S&P 500 factor replication

What I built

I finished a full end-to-end replication of equity style factors on the historical S&P 500: same broad idea as vendor “factor libraries,” in my case lined up with S&P Capital IQ benchmark series. Starting from a firm-month panel (returns, fundamentals, expectations, index data), I walk through how you get to monthly long–short quintile portfolios and then check how close that gets to published factor returns when the rules are fixed in advance.

I’m not pitching a strategy here. Honestly, the part I cared about was making the chain honest—signal construction, timing (next-month returns, no look-ahead), universe definition—so someone else can see where the numbers come from and why small implementation choices matter. The interesting question for me was how much history you can recover when you’re disciplined about that, and where the implementation still fights you (messy fields, turnover, subsamples that don’t look like the full window).

What’s going on (the gist)

Factors are just a way to collapse a lot of cross-sectional information into a few long–short return series. Everyone uses them, but replication is fiddly: units, lags, winsorization, how you split quintiles, how you sign the spread against a benchmark.

I implemented seven of these (things like book-to-price, long-horizon momentum, short-horizon range, beta, size, realized vol, long-term growth expectations). Each month I keep only S&P 500 names, sort into five equal-weight quintiles, and call the factor return the top minus bottom bucket—“QSpread.” Then I compare my series to Capital IQ over shared history. I also zoom in on book-to-price as a case study: coverage, my series vs theirs, the two legs, and cumulative behavior vs the broad index.

On top of the raw performance tables I added some extras I wanted for myself: summary stats, a plain t-test plus Newey–West on the mean (monthly returns aren’t i.i.d.), a rough turnover cost haircut so “net” numbers aren’t confused with a real trading model, and a simple early vs late subsample split for a handful of factors. Take the significance with a grain of salt—seven factors, multiple tests—but the point is to read the output as due diligence, not as proof of edge.

How I structured the work (start to finish)

If you’re mapping the logic in your head, it goes like this:

1. Pull together the three aligned inputs: stock-month panel, vendor factor benchmarks, index / rate context.
2. Clean everything onto a monthly calendar, fix return scaling, winsorize where the spec says to, fill forward where signals would otherwise disappear, and tag S&P 500 membership.
3. Build each factor’s signal the way the definitions ask (e.g. value from book and market cap with the right lag, momentum with the skip month, beta from rolling market covariance).
4. Each month, quintile-sort on names that have a valid signal and a real next-month return.
5. QSpread = top quintile minus bottom; I align signs sensibly when stacking up next to Capital IQ.
6. Compare to the vendor over the window where all seven benchmarks line up.
7. Wrap up with stats, inference, the cost sketch, a little robustness, export the tables, and plot the BP story (those plots also feed a short PDF briefing).

The notebook is the real artifact—story, code, and outputs together. I pulled a tiny factor_replication package out for plotting helpers, the HAC helper, and the turnover drag so the notebook isn’t doing everything inline. There’s also a short LaTeX briefing under docs/ for anyone who wants the narrative without scrolling cells.

I can’t ship the raw CSVs (licensing). The repo is the recipe and code; the data path assumes you have something equivalent locally.

Where to look

Path	What you’ll find
notebooks/Factor Replication.ipynb	The whole run: what I did, how I did it, figures, tables.
src/factor_replication/	Small helpers: month indexing for plots, HAC mean test, turnover drag.
tests/	A few tests on those helpers.
docs/exec_briefing.tex	Compact write-up; pairs with docs/Makefile → exec_briefing.pdf.
requirements.txt	What I used on the Python side.
data/	Where the proprietary panel lives on my machine—not in this repo.
output/	Exports and BP figures after a full notebook pass.

One note on where this came from

This started as a graded course project; I’ve reframed it as my own write-up for a public repo, with my instructor’s okay to share it as portfolio material. The implementation and words are mine. The underlying data still belong to the vendors—cloning this doesn’t give you those files or their rights.