Belief tested · Analysts, insiders and short sellers

Do the stocks analysts follow most do better?

The belief“The stocks Wall Street follows most closely are the better bets.”
VerdictWent the other way
49 in 100of the strongest fifth beat the median stock over the next three months. The weakest fifth: 52.

The opposite. Since 2012, the more research firms published ratings or price targets on a US stock, the weaker its next three months against other stocks tended to be: the most-covered fifth beat the same day's median stock 49 times in 100, the least-covered fifth 52; a coin flip gives 50. The relation is small, held its sign in 14 of 15 years, and vanished among S&P 500 members.

At a glance
5056445052lowest51low49middle49high49highest
Samplen = 1,763of the 1,767-stock universe
Rank correlation-0.029t -3.4, next three months
Period1995–20261,634 Wednesdays

The paper

The full study: its data, method, robustness, limits and sources.

Opulence Alpha Research · Published Sep 25, 2026 · Data through Aug 21, 2026

Keywords: analyst coverage, most covered stocks, analyst coverage and stock returns, neglected stocks, under-followed stocks, number of analysts covering a stock·JEL classification: G11, G12, G14, C12, C58

Study design
Universe
The same for every study: 1,767 US common stocks in the 11 GICS sectors, 422 of them since delisted; S&P 500 members since 1996 plus large and mid-sized companies outside the index
Period
Every Wednesday from Jan 4, 1995 to Aug 19, 2026: 1,634 Wednesdays across 7,959 trading sessions
Sample
n = 1,763 stocks of the 1,767-stock universe; 1,889,758 stock-weeks, a median of 1,148 stocks per Wednesday
Outcome
Return over the next 1, 5, 21 and 63 trading sessions against the same day's median stock
Inference
t on non-overlapping dates; proven only when |t| ≥ 3 and the same sign in at least 60% of years

1Introduction

Stocks followed by many analysts are often seen as the safe, quality end of the market: more eyes, more information, fewer surprises. The opposite idea has an academic form too: investors ask a higher return of companies fewer of them know (Merton 1987). This study counts the research firms publishing on each stock and asks which end of that ranking went on to beat other stocks.

2Data and method

2.1Sample design

Population and frame. The population is US common stocks listed on the NYSE and Nasdaq; funds, ETFs, trusts, preferred shares, warrants and units are excluded. The sampling frame is a fixed universe of 1,767 companies, drawn once when the platform was built and not re-sampled since, in two strata. Stratum 1 is a census of the S&P 500: every company in the index at any time since 1996 whose price history could be recovered, 1,027 companies of which 413 have since delisted; it holds 76% of the index's members in 1996 and at least 96% in every year from 2010. Stratum 2 is 740 large and mid-sized companies outside the index, selected in proportion to the market's sector weights from the stocks that passed a minimum share price of $15 and a minimum average daily trading value of $25 million; 9 of them have since delisted.

Sample. The unit of observation is a stock-week: one stock on one Wednesday. A stock enters a Wednesday's cross-section when it has a valid close that day, a value of the signal and a measured outcome; bars flagged as bad data and returns that cross a change of issuer are left out. This study's sample is n = 1,763 stocks of the 1,767: 1,889,758 stock-weeks on 1,634 Wednesdays from Jan 4, 1995 to Aug 19, 2026, a median of 1,148 stocks per Wednesday (range 820 to 1,371). 331,954 stock-weeks (17.6%) come from the 422 companies that have since delisted. Every stock and every Wednesday carries equal weight.

Representativeness. Table 1 gives the sample by GICS sector beside the S&P Composite 1500: 7.9% of companies would have to change sector for the two to match exactly. By latest market value, 55% of the active companies are large (at least $10bn), 38% mid ($2–10bn) and 6% small. Because Stratum 2 was chosen from companies listed at construction, its history carries survivorship bias. Section 4 repeats the lead result among the stocks that were S&P 500 members on each date, the part of the sample largely free of that bias.

Table 1. The sample by sector

Companies in this study's sample, of them those since delisted, the size of the active companies, the sample's share of stock-weeks and the sector's share of the S&P Composite 1500.

SectorSampleDelistedLargeMidSmallStock-weeksS&P 1500
Information Technology28267122761813.9%12.7%
Financials2566011474814.6%17.2%
Industrials25553126671115.9%17.5%
Health Care2374792811612.4%10.9%
Consumer Discretionary2093969871212.3%12.9%
Energy10530393155.9%4.7%
Consumer Staples10138401755.9%4.9%
Materials9434352425.6%5.1%
Real Estate8410403225.7%6.9%
Communication Services8031281353.9%3.3%
Utilities6013351204%4%
All sectors1,76342274051484100%100%

Sample: n = 1,763 of the 1,767 companies. Size by latest market value for the 1,338 active companies with one: large ≥ $10bn, mid $2–10bn, small < $2bn. S&P 1500 shares count the constituents of the S&P 500, MidCap 400 and SmallCap 600 (1,506 companies, lists read Sep 25, 2026). Sectors are each company's current GICS sector.

2.2Signals

Table 2. Signals studied
SignalDefinitionSinceWednesdays
Analyst coverageDistinct research firms that published a rating or price-target action on the stock in the previous 180 daysFeb 15, 2012748

2.3Measurement

Outcome. The return from the close on the Wednesday to the close 1, 5, 21 and 63 trading sessions later, on closes adjusted for splits and dividends, compared with the same day's median stock; half of all stocks beat the median by construction, so chance is 50 in 100 on every date. No delisting return is added.

Each Wednesday the stocks are ranked on the signal. The study reports how often each fifth of that ranking beat the median stock, and the rank correlation (IC) between the signal and the return that followed.

The t-statistic uses non-overlapping dates only. A result is called proven when |t| is at least 3, it held in at least 60% of years and it is large enough to matter; with 1,470 tests across the studies, a looser bar would pass dozens by luck.

Full data and methods

3Results

Figure 1 ranks the stocks into fifths by the number of research firms that published a rating or price-target action on them in the previous 180 days. Over the next three months the least-covered fifth beat the median stock 52 times in 100 and the most-covered fifth 49. Their average returns against the average stock were 1.56 and -0.72 points.

Figure 1. A flat ladder, and a real one

Share of each fifth that beat the same day’s median stock. Colour only where the relation is proven.

This signalWent the other way
5056445052lowest51low49middle49high49highest

Next three months.

What an edge looks like: short-term reversalProven
5056445051lowest50low50middle49high48highest

Next week, three-session return against the industry; the same way in 32 of 32 years.

Figure 2. How strong, and for how long

Rank correlation between the signal and the return that followed, with its 95% interval. An interval that crosses zero is no relation.

-0.030+0.03
session
-0.002
week
-0.003
month
-0.015
three months
-0.029
Figure 3. Where it holds

Rank correlation inside each GICS sector over the next three months, stocks ranked only against their own sector. Colour only where |t| ≥ 2.

Real Estate-0.051t -2.9
Materials-0.049t -2.4
Industrials-0.041t -2.1
Consumer Discretionary-0.039t -3.0
Health Care-0.038t -2.4
Consumer Staples-0.027t -1.3
Energy-0.025t -0.8
Utilities-0.020t -1.1
Financials-0.009t -0.3
Information Technology-0.005t -0.1
Communication Services+0.021t 0.9

Figure 2 reads the rank correlation by horizon. It is close to zero over the next session and week and grows more negative with time: over three months it reached -0.029, with a t-statistic of -3.4, a proven relation that held its sign in 14 of 15 years. The relation is small, but it runs against the belief: more coverage, slightly weaker returns.

Why it doesn’t work

Coverage follows size: larger companies draw more analysts, which is why studies of coverage adjust for size (Hong, Lim and Stein 2000). One old explanation for a premium on less-known companies is that investors ask a higher return of firms fewer of them know (Merton 1987). The difference measured here is small, and it did not appear among S&P 500 members.

4Robustness

Table 3 re-measures the relation. Over three months it kept its negative sign in 10 of 11 sectors and in every period its record reaches, but among S&P 500 members on the date it vanished (IC 0.001, t -0.3): the tilt comes from the companies outside the index. Fifth against fifth it was less steady: the least-covered fifth beat the median stock more often than the most-covered in 10 of 15 years. Over the next month the relation was weaker (IC -0.015, t -2.1), faint rather than proven.

Table 3. Robustness of the lead relations

Rank IC and its t-statistic on non-overlapping dates.

SampleICtWednesdays
Analyst coverage, next three months
All stocks, whole period-0.0291-3.4735
By decade: 1995–2004
By decade: 2005–2014-0.0314-1.2148
By decade: 2015–-0.0286-2.1587
S&P 500 members on the date0.0011-0.3735
Sectors with the overall sign10 / 11
Analyst coverage, next month
All stocks, whole period-0.0152-2.1744
By decade: 1995–2004
By decade: 2005–2014-0.0180-1.3148
By decade: 2015–-0.0145-2.2596
S&P 500 members on the date-0.00320.3744
Sectors with the overall sign9 / 11

5Limitations

  • Coverage is counted from rating and price-target actions in a record that begins in 2012, so this study covers only the years since then; a firm that follows a stock without publishing a rating or price-target action on it within 180 days is not counted.
  • Coverage rises with company size, and the relation vanished among S&P 500 members; the study does not separate being less covered from being smaller.
  • Before costs. Averages exclude trading costs, taxes and market impact.
  • Same-close timing. Returns start at the close the signal is computed from; a real trade would start later.
  • Survivors among smaller companies. The non-index names were chosen from companies listed when the universe was built; no delisting returns are added.

6Conclusion

More analysts on a stock means more research about it, not a better stock. Measured since 2012 against the same day's median stock, the most-covered companies went on to trail the least-covered ones over three months, by a small margin that held in most years and most sectors and disappeared among S&P 500 members. Wall Street's attention describes how well known a company is; in this data it did not mark the stocks that did better next.

References

  1. Hong, H., Lim, T. and Stein, J. C. (2000). Bad News Travels Slowly: Size, Analyst Coverage, and the Profitability of Momentum Strategies. Journal of Finance 55(1).
  2. Merton, R. C. (1987). A Simple Model of Capital Market Equilibrium with Incomplete Information. Journal of Finance 42(3).
  3. Benjamini, Y. and Hochberg, Y. (1995). Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing. Journal of the Royal Statistical Society, Series B 57(1).
  4. Grinold, R. C. and Kahn, R. N. (2000). Active Portfolio Management, 2nd ed. McGraw-Hill.
  5. Harvey, C. R., Liu, Y. and Zhu, H. (2016). … and the Cross-Section of Expected Returns. Review of Financial Studies 29(1).
  6. Shumway, T. (1997). The Delisting Bias in CRSP Data. Journal of Finance 52(1).

Appendix A. Questions readers ask

Do stocks with more analyst coverage perform better?
Not in this data; slightly the reverse. Since 2012, the most-covered fifth of US stocks beat the same day's median stock over the next three months 49 times in 100 and the least-covered fifth 52, against 50 for a coin flip. The relation is small but steady: it held its sign in 14 of 15 years.
Do less-followed stocks do better?
Slightly, on average across many stocks, over three months. The tilt vanished among S&P 500 members, so it describes smaller, less-followed companies rather than any single stock, and it is measured before trading costs, which tend to be higher for such companies.
Why wouldn't more coverage help?
Coverage follows size: larger companies draw more analysts, which is why studies of coverage adjust for size (Hong, Lim and Stein 2000). One old explanation for a premium on less-known companies is that investors ask a higher return of firms fewer of them know (Merton 1987). The difference measured here is small, and it did not appear among S&P 500 members.
How was analyst coverage measured?
Coverage counts the distinct research firms that published a rating or price-target action on the stock in the previous 180 days. Every Wednesday from 2012, when that record begins, to Aug 19, 2026, stocks were ranked on it and compared with the same day's median stock over the next 1, 5, 21 and 63 trading sessions, on the same universe of 1,767 US stocks every study uses.

Data availability and citation

Data availability

Every figure in this paper is quoted from one frozen snapshot, published as JSON with the sample description, the robustness results and the test counts. The same snapshot feeds the research console, so the two cannot disagree.

How to cite

Opulence Alpha Research (2026). Do the stocks analysts follow most do better? Opulence Alpha Studies, Sep 25, 2026. https://opulencealpha.ai/studies/most-covered-stocks

Research, not advice. A measured tendency across hundreds of stocks is a nudge for any one of them, never a forecast of its price.