This is the last post in the QMJ-TSX series. It’s the most operational of the lot: how I assembled the data layer for a Canadian small-cap factor paper using only free, public sources, and what that constraint forced me to accept.

The pitch is simple. Three data sources, all free, all on the public internet, all cacheable as parquet:

  • Prices. Yahoo Finance via yfinance (.TO for TSX, .V for TSX Venture).
  • AQR factor series. AQR Datasets — QMJ + BAB Equity monthly, all countries including Canada.
  • Fama–French factors. Ken French data library — Developed 5-factor + Developed momentum, monthly.

And one hand-curated universe file: 109 TSX small/mid-cap tickers in data/raw/universe/tsx_smallcap.csv.

That’s it. No Bloomberg, no Refinitiv, no CRSP, no Compustat, no S&P Capital IQ. Whether that is sufficient depends on what you want to claim — which is the rest of the post.

The three sources, briefly

yfinance for prices

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import yfinance as yf
df = yf.download("SU.TO", start="2011-01-01", interval="1mo")

Cached to data/raw/prices/{ticker}.parquet. Monthly is sufficient for a factor paper at this horizon and keeps the cache tiny (~1 MB total for 109 tickers). The .TO suffix is mandatory for TSX names; .V for Venture. Without the suffix you’ll silently get the US listing of a same-symbol unrelated company, which is its own special failure mode.

Honest limitations:

  • Survivorship (see the previous post).
  • Adjusted close handling is yfinance’s, not yours. For monthly rebalanced long-shorts this is fine; for intraday strategies it is not.
  • Some thinly-traded .V names have suspect prints. The 109-ticker universe was filtered partly on data sanity.

AQR datasets for the benchmark

AQR publishes country-level QMJ and BAB series as monthly CSVs. The QMJ-Canada column is the benchmark for the entire paper: without it I have nothing to replicate against. The CSV layout is stable across releases — date column, country columns, one row per month — and the file is small enough to vendor under data/raw/aqr/.

The replication gate (Sharpe within 0.30 of AFP 2019 Table II) is defined against this series. The cross-check against Ken French exists precisely because both benchmark series are public and disagree slightly on what “the Canadian factor cross-section” is.

Ken French for the cross-check factor library

The Developed 5-factor plus Developed momentum, monthly. Canada is too small a market to have its own French-style factor library, so Developed-region is the standard substitute when cross-checking a Canadian series. The RMW (operating profitability) factor in this library is the one that QMJ-CAN should load on if the construct is intact; in the paper it loads at β = +0.61 (t = 4.16). That cross-check is what turns the replication from “the numbers approximately match” into “the construct approximately matches.”

The universe file

data/raw/universe/tsx_smallcap.csv is a hand-curated list of 109 tickers spanning the TSX small/mid-cap range circa late 2025. The construction principles, in rough order:

  1. Currently listed on TSX or TSX Venture with a Yahoo ticker. This is the survivorship-introducing step. There is no free alternative.
  2. Market cap roughly in the small/mid range. No hard cutoff — TSX small-cap definitions vary across providers and I wasn’t going to invent one. Names that were unambiguously large-cap (the big banks, the integrated energy majors) were excluded.
  3. At least ~10 years of monthly data in yfinance. This is what bounded the sample to 2011-12 onward. Newer listings were excluded so that the cross-section per month was reasonably stable.
  4. Sector diversity within what TSX actually is. Which is to say: a lot of energy and materials, some industrials, some tech and healthcare, very little consumer. The universe reflects the index’s actual sectoral skew rather than fighting it.
  5. Sanity-check on prices. Tickers with obvious data pathologies in yfinance (long flat stretches, single-print spikes, missing months) were dropped at curation time.

None of those steps is forecast-aware — none of them required peeking at returns. But step 1 is the survivorship door, and I’m upfront about it in the paper.

Why no fundamentals?

The AQR QMJ construction uses gross profitability, accruals, leverage, payout ratios — accounting fundamentals at point-in-time fidelity for the entire cross-section. The free-data options for Canadian small-caps are:

  • SEDAR+ filings. Available, but unparsed and inconsistent. Parsing PDF financial statements at production quality for 100+ small-cap tickers is a separate paper’s worth of engineering.
  • Yahoo Finance fundamentals. Available via yfinance, but point-in-time-incorrect (the values are as-restated, not as-reported on the original filing date). Using them would introduce look-ahead bias.
  • SimFin / EOD historical data. Coverage of TSX small-caps is thin and gappy. I checked.

The cost of getting fundamentals right for this universe is roughly an order of magnitude greater than the cost of doing everything else combined. That is what drove the entire paper-Q (“price-based proxy”) detour: the negative result on paper-Q is also a measurement of how far you can get without paying that cost. Answer: not as far as you’d hope.

What it costs to do this right

If I were redoing this with a budget, the priority order would be:

  1. Point-in-time Canadian fundamentals (Compustat, FactSet, or equivalent). Unlocks the actual AFP construction. Highest marginal value.
  2. Delisting prices and dates. Kills the survivorship caveat from the previous post. Second-highest marginal value.
  3. Index constituent histories (S&P/TSX SmallCap or TMX equivalent, monthly). Lets the universe be reconstituted point-in-time rather than as a hand-curated snapshot.

You can publish a credible free-data paper without (1)–(3), provided you scope the claims to what the data actually supports. That is what this project tried to do.

Closing the series

If you’ve read all eight posts: thank you, that’s more attention than most academic papers get. The whole project — paper, code, data manifests, blog series — is at github.com/faketut/qmj-tsx. make all regenerates the paper from a clean clone. The pull request template is open if you want to extend the universe, swap in a better data source, or rerun the per-component decomposition on a different market.

The single sentence I’d leave you with, across the whole series: a pre-registered null result on a free-data universe, with the decomposition that explains the null, is a more honest research contribution than a positive result you can’t reproduce.

Series index: README.md.