Do stocks that trade like value stocks beat growth stocks?
Not in the data. Since 1995, the fifth of US stocks that traded most like value stocks, by their loading on the Fama–French value factor, beat the same day's median stock over the next three months 50 times in 100; the fifth that traded most like growth stocks, 50. A coin flip gives 50. The rank correlation was 0.003 (t 0.3): no measurable edge for either style.
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: value vs growth, value stocks vs growth stocks, does value investing still work, value premium, Fama French HML, value factor backtest·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
Value investing holds that cheap stocks, priced low against their book value or earnings, beat expensive growth stocks over time. Fama and French (1992) documented such a premium in US stocks, and the value factor they built on it, HML (Fama and French 1993), became the standard way to describe how much a stock trades like a value or a growth stock. This study asks whether the stocks whose returns behave most like value stocks go on to beat those that behave most like growth stocks, measured against the same day's median stock.
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 |
|---|---|---|---|
| Value-factor loading | The stock's beta on the Fama–French value factor (HML) in a rolling 252-session factor regression; high trades like value, low like growth | Jan 4, 1995 | 1,634 |
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 methods3Results
Figure 1 ranks the stocks every Wednesday by their loading on the value factor, from the most growth-like fifth (lowest) to the most value-like (highest), and reads each fifth over the next three months. The growth end beat the median stock 50 times in 100 and the value end 50 times. Figure 2 reads the rank correlation at every horizon: 0.003 at three months (t 0.3), and close to zero, a shade below it, over a session, a week and a month.
Share of each fifth that beat the same day’s median stock. Colour only where the relation is proven.
Next three months.
Next week, three-session return against the industry; the same way in 32 of 32 years.
Rank correlation between the signal and the return that followed, with its 95% interval. An interval that crosses zero is no relation.
Rank correlation inside each GICS sector over the next three months, stocks ranked only against their own sector. Colour only where |t| ≥ 2.
Average returns show no value advantage either. Measured against the universe's equal-weighted average return over three months, the value end averaged 0.29 points and the growth end 0.87 points. Neither figure is a tested result; they are reported because the value premium is usually quoted in averages.
4Robustness
Table 3 re-measures the three-month relation: it pointed toward value in only 1 of the three decades and kept its sign in only 3 of 11 sectors. Among S&P 500 members the rank correlation was -0.003 (t -0.1), and year by year the value end beat the median stock more often than the growth end in 17 of 32 years.
Rank IC and its t-statistic on non-overlapping dates.
| Sample | IC | t | Wednesdays |
|---|---|---|---|
| Value-factor loading, next three months | |||
| All stocks, whole period | 0.0026 | 0.3 | 1,621 |
| By decade: 1995–2004 | 0.0297 | 2.2 | 516 |
| By decade: 2005–2014 | -0.0202 | -0.9 | 518 |
| By decade: 2015– | -0.0010 | -0.1 | 587 |
| S&P 500 members on the date | -0.0026 | -0.1 | 1,569 |
| Sectors with the overall sign | 3 / 11 |
5Limitations
- Value is measured by how a stock's returns move with the value factor, not by its price-to-book or price-to-earnings ratio. A stock can look cheap on those ratios and still trade like a growth stock; the classic premium, sorted on book-to-market, is not what this study tests.
- The loading is estimated from the past year's returns, so it shifts as a stock's behaviour changes and carries estimation noise, which can blur a relation that exists.
- 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
The belief that value beats growth rests on decades of academic evidence, much of it from years before the period measured here. Since 1995, among the stocks of this universe, those that traded most like value stocks did not beat those that traded most like growth stocks by a measurable margin at any horizon from a session to three months. Whatever premium value once carried, it does not show in this measure of style over this period.
References
- Fama, E. F. and French, K. R. (1992). The Cross-Section of Expected Stock Returns. Journal of Finance 47(2).
- Fama, E. F. and French, K. R. (1993). Common Risk Factors in the Returns on Stocks and Bonds. Journal of Financial Economics 33(1).
- Lakonishok, J., Shleifer, A. and Vishny, R. W. (1994). Contrarian Investment, Extrapolation, and Risk. Journal of Finance 49(5).
- 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 value stocks beat growth stocks?
- Not in this measure. Since 1995, the fifth of US stocks that traded most like value stocks beat the same day's median stock over the next three months 50 times in 100, and the fifth that traded most like growth stocks 50 times, against 50 for a coin flip.
- Is the value premium dead?
- This study cannot say that of value as a whole, because it sorts stocks by how their returns move with the value factor, not by price-to-book. It shows that the relation pointed toward value in only 1 of the three decades measured, and that the value-like end has not beaten the growth-like end by a measurable margin since 1995.
- What counts as a value stock here?
- A stock whose returns over the past 252 sessions moved most closely with the Fama–French value factor, HML, the return of cheap stocks (high book-to-market) minus expensive ones. Each Wednesday the stocks are ranked on that loading and cut into fifths: the top fifth trades most like value stocks, the bottom fifth most like growth stocks.
- How was value vs growth measured?
- Every Wednesday from Jan 4, 1995 to Aug 19, 2026, the same universe of 1,767 US stocks every study uses was ranked on each stock's value-factor loading, cut into fifths, 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 stocks that trade like value stocks beat growth stocks? Opulence Alpha Studies, Sep 25, 2026. https://opulencealpha.ai/studies/value-vs-growth
Research, not advice. A measured tendency across hundreds of stocks is a nudge for any one of them, never a forecast of its price.