum for the
  GMU ASSIP 2026 cohort. Hosted (remote) by Prof. Lei Gao at George
  Mason University's Costello College of Business. Probability, OLS,
  causal inference, quasi-experiments, text-as-data, and a research
  poster for the Aug 12, 2026 ASSIP Symposium.
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George Mason University · College of Science · Aspiring Scientists Summer Internship Program (ASSIP) 2026
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# Empirical Finance from the Ground Up {.hero-title}

### ASSIP 2026 Cohort · Eight full-time weeks · A research poster at the symposium on Aug 12, 2026 {.hero-subtitle}

A textbook-and-lab system for the 14 advanced high-school and pre-graduation
undergraduate researchers in the **ASSIP 2026 Empirical Finance group** —
empirical finance the way it is actually done: with code, with data,
and with intellectual honesty about what we can and cannot conclude.

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[Start here →](book/00-front-matter/00-preface.md){.btn .btn-primary}
[How to use the book](book/00-front-matter/02-how-to-use.md){.btn .btn-outline}
[View source on GitHub](https://github.com/edwardlg/assip-2026-empirical-finance){.btn .btn-outline}
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Faculty mentor: **[Prof. Lei Gao](https://business.gmu.edu/)**, Associate Professor of Finance,
Costello College of Business, George Mason University. **2026 ASSIP slot: REMOTE only.**
Program director: Dr. Amanda Haymond Still · `cosassip@gmu.edu`.
:::

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[**8**]{.stat-num} [Weeks]{.stat-lbl}
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[**40**]{.stat-num} [Chapters]{.stat-lbl}
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[**40**]{.stat-num} [Notebooks]{.stat-lbl}
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[**34**]{.stat-num} [Data cards]{.stat-lbl}
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[**5**]{.stat-num} [Capstones]{.stat-lbl}
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[**1**]{.stat-num} [Instructor manual]{.stat-lbl}
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---

## Two interlinked sites

This project ships as **two sites** on a single GitHub Pages domain. Use the wiki for cohort logistics (calendars, mentors, templates, FAQ, symposium info); use the textbook for the chapters, problem sets, notebooks, capstones, and instructor's manual.

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### Cohort wiki — `/`
Operational dashboard: week-by-week schedule, project templates (PAP, poster), mentor roster, code of conduct, FAQ, symposium logistics, reading list, data access, setup guide.

[Open the wiki →](../){.btn .btn-outline}
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### Textbook — `/textbook/` (you are here)
The 8-week curriculum: probability and inference, OLS, causal inference I & II, paper-reading guides, text/AI, two project weeks, plus appendices A–E, the capstone gallery, and the instructor's manual.

[Read the preface →](book/00-front-matter/00-preface.md){.btn .btn-outline}
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---

## The 8-week arc — ASSIP 2026 calendar

Orientation is **Thursday Jun 18, 2026**. The cohort works **Mon–Fri 9 am – 5 pm**
from **Mon Jun 22** through the **ASSIP Research Symposium on Wed Aug 12**.
Holidays off: **Fri Jun 19** (Juneteenth) and **Fri Jul 3** (Independence Day observance).

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### [Week 1](book/weeks/week-01/README.md)
**Probability, Inference, and the Logic of Estimation**

The two laws, expectations as geometry, sampling distributions, LLN/CLT, the
mechanics of a $t$-test. Lab: simulating the *coin-flip universe*.
*Jun 22 – Jun 26, 2026.*
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### [Week 2](book/weeks/week-02/README.md)
**OLS, Geometry, and Inference**

OLS in matrix form, Gauss-Markov, Frisch-Waugh-Lovell, robust and clustered
standard errors, omitted-variable bias. Lab: Fama-MacBeth on CRSP.
*Jun 29 – Jul 2, 2026 (Jul 3 off).*
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### [Week 3](book/weeks/week-03/README.md)
**Causal Inference Foundations**

Potential outcomes and SUTVA, matching and propensity scores, entropy
balancing, IV and weak-IV pathology. Lab: making weak instruments lie.
*Jul 6 – Jul 10, 2026.*
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### [Week 4](book/weeks/week-04/README.md)
**Quasi-Experiments in Finance**

Event studies, staggered DiD (TWFE vs. Callaway-Sant'Anna), regression
discontinuity, synthetic control, Bartik shift-share. Lab: HMDA DiD.
*Jul 13 – Jul 17, 2026.*
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### [Week 5](book/weeks/week-05/README.md)
**Reading the Empirical Finance Canon**

Reader's guides to Fama-French 1992 & 1993, Jegadeesh-Titman 1993,
Petersen 2009, Bertrand-Duflo-Mullainathan 2004. *What does a JF paper actually do?*
*Jul 20 – Jul 24, 2026.*
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### [Week 6](book/weeks/week-06/README.md)
**Text, AI, and Alternative Data**

Patent value, 10-K similarity, Loughran-McDonald sentiment, fair-lending
decomposition, and an AI copilot lab — without fooling yourself.
*Jul 27 – Jul 31, 2026.*
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### [Week 7](book/weeks/week-07/README.md)
**Your Project, Part I**

Idea generation, data acquisition, pre-analysis plan, dataset
construction, identification memo. The week you stop being a student.
*Aug 3 – Aug 7, 2026.*
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### [Week 8](book/weeks/week-08/README.md)
**Your Project, Part II — Symposium**

Specification curve, robustness battery, poster build, peer review,
defense. **Poster session at the ASSIP Research Symposium, Wed Aug 12.**
*Aug 10 – Aug 12, 2026.*
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---

## Who this is for

This site is built for the **ASSIP 2026 Empirical Finance cohort** — a
group of **14 advanced high-school students (15+) and pre-graduation
undergraduates** selected by GMU's College of Science to work full-time,
Monday through Friday 9 am – 5 pm, from June 22 through the ASSIP
Research Symposium on August 12, 2026.

The group is at the high end of typical ASSIP group size (2–15), so we
run a **layered mentorship model**: Prof. Gao (faculty mentor, remote
slot) over the whole cohort, with PhD / RA sub-leaders working closely
with sub-pods on data engineering, identification, and writing. See
[Cohort & Mentorship Model](book/00-front-matter/04-cohort-and-mentorship.md)
for how that scales for 14 students.

The methodological bar is real. You should be comfortable with strong
precalculus or calculus, ready to learn Python quickly if you have not
yet, and willing to read math that begins:

$$
\hat{\boldsymbol{\beta}}_{\text{OLS}} = (\mathbf{X}'\mathbf{X})^{-1}\mathbf{X}'\mathbf{y}.
$$

If that equation is intimidating but interesting, you are exactly the
intended reader. Appendix A is a math toolkit; Appendix B is a setup
guide; the prerequisite self-test will tell you which gaps to close
before orientation on Jun 18.

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[Take the prerequisite self-test →](book/00-front-matter/03-prerequisite-self-test.md){.btn .btn-outline}
[Cohort & mentorship model →](book/00-front-matter/04-cohort-and-mentorship.md){.btn .btn-outline}
:::

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## What you'll build — and what ASSIP requires

The **required ASSIP 2026 deliverable** is a research poster presented
at the **ASSIP Research Symposium on Wed Aug 12, 2026** (in-person poster
session for on-site interns + virtual breakouts for remote interns —
which is all of us, since our 2026 slot is remote). Beyond that, by
the end of Week 8 you will have:

- A **research poster** for the Aug 12 symposium, audience includes Mason
  faculty, mentors, families, College of Science leadership, and Mason
  News press.
- A **research abstract** submitted to the *Journal of Student-Scientists'
  Research* (JSSR, GMU Mason Publishing).
- An optional **full paper** if your project supports it.
- A **public replication packet**: code, data references, README,
  environment file, one-command rebuild.
- **3 GMU College of Science credits** for completion, plus participation
  in the ASSIP scientific-writing and career forum.
- **Reading fluency** in five canonical empirical-finance papers and
  five more from the text/AI tradition.

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## How to use the book

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### For students
Read each week's chapters in order. Work the problem sets *before*
opening the solutions. Run every notebook. The labs are the part where
you actually learn. Anchor every analysis decision to the
identification memo you will write in Week 7.
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### For instructors & mentors
The [instructor manual](book/instructor-manual/README.md) has the
ASSIP-specific pacing guide, grading rubrics, common pitfalls, and
answer keys. The [capstone gallery](book/capstones/README.md) gives
five fully-specified projects with synthetic illustrative results.
:::

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### For independent readers
You can run every notebook on **Colab** or **Binder** directly from each
notebook page — look for the buttons at the top. The
[`environment.yml`](https://github.com/edwardlg/assip-2026-empirical-finance/blob/main/environment.yml)
pins the conda environment.
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### For everyone
Every chapter that makes a quantitative claim flags the claim with a
`[CHECK]` tag if it has not yet been verified against a primary source.
This is on purpose. Research is a process, not a posture.
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---

## Acknowledgements

This curriculum is built for the **ASSIP 2026 (Aspiring Scientists
Summer Internship Program)** at George Mason University's College of
Science — director **Dr. Amanda Haymond Still**, program contact
`cosassip@gmu.edu`. ASSIP makes research-grade methods accessible to
high-school students and pre-graduation undergraduates who are ready
for them, regardless of GPA, and offers full tuition waivers for
Pell-eligible and reduced-lunch students. The
[articulation matrix](book/00-front-matter/01-articulation-matrix.md)
maps every learning objective to the ASSIP symposium deliverable and to
downstream undergraduate / PhD-level competencies, with sources cited.

The textbook was drafted in a multi-agent workflow that pairs planning,
writing, and review agents under human supervision. See [About](about.qmd)
for the methodology and the editorial standard.

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Ready to start?

[Read the preface →](book/00-front-matter/00-preface.md){.btn .btn-primary}
[Browse the capstones →](book/capstones/README.md){.btn .btn-outline}
[Open on GitHub →](https://github.com/edwardlg/assip-2026-empirical-finance){.btn .btn-outline}
:::

© 2026 Lei Gao · Costello College of Business, George Mason University · Licensed CC BY-NC-SA 4.0.

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