Neither signal is universally superior. EPS revisions tell you that analysts have changed their expectations for a company’s future earnings. Price momentum tells you that the stock’s previous return direction has persisted. They answer different questions, and historical studies find predictive information in both, with overlap that varies by definition, sample and portfolio design.
For serious stock research, use each as a separate lens, then test whether one adds information after controlling for the other. Do not treat historical abnormal returns as a forecast of what either signal will earn today.
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What the two signals actually measure
EPS estimate revisions
An EPS revision is a change in an analyst’s estimate of a company’s future earnings per share. A usable observation needs more detail than “estimates went up”: record the forecast horizon, the old and new estimate, the timestamp, the analyst or consensus source, and whether the change is reported in raw or scaled form.
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Analyst coverage is part of the signal. A revision from a widely followed company may be incorporated into prices quickly; a thinly covered company may have less information already reflected in the market. Jung, Keeley and Ronen reported abnormal returns for low-coverage firms in their sample and related that result to possible initial mispricing and poorer information environments. That is a finding about their data and design, not a rule that every low-coverage stock will outperform.
Price momentum
Price momentum is continuation in a stock’s realized past returns. It is calculated from a specified lookback window and applied to a defined universe. You must state whether returns include dividends, how corporate actions are handled, and whether the most recent month is skipped.
The anomaly literature uses several conventions. The 2017 Review of Financial Studies review, for example, describes examples using six- or eleven-month prior-return windows and revision strategies held for one or six months. Those are research-taxonomy examples, not universal settings. Changing the lookback, skip rule or holding period changes which stocks qualify and can change the measured performance.
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An earnings surprise compares reported earnings with an earlier expectation. An estimate revision changes the expectation before the report. Post-earnings-announcement drift describes a later return pattern after an earnings release. These signals can be related, but they are not interchangeable. A study that controls for past earnings surprises is not automatically testing analyst revisions.
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Why the answer depends on the research question
| Question you are asking | More direct signal | What it can miss |
|---|---|---|
| Are informed observers changing their view of future EPS? | EPS estimate revisions | Coverage gaps, delayed analyst responses and biased or inconsistent forecasts |
| Has the market’s prior return direction continued? | Price momentum | The fundamental reason for the move, and the possibility of reversal or underperformance |
| Does a change in expectations add information beyond the price move? | A joint, controlled test of both signals | Results that depend on timing, scaling, universe, holding period and costs |
Revisions can react to news that prices have already reflected, while momentum can contain information about expectations that are not yet fully incorporated. Consequently, a high revision and a high momentum score may be measuring partly the same information, partly different information, or different stages of the same adjustment.
What published evidence shows
Forecast revisions can contain predictive information, but the definition matters
Jung, Keeley and Ronen’s “The Predictability of Analyst Forecast Revisions,” published in the 2019 issue of the Journal of Accounting, Auditing & Finance (first available online in 2017), studies two-year-ahead EPS estimates. They report that revisions are correlated with contemporaneous price changes and that a strategy using unscaled revisions earned positive abnormal returns. The scaled versions in their tests did not show the same result.
The authors summarize their contribution as: “We find that analyst forecast revisions are predictable and document an implementable strategy for investors.” That sentence describes their sample, variables and strategy. It does not establish a current-market return expectation, and it does not show that every way of scaling a revision is inferior.
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Returns and earnings information can both matter
In “Momentum Strategies,” published in The Journal of Finance in December 1996, Louis K. C. Chan, Narayanan Jegadeesh and Josef Lakonishok report: “Past return and past earnings surprise each predict large drifts in future returns after controlling for the other.” This supports testing related signals together rather than assuming that a price-only measure has absorbed all earnings information.
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Because the paper uses earnings surprises, not analyst estimate revisions, its result should not be relabeled as proof that revisions independently predict returns. It is evidence that return history and a distinct earnings-related measure can have incremental explanatory power in that study.
Other work finds substantial overlap
A 2006 Journal of Financial Economics study titled “Earnings and price momentum” reaches a different emphasis in its tests. It states: “The predictive power of past returns is subsumed by a zero-investment portfolio that is long on stocks with high earnings surprises and short on stocks with low earnings surprises.” In that design, the systematic component of earnings momentum captures the tested price-momentum effect.
The apparent tension between the 1996 and 2006 findings is a reason to inspect definitions, not to declare one signal universally dominant. Different samples, factor constructions, ranking rules and controls can produce different conclusions about what is incremental.
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Event reactions show why the information source matters
Johnson and Womack’s Management Science study, published online in 2016, compares recommendation changes motivated by earnings estimate revisions with recommendation changes without earnings revisions. It reports an initial price reaction about 1.3% greater for upgrades and about 2.8% greater in magnitude for downgrades when earnings estimate revisions motivated the change. These are event-reaction differences in that study; they are not a general return forecast for a portfolio of revised stocks.
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Analyst outputs are not internally uniform
Iselin, Park and Van Buskirk, in a February 2021 Journal of Accounting and Economics article, report that analysts simultaneously revise two outputs in opposite directions in about 20%–30% of cases in their sample. The percentage applies to the paired analyst outputs they examine, not to all estimate changes. It illustrates why a researcher should identify exactly which output moved and avoid treating every analyst update as a clean, one-directional signal.
Other research, including Low and Tan’s 2016 analysis of analyst forecasts and momentum, documents limitations consistent with optimistic bias and inconsistent analyst output. Forecast availability, the construction of consensus estimates and analyst incentives can therefore affect both the quality and the tradability of a revision.
EPS revisions and momentum: a precise comparison
| Comparison axis | EPS estimate revisions | Price momentum |
|---|---|---|
| Underlying change | Analysts’ estimates of future EPS; specify forecast horizon and raw or scaled form. | The stock’s realized past return; specify the return window and any skipped month. |
| Information source | Analyst forecasts and updates; coverage and consensus construction matter. | Market prices and returns; corporate actions, return window and universe definition matter. |
| Timing | May reflect new fundamentals, but analyst responses can lag news. | Observable immediately in prices and may include information investors have already acted on. |
| Main vulnerability | Forecast bias, inconsistent outputs and missing coverage, especially for thinly followed firms. | Reversal or underperformance after a historical winning period, without a direct explanation of changed fundamentals. |
| Fair comparison | Align forecast timestamp, horizon, revision formula, universe and holding period. | Align prior-return window, skip rule, universe and holding period on the same dates. |
How to test whether revisions add information beyond momentum
A credible comparison is a research design, not a chart with two lines. Use the following sequence so that one signal does not receive a timing or construction advantage.
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1Fix the driver behind crashes, sound loss and screen glitches2Repair Windows errors before they cause bigger problems3Scan for outdated or missing drivers - takes under a minute- Define the investable universe. State the exchange or market, listing rules, minimum price or liquidity screens, treatment of delisted companies and the sample dates. Include delisted securities where the data permits; excluding them can distort historical results.
- Set the information timestamp. For revisions, use the time the update became observable, not a later database download date. For prices, use a close or other consistently defined price. Do not let a revision dated after a portfolio formation time enter that portfolio.
- Specify the revision variable. Record forecast horizon, analyst-level versus consensus data, and the exact raw or scaled calculation. If scaling is used, name the denominator and apply it consistently. Keep upward and downward changes separate if that is part of the hypothesis.
- Specify momentum before looking at results. Choose the prior-return window, whether dividends are included, the corporate-action adjustment and any skip period. Six- and eleven-month examples appear in anomaly research, but selecting between them after seeing performance creates a look-ahead problem.
- Match holding periods. A one-month revision strategy is not directly comparable with a six-month momentum strategy. Test common holding periods or report each horizon separately. Rebalance both signals on the same calendar and formation dates.
- Construct comparable portfolios. Use the same ranking method, number of groups, weighting scheme and sector or size controls for both signals. A portfolio of equal-weighted deciles and a portfolio of capitalization-weighted quintiles answer different questions.
- Run nested tests. First test revisions alone and momentum alone. Then place both ranks or standardized scores in the same cross-sectional model or portfolio sort. The incremental result is the change in explanatory or predictive power after the other signal is included.
- Account for implementation. Estimate turnover, bid-ask spreads, commissions, market impact, borrow availability for shorts and applicable taxes. A published abnormal return before these costs is not the net return available to an investor.
- Check robustness. Repeat the analysis across subperiods, coverage levels, market-cap groups and alternative reasonable definitions. Report how many observations survive each screen and whether a result is driven by a small group of extreme winners or losers.
When each signal is most useful in a research workflow
Use revisions to investigate changing expectations
Start with revisions when your central question is whether the earnings outlook is improving or deteriorating. Inspect the direction, size, forecast horizon and breadth of the change: one analyst moving an estimate is different from a broad consensus shift. Then compare the revision date with the stock move. A large price reaction before the update may mean the market anticipated the information; a revision with little price response may deserve a different investigation, not an automatic buy or sell decision.
Use momentum to measure market confirmation
Start with momentum when you want to know whether investors have continued to reward or penalize a stock over a defined period. Examine the return window, skipped month and liquidity of the name. Momentum can confirm that a fundamental story is being recognized, but it cannot by itself tell you whether the underlying earnings outlook is improving.
Use both when the signals agree
An upward revision accompanied by positive momentum gives you two aligned observations: expectations have improved and the market has recently moved in the same direction. Treat that alignment as a screening condition, not proof of future performance. Test whether the combined signal survives after costs and whether its result is simply the stronger of the two individual signals.
Investigate disagreement rather than forcing a ranking
Positive momentum with falling estimates can indicate that price action is being driven by information other than near-term EPS, or that analysts are late. Rising estimates with weak momentum can indicate that the market doubts the forecast, that the update is already priced in, or that the company has an unrelated risk. These disagreements are prompts for fundamental and event research, not automatic contrarian trades.
Common mistakes that make the comparison misleading
- Calling every earnings-related measure a revision. Reported surprises, estimate changes and post-announcement drift have different timestamps and meanings.
- Mixing horizons. A two-year-ahead EPS revision and a six-month return are not interchangeable observations. Preserve each horizon in the data and interpretation.
- Ignoring scaling. The Jung, Keeley and Ronen result differed between unscaled and scaled revisions. A result for one definition cannot be generalized to the other.
- Using future knowledge. Applying a later-restated consensus, revised company history or survivorship-screened universe can introduce look-ahead or survivorship bias.
- Equating correlation with incremental information. Revisions and prices can move together. Only a controlled, timestamped test can show whether one contributes beyond the other in a specified design.
- Assuming analyst consensus is neutral. Optimistic bias, inconsistent paired outputs and uneven coverage affect the meaning of a change.
- Reporting gross historical results as current expectations. Abnormal-return evidence is conditional on the study period, market, formula, portfolio construction and costs.
What the evidence can and cannot establish today
The cited studies establish that both analyst-related and price-related signals have contained predictive information in historical samples. They also establish that the signals can overlap and that apparently small design choices—raw versus scaled revisions, forecast horizon, return window and controls—can alter the conclusion.
They do not establish which signal will deliver the higher net return in the present market. Market structure, data quality, analyst coverage, trading costs and investor behavior change over time. A current decision therefore needs current, point-in-time data and a pre-specified test rather than a promise based on an old anomaly estimate.
The most defensible conclusion is conditional: use EPS revisions to study changes in expected earnings, use momentum to study continuation in realized returns, and evaluate both together when the question is whether changing expectations add information beyond what price has already shown.
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