Do analyst upgrades predict which stocks do better?
No. Since 2011, US stocks with more analyst upgrades than downgrades over the past 63 days beat the same day's median stock over the next month 50 times in 100; stocks with more downgrades, 50 times. A coin flip gives 50. Over a week the upgraded stocks show a faint trace (t 2.0), below the bar for a proven relation; over a month and three months, no measurable edge.
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 upgrades, do analyst upgrades work, analyst downgrades and stock price, analyst recommendations and returns, broker upgrades backtest, are analyst ratings reliable·JEL classification: G11, G12, G14, C12, C58
- 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
Brokerage analysts raise and cut their ratings on the stocks they cover, and an upgrade is widely read as a reason to expect a stock to do better. Womack (1996) found that prices kept drifting in the direction of a rating change for weeks after an upgrade and for months after a downgrade. This study asks whether stocks that have recently collected more upgrades than downgrades go on to beat other stocks, and whether those with more downgrades go on to trail them.
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.
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.
| Sector | Sample | Delisted | Large | Mid | Small | Stock-weeks | S&P 1500 |
|---|---|---|---|---|---|---|---|
| Information Technology | 282 | 67 | 122 | 76 | 18 | 13.9% | 12.7% |
| Financials | 256 | 60 | 114 | 74 | 8 | 14.6% | 17.2% |
| Industrials | 255 | 53 | 126 | 67 | 11 | 15.9% | 17.5% |
| Health Care | 237 | 47 | 92 | 81 | 16 | 12.4% | 10.9% |
| Consumer Discretionary | 209 | 39 | 69 | 87 | 12 | 12.3% | 12.9% |
| Energy | 105 | 30 | 39 | 31 | 5 | 5.9% | 4.7% |
| Consumer Staples | 101 | 38 | 40 | 17 | 5 | 5.9% | 4.9% |
| Materials | 94 | 34 | 35 | 24 | 2 | 5.6% | 5.1% |
| Real Estate | 84 | 10 | 40 | 32 | 2 | 5.7% | 6.9% |
| Communication Services | 80 | 31 | 28 | 13 | 5 | 3.9% | 3.3% |
| Utilities | 60 | 13 | 35 | 12 | 0 | 4% | 4% |
| All sectors | 1,763 | 422 | 740 | 514 | 84 | 100% | 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
| Signal | Definition | Since | Wednesdays |
|---|---|---|---|
| Net upgrades | More upgrades than downgrades among the analyst actions of the past 63 days | Feb 8, 2012 | 748 |
| Net downgrades | More downgrades than upgrades among the analyst actions of the past 63 days | Dec 21, 2011 | 755 |
| Balance of upgrades | Upgrades less downgrades, as a share of all analyst actions over the past 63 days; covered stocks ranked each Wednesday | Feb 15, 2012 | 748 |
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 showing the reading are scored against the same day's median stock. A state counts on every Wednesday it holds; a cross or breakout counts only on the Wednesday it happens.
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 methods3Results
Figure 1 reads every horizon for both readings. Stocks with more upgrades than downgrades beat the median stock 50 times in 100 over the next session, 50 over the next week, 50 over the next month and 50 over the next three months; stocks with more downgrades, 50, 50, 50 and 50. The one reading that stirs is the upgraded stocks' week, at 50.3 in 100 with a t-statistic of 2.0: faint, not reliable on its own. The other seven show no measurable edge.
Share of stocks showing the reading that beat the same day's median stock, by horizon. The line at 50 is chance, the grey band the range chance alone produces; a filled square is a proven relation.
Rank correlation between the signal and the return that followed, with its 95% interval. An interval that crosses zero is no relation.
Each square is one calendar year; filled = a year in which the average stock showing the signal lagged the median stock over the next month. Years are counted, not shown in order.
A signal with an edge would fill most squares, or leave most empty.
Rank correlation inside each GICS sector over the next month, stocks ranked only against their own sector. Colour only where |t| ≥ 2.
Out of every 100 stocks with net upgrades, 50 beat the median stock over the following month; out of every 100 with net downgrades, 50 did (Figure 2). Figure 3 drops the sign and ranks every covered stock on the balance of upgrades and downgrades: its rank correlation with the next month's return was 0.004 (t 1.0), and only the one-week horizon stirs, faintly.
An upgrade is public the moment it is issued, and many investors see it at once. By the time a stock carries a run of upgrades, whatever they told the market is likely to be in the price already; what follows in this data is an ordinary month for an ordinary stock. Barber, Lehavy, McNichols and Trueman (2001) found that even the gains from tracking consensus ratings day by day did not reliably survive trading costs.
4Robustness
Table 3 reads both signals horizon by horizon: over the next month the upgraded stocks beat the median stock in only 7 of 15 years, and the downgraded stocks lagged it in 7 of 15. In Table 4 the rank correlation kept its sign in 8 of 11 sectors but was 0.001 among S&P 500 members (t 0.3).
Share that beat the median stock, its t-statistic on non-overlapping dates, and the years in which it pointed the same way.
| Horizon | Beat the median | t | Years, same way |
|---|---|---|---|
| Net upgrades, session | 49.8 | -0.9 | 10 / 15 |
| Net upgrades, week | 50.3 | 2.0 | 9 / 15 |
| Net upgrades, month | 50.2 | 1.5 | 7 / 15 |
| Net upgrades, three months | 49.6 | -0.4 | 9 / 15 |
| Net downgrades, session | 50.3 | 1.5 | 11 / 15 |
| Net downgrades, week | 49.8 | -0.7 | 9 / 15 |
| Net downgrades, month | 49.8 | -0.2 | 7 / 15 |
| Net downgrades, three months | 50.1 | 1.5 | 9 / 15 |
Rank IC and its t-statistic on non-overlapping dates.
| Sample | IC | t | Wednesdays |
|---|---|---|---|
| Balance of upgrades, next month | |||
| All stocks, whole period | 0.0037 | 1.0 | 744 |
| By decade: 1995–2004 | |||
| By decade: 2005–2014 | 0.0022 | 0.3 | 148 |
| By decade: 2015– | 0.0040 | 0.8 | 596 |
| S&P 500 members on the date | 0.0011 | 0.3 | 744 |
| Sectors with the overall sign | 8 / 11 |
5Limitations
- The universe and its calendar are the same as every study's, from Jan 4, 1995; analyst actions, though, are on record only from 2011. The readings therefore cover a shorter span than the price-based studies on the same universe, and only the stocks analysts cover.
- The reading is the balance of actions over the past 63 days, observed on Wednesdays. The price reaction on the day of a single rating change, which earlier studies measure, is not what this study reads: most readings begin days or weeks after the action.
- 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
An upgrade is news, and news that many investors see at once is quickly priced. Measured every Wednesday since 2011 against the same day's median stock, stocks with more upgrades than downgrades did not go on to beat other stocks by a measurable margin over a month or three months, and stocks with more downgrades did not go on to trail them. A faint trace at one week is the most the data allows.
References
- Womack, K. L. (1996). Do Brokerage Analysts' Recommendations Have Investment Value? Journal of Finance 51(1).
- Barber, B., Lehavy, R., McNichols, M. and Trueman, B. (2001). Can Investors Profit from the Prophets? Security Analyst Recommendations and Stock Returns. Journal of Finance 56(2).
- 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).
- Grinold, R. C. and Kahn, R. N. (2000). Active Portfolio Management, 2nd ed. McGraw-Hill.
- Harvey, C. R., Liu, Y. and Zhu, H. (2016). … and the Cross-Section of Expected Returns. Review of Financial Studies 29(1).
- Shumway, T. (1997). The Delisting Bias in CRSP Data. Journal of Finance 52(1).
Appendix A. Questions readers ask
- Do analyst upgrades make stocks go up?
- Not measurably, once the upgrades are public. Since 2011, stocks with more upgrades than downgrades over the past 63 days beat the same day's median stock over the next month 50 times in 100 and over three months 50 times, against 50 for a coin flip.
- Do analyst downgrades predict a fall?
- Not reliably. Stocks with more downgrades than upgrades beat the median stock over the next month 50 times in 100, and over three months 50 times: indistinguishable from a coin flip.
- Why don't upgrades help?
- An upgrade is public the moment it is issued, and many investors see it at once. By the time a stock carries a run of upgrades, whatever they told the market is likely to be in the price already; what follows in this data is an ordinary month for an ordinary stock. Barber, Lehavy, McNichols and Trueman (2001) found that even the gains from tracking consensus ratings day by day did not reliably survive trading costs.
- How were analyst upgrades measured?
- Every Wednesday from the start of the analyst data in 2011 to Aug 19, 2026, the stocks of the same universe of 1,767 US stocks every study uses were split by the balance of upgrades and downgrades over the past 63 days, and compared with the same day's median stock over the next 1, 5, 21 and 63 trading sessions.
Data availability and citation
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.
Opulence Alpha Research (2026). Do analyst upgrades predict which stocks do better? Opulence Alpha Studies, Sep 25, 2026. https://opulencealpha.ai/studies/analyst-upgrades
Research, not advice. A measured tendency across hundreds of stocks is a nudge for any one of them, never a forecast of its price.