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This builds on something I posted a while back about every price implying a growth rate, and on the pushback in that thread, which is a lot of why I ended up caring whether a valuation claim can be checked at all. The idea is simple. For any company you have two numbers. The growth it can fund from its own economics, which is ROIC times reinvestment rate. And the growth the market is requiring, which you pull out of a reverse DCF on the enterprise value. Subtract one from the other and you get a single signed number. I have been calling it the Brina Gap and running it on my own positions for about three years.
Positive gap, the business can grow faster than the price needs. Negative gap, the market is demanding growth the business has no track record of producing. Two numbers, both straight from public filings, no analyst intrinsic -value estimate anywhere in it, which means two people running it on the same stock get the same answer. That is the part that does not exist in a normal DCF.
The reason I built it in growth units rather than as another price multiple is that growth is the only valuation claim you can actually check against the future. "This stock is cheap on a P/E of 12" can never be verified against a later fact. "This price implies the business will grow 12% a year" can. You wait and see what it did. As far as I can tell the Gap is the only standard valuation metric whose central claim about the future is falsifiable at all, so the first thing I tested was whether the implied growth it pulls out of price actually tracks what firms go on to deliver. It does, correlation around 0.4 to 0.5 and nearly unbiased at the ten-year horizon, stable across three different recovery methods. The core number measures something real, not an artifact of my particular DCF setup.
I backtested it on the full point-in-time S&P 500, observation years 2010 to 2019, five-year forward windows, every company carried to its real outcome including the ones that got acquired or went to zero. Then I ranked it against the most celebrated metrics in finance on the exact same sample and the same statistic, correlation with five-year forward returns.
Among every pure valuation metric, the Brina Gap ranked first. It out-sorted the Margin of Safety, earnings yield, Greenwald's EPV, and Fama-French book-to-market, the canonical academic value factor by a wide margin. It also beat gross profitability, ROE, and the Piotroski F-score. The only two metrics above it were ROIC and FCFROIC, which are quality measures, not valuation measures, so they are scoring a different axis entirely. On the pricing axis, the thing every value investor is trying to judge, nothing in the test sorted returns better!
The Margin of Safety, for reference, landed at essentially zero in the same test. The Brina Gap beats it head to head by about 4 points on the full universe and about 5 on the survivor subset, and that margin survives the survivorship correction and the switch to total returns. So on the specific question of whether a price is reasonable, this sorted future returns better than anything else I tested, including the method most of us were taught to use. That is the result I care most about, and it is the one I most want someone to attack.
Now the honest part, because it matters and someone would catch it anyway. No pure pricing metric, the Gap included, produced a large standalone return spread in this sample. What took me a while to appreciate is why. 2010 to 2024 was the most hostile decade on record for pricing metrics. Raw cheapness itself earned a negative return over this period, and book-to-market, earnings yield, every value measure went flat. That the Gap's growth-measurement held up through the exact regime built to punish pricing approaches is, if anything, the more demanding test. It ranked first on an axis where the whole axis was underwater.
Where it has real directional teeth is the short side. Flagging companies the market is pricing for growth they cannot sustain, it called underperformance correctly about 59% of the time. The two negative buckets, value traps and expensive hype, both came in around 58 to 60%, and the value trap cell, cheap stocks that are cheap for a reason, was the single worst-performing group in the entire sample. The long side, picking which cheap stock actually rises, was a coin flip at 48%. The asymmetry has a clean cause. A negative call only needs the market to eventually notice an unsustainable price. A positive call needs the business to keep compounding and the market to reward it. One condition versus two. The pessimistic calls land, the optimistic ones do not, and I did not design that in, the data just did it.
One caveat I will not hide, since someone would find it anyway. Overlapping five-year windows are not independent observations, so the clean-looking p-values flatter the standalone result. Correct for that properly and the edge stays real but the absolute significance gets modest. The comparison against Margin of Safety is the sturdy claim and survives the correction. Including the delisted and acquired firms actually strengthened the short-side screen, which is the opposite of what a fragile signal does.
It is sector-dependent too. Strong where ROIC is stable, utilities and real estate around 72%, weak in technology at 47% where ROIC moves too fast for a steady-state model to mean much. Worth knowing before you point it at a hot growth name.
None of the individual pieces are mine. Damodaran on reinvestment rates and reverse DCF, Greenwald on earnings power, Mauboussin and Rappaport on reading price as an embedded forecast. What I did was combine the reverse DCF with the ROIC-times-reinvestment growth ceiling, make it one falsifiable number, and test it head to head against everything else on a complete universe. It came out the best valuation metric in the test. I would genuinely like someone to pull the data and try to knock it off that spot.
Paper, full dataset, and code below.
Working paper (Zenodo, open access): 10.5281/zenodo.19052189
SSRN: 6361659