N/A
5-year backtest return
The five-year cumulative return of an equal-weight portfolio formed from the most undervalued quintile, rebalanced monthly and shown net of estimated transaction costs.
Do our valuations
Every valuation on Alpha Spread is scored against what happened afterwards, across thousands of stocks, over years of history.
Valuation Backtest
What $10,000 became over time
This chart compares two simulated $10,000 portfolios with the S&P 500 price index. The benchmark excludes dividends and trading costs. Historical results do not predict future returns.
Results at a glance
Three tests of valuation quality
We ask three simple questions: Did a portfolio built from our valuations grow? Were individual calls right more often than chance? And did undervalued stocks outperform overvalued ones?
62.4%
Directional signal accuracy
This measures whether historical undervalued and overvalued calls moved in the expected direction over the following 12 months.
+32.6 pp
Undervalued vs overvalued
This compares the median 12-month returns of the most undervalued and most overvalued quintiles.
Model-by-model results
Intrinsic Value uses the company-specific valuation method selected from DCF, multiples-based value, or a 50/50 blend. DCF and multiples are shown separately for transparency.
| Valuation model | 5-Year Backtest | 12M Hit Rate | 12M Value Spread | VIC 12M |
|---|---|---|---|---|
|
Intrinsic Value
Company-specific DCF, multiples, or 50/50 blend
|
N/A | 62.4% | +32.6 pp | 0.24 |
|
DCF Value
Discounted cash flow
|
N/A | 58.9% | +22.7 pp | 0.19 |
|
Multiples-Based Value
Industry and historical multiples
|
N/A | 54.9% | +10.5 pp | 0.11 |
Robustness check
Does the result hold up?
A strong overall result can hide weak periods or markets. We check whether the signal remained consistent over time, across regions, and across sectors.
Directional accuracy
62.4
%
Latest accuracy
How often signals moved in the expected direction. A random direction would be right 50% of the time.
Over time
25 of 25
monthly hit rates above 50%
Across regions
6 of 6
regions above 50% hit rate
Across sectors
11 of 11
sectors above 50% hit rate
Value spread
+32.6
pp
Latest return gap
How much the most undervalued group beat the most overvalued group. Zero means no return advantage.
Over time
25 of 25
months with positive spread
Across regions
6 of 6
regions with positive spread
Across sectors
11 of 11
sectors with positive spread
For professionals
Methodology, in full
How the historical sample is built, when information becomes eligible, and how every signal, return, and statistic is calculated.
The calculation uses today’s eligible company stocks with a primary, active listing and current market capitalization of at least $100 million. A stock also needs a valid historical price and valuation for the observation month.
- Funds, secondary listings, and other non-company securities are excluded.
- Using today’s active universe and classifications introduces survivorship and look-ahead bias into historical results.
- Observations without a usable ending price or historical exchange rate are excluded from the relevant metric and sample size.
For each month-end, the calculation selects the latest available price, valuation, and exchange rate dated on or before that cutoff. It never fills a missing historical value with a later one.
- DCF and multiples history come from the historical sources currently available in Alpha Spread.
- Those sources are not complete archived data vintages, so a backtest rerun can reflect restatements or model-history corrections.
The valuation gap compares the model estimate with the market price available at the snapshot. Positive gaps indicate undervaluation; negative gaps indicate overvaluation.
- For group comparisons, eligible stocks are ranked by valuation gap and split into five equal groups.
- Observations within ±5% of fair value are neutral and excluded from directional hit rate.
Each historical signal is matched with the adjusted-close return of that stock over the following 12 months. Prices are translated to U.S. dollars using historical exchange rates.
- An undervalued call is correct when the forward return is positive; an overvalued call is correct when the forward return is negative.
- Value spread is the median return of the most undervalued group minus the median return of the most overvalued group.
Every metric has a predefined neutral reference. Directional accuracy is read against 50%, value spread and rank correlation against zero, and the undervalued portfolio against an overvalued portfolio built from the same dates and universe.
- Control portfolios use the same eligible stocks, rebalance schedule, and weighting method so the valuation signal is the intended difference.
- Market-wide gains or losses are not treated on their own as evidence that the valuation signal worked.
The chart normalizes two simulated portfolio NAV series and the S&P 500 benchmark to $10,000. One portfolio holds the most undervalued quintile and the control portfolio holds the most overvalued quintile.
- Both long-only portfolios are equal weighted and rebalanced monthly using a valuation from the signal month.
- The signal and simulated execution use the same adjusted month-end close, which is a simplifying assumption and can introduce look-ahead bias.
- Adjusted close keeps the portfolio on a consistent split-adjusted, dividend-reinvested total-return basis.
- The displayed series deducts estimated 5 bps commission, 10 bps one-way slippage, and a 5 bps FX spread on non-USD trades.
- The S&P 500 benchmark uses the month-end USD price index and excludes dividends and trading costs.
- The result is shown before investor-specific taxes.
Medians and ranks reduce the influence of a small number of extreme winners or losers. VIC 12M is the cross-sectional Spearman rank correlation between valuation gaps and subsequent returns.
- Accuracy, spread, and VIC are reported only when the relevant sample contains at least 30 observations.
- The displayed sample size changes with the selected metric because each metric has its own eligibility rules.
A new result is calculated after each monthly valuation refresh. The latest month is the row with the most recent published date.
- The date shown on this page identifies the latest completed monthly result.
- When the methodology changes, the affected history is recalculated so every point uses the same rules.
Use it correctly
What these numbers don’t mean
These aggregate results describe how the valuation rules behaved historically. They do not promise what will happen to any single stock.
Better odds are not a guaranteed outcome
A positive historical relationship, when present, changes probabilities rather than guaranteeing an outcome. A cheap stock can become cheaper, and an expensive stock can become more expensive.
Valuation does not tell you when the gap will close
A valuation gap can persist for months or years. The signal identifies a difference between price and estimated value; it is not a catalyst, an entry point, or a short-term price target.
A valuation is an estimate, not an exact future price
It depends on current financial data and assumptions about the business. New information can change the estimate itself, even when the market price has not moved.
A backtest is not a forecast
Backtests describe how the rules behaved historically. These results include estimated trading costs but remain before personal taxes; markets, companies, and model relationships can change.
Full results
Explore every market and model
Choose a metric, then compare Intrinsic Value, DCF, and multiples-based results globally, across regions, selected countries, and GICS sectors.
Five-year cumulative return of an equal-weight portfolio formed from the most undervalued quintile, rebalanced monthly and shown net of estimated transaction costs.
Break-even: 0%Share of valuation signals that moved in the expected direction over the following 12 months.
Random direction: 50%Median 12-month return of the most undervalued fifth minus the most overvalued fifth.
No return advantage: 0 ppSpearman rank correlation between valuation gaps and subsequent 12-month returns.
No valuation-return relationship: 0| Market GICS sector |
Intrinsic Value
Company-specific DCF, multiples, or 50/50 blend
|
DCF Value
Discounted cash flow
|
Multiples-Based Value
Industry and historical multiples
|
|---|---|---|---|
|
GLOB
Global
|
N/A
62.4%
18 033 observations
+32.6 pp
20 736 observations
0.24
20 736 observations
|
N/A
58.9%
18 059 observations
+22.7 pp
20 271 observations
0.19
20 271 observations
|
N/A
54.9%
18 400 observations
+10.5 pp
20 430 observations
0.11
20 430 observations
|
|
NAM
North America
|
N/A
64.9%
3 123 observations
+30.6 pp
3 610 observations
0.2
3 610 observations
|
N/A
64.1%
3 008 observations
+31.7 pp
3 397 observations
0.21
3 397 observations
|
N/A
57.8%
3 114 observations
+8.1 pp
3 466 observations
0.06
3 466 observations
|
|
WEU
Western Europe
|
N/A
63%
2 211 observations
+30.9 pp
2 601 observations
0.28
2 601 observations
|
N/A
60.8%
2 202 observations
+26.6 pp
2 535 observations
0.25
2 535 observations
|
N/A
52.6%
2 303 observations
+4.5 pp
2 537 observations
0.07
2 537 observations
|
|
EEU
Eastern Europe
|
N/A
60%
507 observations
+13 pp
571 observations
0.13
571 observations
|
N/A
54.8%
520 observations
+5.4 pp
569 observations
0.04
569 observations
|
N/A
49.5%
505 observations
-8.1 pp
557 observations
-0.02
557 observations
|
|
APAC
Asia-Pacific
|
N/A
61.6%
11 458 observations
+32.1 pp
13 079 observations
0.26
13 079 observations
|
N/A
57.3%
11 571 observations
+20.3 pp
12 906 observations
0.18
12 906 observations
|
N/A
55%
11 717 observations
+12.2 pp
13 036 observations
0.13
13 036 observations
|
|
LATAM
Latin America
|
N/A
67.6%
377 observations
+29.9 pp
455 observations
0.31
455 observations
|
N/A
63.6%
393 observations
+22.4 pp
449 observations
0.28
449 observations
|
N/A
56.3%
382 observations
-4.7 pp
422 observations
0.03
422 observations
|
|
MEA
Middle East & Africa
|
N/A
57.1%
357 observations
+21.8 pp
420 observations
0.18
420 observations
|
N/A
54.2%
365 observations
+10.5 pp
415 observations
0.08
415 observations
|
N/A
45.4%
379 observations
+1.7 pp
412 observations
0.02
412 observations
|
|
United States of America
|
N/A
66.9%
2 583 observations
+42.5 pp
3 010 observations
0.26
3 010 observations
|
N/A
66.6%
2 474 observations
+42.3 pp
2 817 observations
0.28
2 817 observations
|
N/A
57.6%
2 650 observations
+10.8 pp
2 944 observations
0.07
2 944 observations
|
|
India
|
N/A
70.1%
1 134 observations
+39.1 pp
1 342 observations
0.37
1 342 observations
|
N/A
66.8%
1 158 observations
+21.3 pp
1 335 observations
0.26
1 335 observations
|
N/A
56.9%
1 170 observations
+6.8 pp
1 338 observations
0.12
1 338 observations
|
|
United Kingdom
|
N/A
62.6%
428 observations
+16.5 pp
507 observations
0.17
507 observations
|
N/A
60.6%
424 observations
+11.4 pp
490 observations
0.14
490 observations
|
N/A
52.3%
474 observations
+13.2 pp
500 observations
0.09
500 observations
|
|
Japan
|
N/A
63.5%
1 805 observations
+46.7 pp
2 057 observations
0.44
2 057 observations
|
N/A
59.8%
1 852 observations
+40.8 pp
2 052 observations
0.36
2 052 observations
|
N/A
58%
1 821 observations
+18.3 pp
2 058 observations
0.23
2 058 observations
|
|
COMM
Communication Services
|
N/A
61.4%
674 observations
+33 pp
765 observations
0.27
765 observations
|
N/A
57.2%
671 observations
+20.9 pp
749 observations
0.22
749 observations
|
N/A
57%
697 observations
+21 pp
754 observations
0.21
754 observations
|
|
CONS
Consumer Discretionary
|
N/A
60.4%
2 274 observations
+24 pp
2 602 observations
0.25
2 602 observations
|
N/A
56%
2 318 observations
+18.1 pp
2 574 observations
0.18
2 574 observations
|
N/A
55.7%
2 308 observations
+11.2 pp
2 572 observations
0.13
2 572 observations
|
|
CONS
Consumer Staples
|
N/A
58.1%
1 060 observations
+25.2 pp
1 266 observations
0.3
1 266 observations
|
N/A
56.6%
1 097 observations
+23.7 pp
1 257 observations
0.27
1 257 observations
|
N/A
50.6%
1 137 observations
+5.2 pp
1 257 observations
0.09
1 257 observations
|
|
ENER
Energy
|
N/A
64.6%
571 observations
+42.8 pp
639 observations
0.26
639 observations
|
N/A
61.8%
555 observations
+33.3 pp
621 observations
0.22
621 observations
|
N/A
56.8%
577 observations
+14.9 pp
622 observations
0.12
622 observations
|
|
FINA
Financials
|
N/A
69.2%
1 804 observations
+32.3 pp
2 065 observations
0.32
2 065 observations
|
N/A
67%
1 883 observations
+27.9 pp
2 057 observations
0.26
2 057 observations
|
N/A
53.6%
1 729 observations
+2.9 pp
2 014 observations
0.05
2 014 observations
|
|
HEAL
Health Care
|
N/A
59%
1 626 observations
+20.4 pp
1 888 observations
0.16
1 888 observations
|
N/A
57.9%
1 486 observations
+21.4 pp
1 686 observations
0.16
1 686 observations
|
N/A
52.5%
1 673 observations
-2.3 pp
1 867 observations
0.05
1 867 observations
|
|
INDU
Industrials
|
N/A
63.4%
3 528 observations
+37 pp
4 085 observations
0.29
4 085 observations
|
N/A
59.8%
3 572 observations
+28.1 pp
4 047 observations
0.22
4 047 observations
|
N/A
54.6%
3 606 observations
+12.5 pp
4 053 observations
0.11
4 053 observations
|
|
INFO
Information Technology
|
N/A
63.9%
2 496 observations
+82.8 pp
2 833 observations
0.32
2 833 observations
|
N/A
58.7%
2 516 observations
+70.9 pp
2 783 observations
0.22
2 783 observations
|
N/A
59.6%
2 539 observations
+49.1 pp
2 822 observations
0.24
2 822 observations
|
|
MATE
Materials
|
N/A
61.6%
2 129 observations
+23.3 pp
2 417 observations
0.16
2 417 observations
|
N/A
57.7%
2 084 observations
+14.9 pp
2 354 observations
0.11
2 354 observations
|
N/A
54.8%
2 132 observations
+4 pp
2 348 observations
0.08
2 348 observations
|
|
REAL
Real Estate
|
N/A
58%
1 052 observations
+8.9 pp
1 231 observations
0.12
1 231 observations
|
N/A
54.8%
1 066 observations
+5.4 pp
1 230 observations
0.08
1 230 observations
|
N/A
47.5%
1 156 observations
-1.8 pp
1 216 observations
0
1 216 observations
|
|
UTIL
Utilities
|
N/A
66.4%
479 observations
+26.7 pp
575 observations
0.32
575 observations
|
N/A
60.7%
483 observations
+19.9 pp
572 observations
0.22
572 observations
|
N/A
62.7%
539 observations
+16.7 pp
564 observations
0.18
564 observations
|
Metrics are shown as N/A when a scope does not meet the minimum sample or data-quality rules.
Global · 6 regions · 4 countries
11 sectors
Put it to work
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Explore intrinsic value, DCF, multiples-based value, and historical context for every stock you follow.
