In the QMJ-TSX paper I flag yfinance survivorship as a likely contaminant of the paper-Q extension’s results. This post unpacks what that actually means, why it’s worse for small-caps than for large-caps, and what an honest researcher with a free-data constraint can do about it.

The short version: if you build a TSX small-cap universe from “tickers that still trade today” and then backtest over 2011–2025, your historical universe is silently missing every delisting, every reverse-merger reshuffle, and every junior-resource zero. Whatever strategy you test will look better than it would have on the real cross-section that was investable in 2011.

What yfinance gives you

yfinance is a Yahoo Finance scraper. For any ticker that currently has a Yahoo page (e.g. XYZ.TO for TSX, XYZ.V for TSX Venture), it returns historical daily OHLCV back to the listing date. That is genuinely useful and has the unbeatable property of being free.

What it does not give you:

  1. Delisted tickers. A ticker that traded on TSX in 2014 and delisted in 2017 — for any reason — generally has no Yahoo page today. yfinance returns “no data” or silently skips.
  2. Reverse-merged or renamed tickers without a careful symbol trail. A junior that was acquired, reverse-merged, or rolled into a SPAC becomes effectively invisible.
  3. Point-in-time index membership. “The TSX small-cap universe in 2014” is not a thing yfinance can tell you. The best you can do is “tickers in the small-cap bucket today that have data going back to 2014,” which is exactly the survivorship trap.

For US large-caps, the gap between (1)–(3) and reality is small enough that yfinance is a defensible free data source. For TSX small/mid-caps it is structurally large.

Why small-caps are the worst case

Three reasons compound:

Base rate of delisting is high. Junior energy, mining, and biotech names — which dominate the TSX small/mid-cap universe — fail at rates that have nothing in common with S&P 500 attrition. A universe drawn from “what survived” is not a random subsample of “what was investable”; it is the right tail.

Index reconstitutions are large and frequent. The TSX small/mid-cap bucket has meaningful turnover every year. Even if you somehow had a perfect snapshot today, projecting it backward implies an unrealistic constancy of membership.

The risk being measured is asymmetric. This is the one that matters most for a Quality / Safety-style strategy. A “low volatility” name that quietly delisted in 2018 doesn’t show up in your backtest’s loss distribution. The survivors you do test on include disproportionately many names whose volatility was low because they didn’t blow up. Your backtest gets the reward of defensiveness without paying the tail cost. The whole construct’s premium comes from avoiding tail events, so this is exactly the worst place to have survivorship.

What it does to paper-Q specifically

The paper-Q long-short on TSX small/mid-caps over 2011-12 to 2025-11 has a full-sample Sharpe near zero. My honest assessment is that the true number — on a survivorship-corrected universe — is probably worse, not better, for a structural reason:

  • A defensiveness-tilted long leg benefits most from removing the worst-performing junior resource and biotech names.
  • yfinance survivorship removes exactly those names from the universe.
  • So the long leg of paper-Q is the part most contaminated by survivorship; the short leg less so.
  • Removing survivorship would tend to hurt the long leg’s measured Sharpe and improve the short leg’s.

Net direction on a long-short: roughly negative. The null result likely understates how badly the strategy actually performs.

That is an uncomfortable thing to say in a paper, which is why I say it. The pre-COVID +0.47 Sharpe is the one I trust least on this account: the bull run of 2011–2019 generated a lot of names that quietly disappeared by 2025 and are missing from my universe today.

What you can do with a free-data constraint

Two practical interventions worth the effort, two not worth it:

Worth it.

  1. Snapshot the universe at multiple historical dates if you can. Archive.org and historical TSX/TMX bulletins sometimes preserve old constituent lists. Even three or four historical snapshots, used as additional “as-of” universes, expose how much the surviving-today list misses.
  2. Report the cross-section size over time. If your 2011 “universe” has the same 109 tickers as your 2025 universe, you are not running a 2011 backtest — you are running a 2025 backtest on 2011 prices. Putting the cross-section count on a chart per month makes this visible to readers.

Not worth it for a working paper.

  1. Don’t fake a survivorship correction. Without delisting prices and dates, you cannot impute returns for missing tickers honestly. A made-up −90% return on a delisted name is research fraud in a coat.
  2. Don’t buy CRSP / Compustat just for this. If you can, great. But the right move for a free-data paper is to flag the limitation honestly and design the conclusions around it, not to pretend you have data you don’t.

The honest framing

The paper’s framing reflects the constraint. The headline claim is not “paper-Q doesn’t work on TSX small-caps.” It is “in a universe constructed from currently-listed TSX small-caps with free price data, a fundamentals-free price proxy fails to recover the AQR QMJ-Canada premium, and the failure is concentrated in the post-COVID low-volatility unwind.” Every clause in that sentence is true on the data I actually have.

If you are doing a free-data quant project: name your data limitations specifically, and design your claims so they survive the limitations being real. That is the cheap version of academic honesty, and it is much more useful than a confident claim built on data that can’t support it.

Universe file: data/raw/universe/tsx_smallcap.csv. A companion post on how that universe was built covers the construction in more detail.