EPV
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Right, and this bites harder than the ROIC denominator issue because it hits both arms at once. An insurance broker hiring producers, or a software company paying developers, is making a growth investment that runs entirely through the P&L. Capex-based reinvestment reads near zero, NOPAT is depressed by the same spend and the fundamental ceiling comes out well below what the business can actually do.
The direction of the error is at least predictable. It understates fundamental growth for people-driven and IP-driven businesses, pushes the gap negative, and has the framework calling them overvalued when they may not be. Same direction as the capital-light bias and the same sectors, so the two stack. That’s a good part of why technology came in at 47% while capital-intensive sectors ran around 72%. In mining or logistics or utilities, capex genuinely is the growth engine, so the measure is doing what it claims.
The standard remedy is capitalizing R&D and some portion of S&M and rebuilding invested capital and NOPAT from there, which is Damodaran’s approach. That’s the right refinement and a version of it is worth building in. Your inconsistent-capitalization point is the harder one though, since even with a correction applied, two software firms with different policies on what they capitalize won’t be comparable until you normalize them.
Worth saying this isn’t a problem unique to reinvestment-based measures either. Book-to-market takes the same intangibles distortion straight into the numerator, EPV inherits it through normalized earnings, and any multiple built on reported book or earnings carries it. All of them were tested on the same sample with the same accounting, and the Gap still sorted forward returns better. Accounting distorts every one of these inputs, so what I could actually test was whose version holds up best under it.
Where I’d push back slightly is that this tells you which businesses the measure describes rather than undermining it. Where capex is the growth mechanism it works as intended, and those are the sectors the backtest is strongest in. I’d rather name the boundary than apply the thing everywhere and hope.

Both of those are real, and the intangibles one is a measurement problem rather than a modelling quibble.
On reinvestment, the framework doesn’t need ROIC to persist. It takes current ROIC times reinvestment as a ceiling and asks whether the price is already demanding more than that. Decay works in my favour on the short side: if a company needs 15% growth to justify its price and its best-ever economics only fund 9%, the call gets stronger as returns fade, not weaker.
The bigger thing though is that every valuation metric carries this problem, mine included. A DCF needs you to forecast growth and margins for a decade. Margin of Safety needs an intrinsic value estimate that two analysts will put 40% apart. Book-to-market is more distorted by intangibles than ROIC is, since it puts the mismeasured number directly in the numerator. Every one of them is using the past to say something about the future.
What I could do is test whose version of the problem hurts least. Same sample, same statistic, everything measured point-in-time. The Gap sorted forward returns better than Margin of Safety, book-to-market, EPV and earnings yield, and better than gross profitability and Piotroski too. Margin of Safety came in at roughly zero. So the ROIC assumption is doing less damage than the assumptions inside the alternatives, which is the comparison that actually matters when you have to pick something to use.
Your big tech point is right about where it breaks down and the sector splits show it: technology weakest at 47%, utilities and real estate around 72%. A steady-state model only means something in a business that’s actually near one. I’d rather know that bo

I tested a valuation metric against the Margin of Safety, book-to-market, EPV, and earnings yield on the full S&P 500. It came out on top of all of them.

Right, and this bites harder than the ROIC denominator issue because it hits both arms at once. An insurance broker hiring producers, or a software company paying developers, is making a growth investment that runs entirely through the P&L. Capex-based reinvestment reads near zero, NOPAT is depressed by the same spend and the fundamental ceiling comes out well below what the business can actually do.
The direction of the error is at least predictable. It understates fundamental growth for people-driven and IP-driven businesses, pushes the gap negative, and has the framework calling them overvalued when they may not be. Same direction as the capital-light bias and the same sectors, so the two stack. That’s a good part of why technology came in at 47% while capital-intensive sectors ran around 72%. In mining or logistics or utilities, capex genuinely is the growth engine, so the measure is doing what it claims.
The standard remedy is capitalizing R&D and some portion of S&M and rebuilding invested capital and NOPAT from there, which is Damodaran’s approach. That’s the right refinement and a version of it is worth building in. Your inconsistent-capitalization point is the harder one though, since even with a correction applied, two software firms with different policies on what they capitalize won’t be comparable until you normalize them.
Worth saying this isn’t a problem unique to reinvestment-based measures either. Book-to-market takes the same intangibles distortion straight into the numerator, EPV inherits it through normalized earnings, and any multiple built on reported book or earnings carries it. All of them were tested on the same sample with the same accounting, and the Gap still sorted forward returns better. Accounting distorts every one of these inputs, so what I could actually test was whose version holds up best under it.
Where I’d push back slightly is that this tells you which businesses the measure describes rather than undermining it. Where capex is the growth mechanism it works as intended, and those are the sectors the backtest is strongest in. I’d rather name the boundary than apply the thing everywhere and hope.

Both of those are real, and the intangibles one is a measurement problem rather than a modelling quibble.
On reinvestment, the framework doesn’t need ROIC to persist. It takes current ROIC times reinvestment as a ceiling and asks whether the price is already demanding more than that. Decay works in my favour on the short side: if a company needs 15% growth to justify its price and its best-ever economics only fund 9%, the call gets stronger as returns fade, not weaker.
The bigger thing though is that every valuation metric carries this problem, mine included. A DCF needs you to forecast growth and margins for a decade. Margin of Safety needs an intrinsic value estimate that two analysts will put 40% apart. Book-to-market is more distorted by intangibles than ROIC is, since it puts the mismeasured number directly in the numerator. Every one of them is using the past to say something about the future.
What I could do is test whose version of the problem hurts least. Same sample, same statistic, everything measured point-in-time. The Gap sorted forward returns better than Margin of Safety, book-to-market, EPV and earnings yield, and better than gross profitability and Piotroski too. Margin of Safety came in at roughly zero. So the ROIC assumption is doing less damage than the assumptions inside the alternatives, which is the comparison that actually matters when you have to pick something to use.
Your big tech point is right about where it breaks down and the sector splits show it: technology weakest at 47%, utilities and real estate around 72%. A steady-state model only means something in a business that’s actually near one. I’d rather know that bo

I tested a valuation metric against the Margin of Safety, book-to-market, EPV, and earnings yield on the full S&P 500. It came out on top of all of them.