Stambaugh 1999 Replication

Stambaugh 1999 Replication#

Last updated: Aug 20, 2026, 1:04:28 PM

Project Notes

Project Overview

Table of Contents#

Notebooks 📖

Walkthrough: replicating Stambaugh (1999)

Pipeline Charts 📈

Chart List

Pipeline Specs#

Pipeline Name	Stambaugh 1999 Replication
Pipeline ID	ashishkmaheshwari/stambaugh_1999_replication
Maintainer	Ashish Maheshwari & Omar Anabtawi
Contributors	Ashish Maheshwari & Omar Anabtawi
Repository
Pipeline Web Page	Pipeline Web Page
Date of Last Code Update	2026-08-20 13:04:16
OS Compatibility	Windows, Linux, macOS
Linked Dataframes

Build Commands:

pip install -r requirements.txt
doit

Does the dividend–price ratio predict stock returns? For decades the standard test regressed next month’s excess return on this month’s dividend yield and usually found a positive, “significant” slope. Stambaugh (1999, Journal of Financial Economics 54) showed the test is broken in a quantifiable way: the dividend yield is highly persistent and shares a price with the return, so the OLS slope is biased upward in finite samples. A positive slope is what you should expect even when the true slope is zero.

This project rebuilds the data from CRSP, replicates the paper’s Table 1, Table 2, and Figure 1 within a documented tolerance, and extends every exhibit through 2024 — where the problem turns out to be worse than in the paper’s own sample: the gap between the naive and the finite-sample p-value has grown from roughly threefold to tenfold.

Read the project site — overview, walkthrough notebook, interactive playground, and full report.

Exhibits#

Exhibit	Content	Headline result
Table 1	Finite-sample properties of the OLS slope, by subsample	Our finite-sample p-value 0.177 vs the paper’s 0.17
Table 2	Bayesian posteriors under four prior/likelihood specifications	All sixteen cells within ~0.03 of the paper
Figure 1	β vs ρ across methods and subperiods	Reproduces the paper’s ρ > 1 overshoot in 1977–96
Updates	Tables 1 and 2 on samples through 2024	Naive p 0.015 vs honest p 0.149 in 1997–2024

Plus two educational products: a guided walkthrough notebook and an interactive browser playground where two sliders show the bias growing with persistence and shrinking with sample size.

Repository structure#

.
├── dodo.py                     # PyDoit build file — runs everything
├── chartbook.toml              # site configuration
├── requirements.txt
├── .env.example                # template for your .env (WRDS_USERNAME)
├── src/
│   ├── settings.py             # configuration: paths, dates, credentials
│   ├── pull_CRSP_index.py      # WRDS pull: CRSP monthly market index
│   ├── pull_fama_french.py     # WRDS pull: risk-free rate
│   ├── calc_predictor_data.py  # DATA CLEANING ONLY -> tidy monthly panel
│   ├── monte_carlo.py          # simulation engine (Table 1 Part A)
│   ├── stambaugh_bias.py       # bias-corrected estimators (Figure 1)
│   ├── bayesian.py             # conjugate posteriors (Table 2 specs A, B)
│   ├── mcmc.py                 # Metropolis-Hastings (Table 2 specs C, D)
│   ├── create_table_01_partC.py
│   ├── create_table_01.py      # Table 1 assembly -> _output/*.tex
│   ├── create_table_02.py      # Table 2 assembly -> _output/*.tex
│   ├── create_figure_01.py     # Figure 1 -> _output/figure_01.png
│   ├── 01_walkthrough.ipynb.py # guided tour notebook (jupytext source)
│   └── test_*.py               # unit tests
├── reports/report.tex          # the write-up; inputs the generated exhibits
├── docs_src/                   # site sources (edit these)
├── docs/                       # BUILT site — generated, served by Pages
├── _data/                      # pulled and cleaned data (git-ignored)
└── _output/                    # generated tables, figures (git-ignored)

Data cleaning lives in its own file, separate from all analysis. Raw data never enters the repository.

Setup#

conda create -n stambaugh python=3.12 -y
conda activate stambaugh
pip install -r requirements.txt
cp .env.example .env      # then set WRDS_USERNAME=your_login

The first WRDS connection prompts for your password and offers to create a .pgpass file so later runs are non-interactive. .env is git-ignored and must never be committed.

Running it#

doit

That pulls from WRDS, builds the tidy panel, regenerates every table and figure, compiles the report, executes the notebook, rebuilds the site, and runs the tests. PyDoit tracks dependencies, so re-running rebuilds only what changed.

Individual stages:

doit pull                  # WRDS pulls
doit clean_data            # tidy panel
doit table_01 figure_01    # paper-sample exhibits
doit table_01_updated      # extended-sample exhibits
doit notebook              # execute the walkthrough
doit compile_latex_docs    # report PDF
doit build_chartbook_site  # the published site
doit run_pytest            # test suite

Note that table_02 runs eight Metropolis-Hastings chains and takes several minutes.

Testing#

pytest -q src/

The suite is split deliberately. Simulation-based tests verify the bias mechanism itself — that the bias is positive when innovations are negatively correlated, vanishes when they are not, shrinks like 1/T, and matches the Kendall/Stambaugh analytical formula — and run anywhere, including CI without credentials. Data-dependent tests check our estimates against the paper’s published values within stated tolerances, and skip with an explanatory message when the panel has not been built.

Data sources#

CRSP Monthly Stock Market Indexes (crsp.msi, WRDS) — value-weighted returns with (vwretd) and without (vwretx) dividends. Their difference gives the dividend series, which is how the dividend–price ratio is reconstructed without a separate dividend file.
Fama–French monthly factors (ff.factors_monthly, WRDS) — the one-month risk-free rate, for continuously compounded excess returns.

Stambaugh uses a NYSE-only value-weighted index; our WRDS instance provides no pre-built NYSE-only monthly index carrying both return columns, so we use the CRSP total-market value-weighted index and document the choice in the report.

A note on `docs/`#

The built site is committed so GitHub Pages can serve it directly without a build step. Edit docs_src/, never docs/ — the latter is regenerated by doit build_chartbook_site and hand edits are lost.

Team#

Ashish Maheshwari
Omar Anabtawi

FINM 32900, Full-Stack Quantitative Finance, Summer 2026.