Reddit's 6.22-point lead over 13F buys has no date

Reddit's five most-discussed stocks earned 6.22 percentage points more over three months than the five names institutions accumulated most, according to Murodov and Aliyeva. The paper gives readers no way to check that result. The question deserves an answer. This paper leaves it open.

Two lists at the filing deadline

The motivation is January 2021: coordinated buying on r/WallStreetBets squeezed GameStop and AMC, inflicting heavy losses on short sellers. The authors ask whether crowd attention can select stocks as well as professional money. They compare a list of forum favourites with a list of institutional purchases.

Both portfolios are formed at the close on T0, the 13F filing deadline. Form 13F holdings may be disclosed up to 45 days after quarter-end, making T0 the first day both lists are public. For the retail cohort, the authors take the top five individual equities by WallStreetBets mentions from an ApeWisdom snapshot at T0. ETFs are excluded. For the institutional cohort, they aggregate holdings across all 13F filers and select the top five issuers by net change in shares from the prior quarter.

A size and liquidity screen applies; the example threshold is USD 2 billion in market cap. Where a name appears on both lists, the next-ranked stock replaces it. The holding period is three calendar months. Returns are calculated from the entry and exit closing prices.

The retail results are GME +25.0%, AMC -10.0%, TSLA +10.0%, NVDA +20.0% and PLTR +20.0%. For institutions, they are AAPL +8.3%, MSFT +5.0%, GOOGL +10.0%, AMZN +5.6% and META +5.0%. That gives means of 13.00% against 6.78% and medians of 20.00% against 5.56%.

The labels need care. As the authors acknowledge, "Mentions on Reddit indicate interest, not necessarily purchases." Their retail cohort measures attention. Their institutional cohort measures aggregate changes in share counts across all 13F filers, whose filings, they concede, "aggregate across managers with very different strategies."

When did the 6.22-point gap occur?

We could not establish the quarter. The methodology calls it "the quarter in question" without identifying it, and we found neither T0 nor T1 nor an ApeWisdom snapshot date anywhere in the text. The paper describes itself as "fully documented and freely replicable." Nobody can replicate a three-month result without its dates.

The printed prices deepen the problem. Every entry and exit close for the ten stocks is a round number: GME 20.00 to 25.00, AMC 5.00 to 4.50, MSFT 400.00 to 420.00, META 500.00 to 525.00. With no window to check, we cannot match those prices to market data. They look like illustrations of the method. The paper should tell readers whether they are.

Using the filing deadline is the paper's strongest choice. It removes the head start of up to 45 days that institutions would receive under quarter-end pricing. But replication requires timestamps on both inputs. Readers need the mention ranking as it stood at T0 and an aggregation of 13F holdings using only filings available by T0. The paper places both inputs at T0, describing the ranking as "the ApeWisdom snapshot for T0". It supplies neither that snapshot nor its date.

The ranking rule matters too. Every one of the five institutional selections is a US mega-cap platform. The authors say mega-caps qualify "only if they genuinely rank in the top five by net accumulation." Ranking by net shares mechanically favours issuers with large share counts, unlike ranking by dollar changes or changes relative to shares outstanding. The resulting institutional cohort acts like a big-tech basket.

A wide spread across five stocks

The median makes the retail result look stronger than the mean does. Compare 20.00% with 5.56%, and the difference seems decisive. Then look across the row: retail returns range from -10.0% to +25.0%, while institutional returns range from +5.0% to +10.0%. Win rates favour institutions, at 80% against 100%.

Retail's cross-sectional standard deviation is 13.96 points; the institutional figure is 2.27. Those spreads each come from five names, making the six-fold ratio noisy in its own right. With only two prices per stock, the data cannot yield any time-series risk measure.

The tests fit that picture. An exact two-sided Mann-Whitney test returns U = 19.5 and p = 0.222. A permutation test considers all 252 ways to divide ten names into two groups of five and returns p = 0.421. The authors explain the gap between the tests by the mean difference's sensitivity to retail dispersion, especially the AMC loss and GME gain.

They are frank about the sample's limited power. Five stocks per group constitute "a demonstration rather than an experiment," they write. They also caution that non-rejection "should not be read as evidence of equal performance." We agree on both counts. The retail list combines two meme names with three large-cap momentum names, leaving size and sector as plausible explanations for any difference alongside selection. The authors acknowledge the sector issue themselves: "market direction, sector exposure and event risk can dominate the comparison."

The missing benchmark adjustment

H0 refers to the "three-month benchmark-adjusted return." The conclusion also includes "to adjust for the market benchmark" among the paper's main methodological choices. Yet the discussion states: "Because the data do not include daily prices or a market benchmark, we cannot adjust for risk or for the overall market movement during the window."

The reported 13.00% and 6.78% therefore have no risk or market adjustment. Costs and spreads are excluded, as the authors acknowledge. Although the paper describes its prices as split- and dividend-adjusted, it presents no total-return calculation. The appendix code works solely from the two printed prices for each stock.

The abstract calls the result "descriptive evidence for one market window." The authors defend the paper on the grounds that its contribution is the design. That case would stand if they supplied dates and archived inputs, and if the conclusion described the procedure they actually performed. In its published form, the design promises an adjustment it never makes over a window it never identifies.

We did not backtest this. We lack a historical archive of Reddit or ApeWisdom mention rankings, as well as manager-level 13F history with filing timestamps. We therefore cannot reconstruct either list at any T0. Replaying the ten printed tickers would test those stocks after their selection had already been settled.

Keep the question; treat the table as an illustration.

The authors propose the right repair: repeat the comparison across consecutive quarters, or expand each list to the top 20. Dated snapshots and an actually applied market benchmark would make that version capable of changing our view. An undated 6.22-point gap with p = 0.421 will not.