Equity valuation
What the market costs, and what that has actually predicted
Robert Shiller’s file carries CAPE and the realised next-ten-year annualised real return in the same row, so the claim can be tested against its own source rather than against a redrawn chart. It is tested below, on the 1,627 months where both numbers exist, and then tested again on the 14 windows that do not overlap each other. Damodaran’s annual Implied ERP (FCFE), monthly ERP (T12m) and country-risk tables sit beside his 2026-01-05 industry snapshot: cost of capital, beta and four multiple families for 94 industries. The same page carries one aggregate innovation block from KPSS patent values, but not a named-firm view, because the file’s firm identifier is CRSP permno and this estate carries no license-clean permno crosswalk.
Data as of 2026-07 (Robert J. Shiller, Irrational Exuberance data)
The level
CAPE is 41.37 in 2026-07, the last month of the file. 1,731 of the 1,747 monthly readings since 1881-01 are at or below that. The median of the series is 16.61, its trough 4.78 in 1920-12, its peak 44.20 in 1999-12.
Source: Robert J. Shiller, Irrational Exuberance monthly data (Yale) CAPE is real price divided by the mean of the trailing 120 months of real earnings. Methodology
Does a high CAPE predict a low return?
Shiller publishes the answer in his own file. The column real_10yr_ann_stock_return holds the annualised real return the market went on to deliver over the ten years after each month. CAPE is null for the first 120 months of the file and the forward return is null for the last 120, so 1,627 months carry both, 1881-01 to 2016-07. That is the entire sample.
Regressed on the CAPE earnings yield (1 / CAPE), those 1,627 months give an R-squared of 0.257. The sign is the one the folklore predicts. The magnitude is not: 74.3% of the variation in ten-year outcomes is left unexplained by the starting valuation.
Source: Robert J. Shiller, Irrational Exuberance monthly data (Yale) Both curves are y = a + b / CAPE. Overlapping fit: a = 0.0069, b = 0.8594. Non-overlapping fit: a = 0.0092, b = 0.8108. Methodology
Why that scatter overstates its own evidence
Two consecutive rows of the forward-return column describe ten-year windows that share 119 of their 120 months. The lag-1 autocorrelation of that column is 0.993; twelve months apart it is still 0.857. The 1,627 points in the scatter above are not 1,627 independent observations, and the t statistic computed as though they were, 23.7, means nothing.
Take the first testable month and every 120th month after it and 14 strictly non-overlapping windows remain: the first begins 1881-01, the last ends 2021-01, and no two of them share a month. On those 14, R-squared is 0.286 and t is 2.19 on 12 degrees of freedom.
The R-squared barely moved: 0.257 on 1,627 overlapping months, 0.286 on 14 independent ones. Overlap did not inflate the fit. What it inflated is the confidence: t falls from 23.7 to 2.19. The two-sided 5 percent critical value of Student’s t with 12 degrees of freedom is 2.179, so the relationship clears the bar by 0.016.
Every specification, both samples
| Predictor of the next 10 years’ real return | Sample | n | r | R-squared | t |
|---|---|---|---|---|---|
| CAPE | overlapping | 1,627 | -0.487 | 0.237 | -22.46 |
| CAPE | non-overlapping | 14 | -0.480 | 0.230 | -1.89 |
| CAPE earnings yield (1 / CAPE) | overlapping | 1,627 | 0.507 | 0.257 | 23.70 |
| CAPE earnings yield (1 / CAPE) | non-overlapping | 14 | 0.535 | 0.286 | 2.19 |
| log CAPE | overlapping | 1,627 | -0.510 | 0.260 | -23.88 |
| log CAPE | non-overlapping | 14 | -0.532 | 0.283 | -2.18 |
| Excess CAPE yield, against the 10-year excess return | overlapping | 1,627 | 0.505 | 0.255 | n/a |
| Excess CAPE yield, against the 10-year excess return | non-overlapping | 14 | 0.516 | 0.266 | n/a |
Source: Robert J. Shiller, Irrational Exuberance monthly data (Yale) t is r * sqrt(n - 2) / sqrt(1 - r squared). Reported for the overlapping sample only to show how far wrong it goes. Excess CAPE yield is Shiller's own column, the CAPE earnings yield minus the real ten-year bond yield. Methodology
What 14 observations can and cannot carry
Refit the same regression 14 times, each time dropping one of the 14 independent windows. R-squared moves between 0.106 and 0.376; t moves between 1.14 and 2.57. The two-sided 5 percent critical value at 11 degrees of freedom is 2.201, and 11 of the 14 refits fall below it. Dropping the 1921-01 window alone takes R-squared to 0.106.
That is the honest state of the evidence. The relationship is real in sign, modest in size, and, once the overlapping windows are removed, fragile: removing one decade from the sample costs it 5 percent significance in 11 of the 14 cases.
| Window dropped | n | R-squared | t | Clears 2.201? |
|---|---|---|---|---|
| 1921-01 | 13 | 0.106 | 1.14 | no |
| 2001-01 | 13 | 0.226 | 1.79 | no |
| 1981-01 | 13 | 0.279 | 2.07 | no |
| 1881-01 | 13 | 0.280 | 2.07 | no |
| 1961-01 | 13 | 0.281 | 2.08 | no |
| 1931-01 | 13 | 0.281 | 2.08 | no |
| 1901-01 | 13 | 0.285 | 2.10 | no |
| 1941-01 | 13 | 0.286 | 2.10 | no |
| 1971-01 | 13 | 0.292 | 2.13 | no |
| 1951-01 | 13 | 0.293 | 2.13 | no |
| 1891-01 | 13 | 0.294 | 2.14 | no |
| 1991-01 | 13 | 0.360 | 2.49 | yes |
| 2011-01 | 13 | 0.375 | 2.57 | yes |
| 1911-01 | 13 | 0.376 | 2.57 | yes |
Source: Robert J. Shiller, Irrational Exuberance monthly data (Yale) Methodology
By quintile, and at today’s level
Cutting the 1,627 overlapping months into CAPE quintiles gives a mean realised return that falls at every step, from 10.8% a year in the cheapest fifth to 3.7% in the most expensive. The ranges still cross: the worst outcome in the cheapest quintile was 1.2% a year, the best in the most expensive was 12.8%. 0 of the 326 months in the cheapest quintile went on to a negative ten-year real return; 89 of the 325 in the most expensive did.
| CAPE quintile | Months | CAPE range | Mean real return | Worst | Best | Negative |
|---|---|---|---|---|---|---|
| 1 (cheapest) | 326 | 4.8 to 11.1 | 10.8% | 1.2% | 20.0% | 0 of 326 |
| 2 | 326 | 11.1 to 14.6 | 7.1% | -4.2% | 15.4% | 37 of 326 |
| 3 | 325 | 14.6 to 17.5 | 6.4% | -4.6% | 16.1% | 35 of 325 |
| 4 | 325 | 17.5 to 21.4 | 5.5% | -4.0% | 14.6% | 32 of 325 |
| 5 (most expensive) | 325 | 21.4 to 44.2 | 3.7% | -5.9% | 12.8% | 89 of 325 |
Source: Robert J. Shiller, Irrational Exuberance monthly data (Yale) Quintiles of the overlapping sample, so the months within a quintile are not independent of each other either. Methodology
The precedent for today’s CAPE
17 months in the whole series carry a CAPE at or above today’s 41.4, spread over 3 calendar years: 1999, 2000, 2026. Only 16 of those 17 are old enough to have a completed ten-year window, and all 16 of them sit inside one 18-month stretch, 1999-04 to 2000-09. Those windows returned between -5.2% and -2.6% a year in real terms, mean -3.5%; 16 of the 16 were negative.
Fitted at today’s CAPE, the 14-window regression returns 2.9% a year real over the coming decade. The residual standard deviation of that fit is 5.4 points a year. Shiller’s excess CAPE yield stands at 1.39%, against a ten-year Treasury of 4.44%.
Source: Robert J. Shiller, Irrational Exuberance monthly data (Yale) Methodology
The industry cross-section
Damodaran’s data set is one snapshot. Every row carries the vintage 2026-01-05 and the file has no other date column, so nothing below can be sliced through time. It covers 94 industries in two regions, plus two aggregate rows that are not industries. The 94 US industries hold 5,994 firms and sum exactly to the count the Total Market row reports; the Global set holds 48,156.
Cost of equity here is a formula, not an estimate
Across the 94 US industries, cost of equity equals 3.950% + 4.460% × beta, with a maximum absolute residual of 3.5e-17: exact to machine precision. Across the 94 Global industries the same identity holds with a slope of 5.630%. Read as CAPM, the intercept is the risk-free rate and the slope is the equity risk premium, and both are the same number for every industry in a region. The cross-section of cost of equity therefore carries exactly the information in the cross-section of beta and nothing else. Cost of debt is coarser still: it takes 4 distinct values across the 94 US industries and 4 across the 94 Global ones.
Source: Aswath Damodaran, NYU Stern, current industry data Methodology
US industry cost of capital runs from 4.36% (Utility (General)) to 10.66% (Software (Internet)), median 7.09%. Global runs from 4.99% (Banks (Regional)) to 15.31% (Semiconductor Equip), median 7.67%.
The PE column that is not the market’s PE
Damodaran ships two different things both called PE. The current, trailing and forward columns are averages across firms; the aggregate columns are total market capitalisation divided by total net income. For the US Total Market row they read 72.2 and 34.2. The second is what a dollar of the market’s earnings costs. The first is not.
The same file says why an average across firms is not the market’s ratio: 57.2% of the 5,994 US firms had negative trailing net income, and 56 of the 94 US industries had more than half their firms losing money. Trailing PE is null in exactly the 3 US industries in which every firm lost money, and nowhere else in the US (0 mismatches in either direction). An average-of-firms PE is an average over whichever firms had positive earnings.
The aggregate ratio comes in two forms. Across all 5,994 US firms it is 34.2; across the profitable ones only it is 26.6.
Source: Aswath Damodaran, NYU Stern, current industry data Methodology
All 94 industries
US figures. Every row links to the full US and Global detail.
| Industry | US firms | Beta | Cost of capital | Aggregate PE | P/B | EV/EBITDA | Money-losing firms |
|---|---|---|---|---|---|---|---|
| Advertising | 52 | 1.21 | 7.81% | 460.5 | 4.6 | 15.1 | 79% |
| Aerospace/Defense | 79 | 0.95 | 7.60% | 78.1 | 7.9 | 33.4 | 51% |
| Air Transport | 23 | 1.19 | 6.72% | 26.1 | 2.8 | 10.0 | 61% |
| Apparel | 35 | 0.94 | 7.13% | 41.2 | 3.9 | 13.9 | 63% |
| Auto & Truck | 33 | 1.46 | 9.38% | 148.5 | 7.7 | 51.8 | 82% |
| Auto Parts | 35 | 1.34 | 8.18% | 47.0 | 1.9 | 9.1 | 49% |
| Bank (Money Center) | 15 | 0.76 | 4.98% | 15.0 | 1.6 | n/a | 7% |
| Banks (Regional) | 568 | 0.40 | 4.98% | 15.0 | 1.1 | n/a | 14% |
| Beverage (Alcoholic) | 14 | 0.81 | 6.48% | 24.3 | 1.9 | 8.3 | 64% |
| Beverage (Soft) | 27 | 0.64 | 6.33% | 26.9 | 6.9 | 18.2 | 70% |
| Broadcasting | 24 | 0.47 | 5.09% | n/a | 1.7 | 7.7 | 71% |
| Brokerage & Investment Banking | 32 | 1.17 | 6.08% | 24.4 | 2.8 | n/a | 22% |
| Building Materials | 41 | 1.11 | 7.85% | 14.7 | 3.6 | 11.8 | 29% |
| Business & Consumer Services | 155 | 0.89 | 7.23% | 30.8 | 5.3 | 16.2 | 57% |
| Cable TV | 9 | 0.74 | 5.20% | 8.7 | 1.3 | 6.4 | 44% |
| Chemical (Basic) | 29 | 1.01 | 6.22% | 18.5 | 1.1 | 6.9 | 76% |
| Chemical (Diversified) | 4 | 0.85 | 5.23% | n/a | 1.1 | 8.7 | 100% |
| Chemical (Specialty) | 59 | 0.97 | 7.25% | 48.1 | 2.4 | 14.7 | 59% |
| Coal & Related Energy | 16 | 1.07 | 8.41% | 52.2 | 2.2 | 19.5 | 81% |
| Computer Services | 64 | 1.09 | 7.83% | 30.2 | 4.7 | 16.5 | 55% |
| Computers/Peripherals | 36 | 1.35 | 9.71% | 36.2 | 34.1 | 26.2 | 61% |
| Construction Supplies | 40 | 1.15 | 8.29% | 21.9 | 5.5 | 16.6 | 30% |
| Diversified | 20 | 0.88 | 7.30% | 13.4 | 1.8 | 9.8 | 80% |
| Drugs (Biotechnology) | 496 | 1.14 | 8.49% | n/a | 8.2 | 51.5 | 90% |
| Drugs (Pharmaceutical) | 228 | 0.98 | 7.85% | 58.9 | 6.6 | 18.6 | 85% |
| Education | 32 | 0.78 | 6.75% | 39.3 | 2.6 | 12.0 | 50% |
| Electrical Equipment | 112 | 1.25 | 8.99% | n/a | 6.5 | 35.0 | 75% |
| Electronics (Consumer & Office) | 8 | 0.87 | 7.63% | n/a | 3.9 | n/a | 100% |
| Electronics (General) | 114 | 0.97 | 7.85% | 56.7 | 5.0 | 25.9 | 60% |
| Engineering/Construction | 48 | 1.21 | 8.69% | 36.9 | 5.5 | 21.8 | 40% |
| Entertainment | 92 | 0.83 | 7.13% | n/a | 4.8 | 24.4 | 83% |
| Environmental & Waste Services | 53 | 0.95 | 7.43% | 37.7 | 6.6 | 17.6 | 79% |
| Farming/Agriculture | 35 | 1.13 | 7.27% | 24.2 | 2.4 | 16.7 | 66% |
| Financial Svcs. (Non-bank & Insurance) | 176 | 0.97 | 5.00% | 22.9 | 3.7 | 89.9 | 41% |
| Food Processing | 78 | 0.61 | 5.79% | 16.8 | 1.8 | 9.6 | 60% |
| Food Wholesalers | 13 | 0.87 | 6.53% | 27.1 | 4.9 | 12.3 | 54% |
| Furn/Home Furnishings | 27 | 0.82 | 6.53% | 85.4 | 2.5 | 13.7 | 52% |
| Green & Renewable Energy | 15 | 0.86 | 6.04% | 66.4 | 1.3 | 13.5 | 87% |
| Healthcare Products | 204 | 0.91 | 7.54% | 46.5 | 4.4 | 23.4 | 76% |
| Healthcare Support Services | 104 | 0.87 | 6.83% | 23.6 | 2.8 | 11.8 | 63% |
| Heathcare Information and Technology | 115 | 1.11 | 8.22% | 109.9 | 4.0 | 25.9 | 77% |
| Homebuilding | 30 | 0.91 | 7.27% | 10.2 | 1.6 | 8.4 | 20% |
| Hospitals/Healthcare Facilities | 31 | 0.80 | 6.19% | 16.7 | 5.6 | 10.7 | 52% |
| Hotel/Gaming | 63 | 1.08 | 7.36% | 31.7 | 12.3 | 17.7 | 54% |
| Household Products | 110 | 0.82 | 7.03% | 24.2 | 5.8 | 13.7 | 72% |
| Information Services | 15 | 0.92 | 7.00% | 25.8 | 3.8 | 12.8 | 40% |
| Insurance (General) | 21 | 0.67 | 6.34% | 30.0 | 3.5 | 18.3 | 24% |
| Insurance (Life) | 20 | 0.64 | 5.60% | 10.2 | 1.4 | 11.2 | 25% |
| Insurance (Prop/Cas.) | 57 | 0.48 | 5.78% | 16.5 | 2.0 | 10.1 | 14% |
| Investments & Asset Management | 283 | 0.66 | 6.13% | 24.8 | 2.5 | 47.1 | 58% |
| Machinery | 105 | 0.96 | 7.70% | 26.2 | 4.5 | 17.5 | 43% |
| Metals & Mining | 73 | 1.04 | 8.20% | 45.6 | 4.3 | 13.7 | 85% |
| Office Equipment & Services | 14 | 1.33 | 7.92% | 55.6 | 2.9 | 10.0 | 43% |
| Oil/Gas (Integrated) | 4 | 0.30 | 5.07% | 15.7 | 1.7 | 8.1 | 0% |
| Oil/Gas (Production and Exploration) | 142 | 0.72 | 6.25% | 15.2 | 1.4 | 6.2 | 59% |
| Oil/Gas Distribution | 23 | 0.67 | 5.78% | 21.6 | 2.9 | 13.4 | 43% |
| Oilfield Svcs/Equip. | 97 | 0.95 | 7.04% | 19.0 | 1.9 | 9.6 | 58% |
| Packaging & Container | 19 | 1.02 | 6.75% | 12.7 | 2.2 | 10.8 | 37% |
| Paper/Forest Products | 6 | 0.96 | 6.93% | 11.0 | 1.8 | 7.0 | 50% |
| Power | 46 | 0.48 | 5.01% | 23.1 | 2.3 | 13.3 | 11% |
| Precious Metals | 56 | 0.84 | 7.47% | 50.9 | 3.6 | 17.3 | 86% |
| Publishing & Newspapers | 19 | 0.56 | 5.95% | 21.1 | 2.3 | 12.9 | 53% |
| R.E.I.T. | 190 | 0.64 | 5.32% | 56.1 | 2.0 | 24.3 | 34% |
| Real Estate (Development) | 14 | 0.84 | 5.82% | 23.3 | 1.0 | 12.2 | 79% |
| Real Estate (General/Diversified) | 12 | 0.81 | 6.25% | 41.7 | 1.2 | 25.1 | 58% |
| Real Estate (Operations & Services) | 54 | 0.97 | 7.41% | 405.5 | 3.1 | 37.0 | 59% |
| Recreation | 49 | 1.02 | 6.76% | 96.0 | 4.0 | 11.7 | 57% |
| Reinsurance | 1 | 0.58 | 5.64% | 18.6 | 1.0 | 10.9 | 0% |
| Restaurant/Dining | 64 | 0.92 | 7.16% | 36.4 | 74.7 | 21.4 | 47% |
| Retail (Automotive) | 34 | 0.94 | 6.78% | 27.4 | 7.5 | 17.6 | 56% |
| Retail (Building Supply) | 14 | 1.54 | 9.51% | 23.4 | 132.2 | 15.9 | 50% |
| Retail (Distributors) | 62 | 0.95 | 7.22% | 25.4 | 4.0 | 16.7 | 45% |
| Retail (General) | 23 | 0.81 | 7.27% | 43.0 | 7.2 | 20.9 | 26% |
| Retail (Grocery and Food) | 15 | 1.12 | 7.24% | 17.7 | 3.9 | 9.1 | 40% |
| Retail (REITs) | 26 | 0.62 | 5.57% | 35.7 | 2.1 | 17.9 | 8% |
| Retail (Special Lines) | 94 | 1.09 | 8.01% | 27.7 | 7.3 | 16.5 | 62% |
| Rubber& Tires | 3 | 0.53 | 4.48% | 42.7 | 0.8 | 6.7 | 100% |
| Semiconductor | 66 | 1.52 | 10.55% | 77.4 | 13.3 | 42.7 | 62% |
| Semiconductor Equip | 31 | 1.40 | 9.89% | 35.9 | 9.7 | 26.2 | 45% |
| Shipbuilding & Marine | 8 | 0.75 | 6.69% | 12.9 | 1.5 | 8.5 | 63% |
| Shoe | 11 | 1.02 | 8.01% | 22.5 | 6.1 | 17.2 | 36% |
| Software (Entertainment) | 77 | 1.03 | 8.44% | 34.5 | 9.1 | 26.2 | 71% |
| Software (Internet) | 29 | 1.69 | 10.66% | n/a | 10.9 | 100.4 | 72% |
| Software (System & Application) | 309 | 1.28 | 9.34% | 49.1 | 9.1 | 31.8 | 70% |
| Steel | 19 | 1.06 | 7.76% | 23.6 | 1.9 | 11.0 | 37% |
| Telecom (Wireless) | 12 | 0.54 | 5.48% | 21.4 | 3.5 | 10.8 | 75% |
| Telecom. Equipment | 57 | 0.92 | 7.72% | 48.0 | 8.4 | 27.2 | 68% |
| Telecom. Services | 39 | 0.63 | 5.39% | 14.4 | 1.6 | 7.5 | 79% |
| Tobacco | 10 | 0.79 | 6.94% | 19.0 | n/a | 15.1 | 50% |
| Transportation | 19 | 0.86 | 6.72% | 24.4 | 4.8 | 16.7 | 53% |
| Transportation (Railroads) | 4 | 0.98 | 7.27% | 21.5 | 5.8 | 13.6 | 25% |
| Trucking | 26 | 1.01 | 7.52% | 34.6 | 2.9 | 11.2 | 50% |
| Utility (General) | 14 | 0.24 | 4.36% | 21.7 | 1.8 | 15.0 | 0% |
| Utility (Water) | 14 | 0.41 | 4.93% | 22.0 | 2.0 | 15.7 | 29% |
Source: Aswath Damodaran, NYU Stern, current industry data Aggregate PE is total market capitalisation over total net income across all firms in the industry, not the average of firm PEs. Blank cells are missing in the source. Vintage 2026-01-05. Methodology
The market value of innovation
Kogan, Papanikolaou, Seru and Stoffman infer a patent’s value from the stock market reaction to its grant. The file on this box runs from 1926 to 2024 and holds 3,419,552 patents, but its firm identifier is CRSP permno. CRSP is licensed, no permno-to-name crosswalk exists in this estate, and this page does not try to reconstruct one. What can be shown honestly is the aggregate story: yearly totals, the yearly value distribution, the count of distinct patenting firms, and citation buckets that stop before the right-censored tail.
Data as of 2024 issue year in the authors' December 7, 2025 release, citations updated through 2024
The sum is a tail story. In 2024, total real innovation value reached $2969.74bn, but the median patent in the same year was worth $7.9m and the full-sample median is only $3.5m. The 99th percentile over the full sample is $132.2m, which is why the distribution has to sit beside the sum rather than behind it.
Source: Kogan, Papanikolaou, Seru and Stoffman (2017, QJE) | Authors' extended patent data through 2024 xi_nominal is millions of current dollars; xi_real is the authors' CPI-deflated 1982-dollar measure in millions. Firms are not named here because permno is a CRSP identifier and no license-clean permno crosswalk exists on this box. Methodology
The distribution beside the sum
Recent yearly totals rose sharply after 2019, while the breadth of patenting firms did not. The table keeps the sum beside the median, 90th percentile and 99th percentile so the tail is visible.
| Issue year | Patents | Distinct firms | Sum, 1982 USD bn | Median, 1982 USD m | P90, 1982 USD m | P99, 1982 USD m |
|---|---|---|---|---|---|---|
| 2024 | 92,115 | 1,260 | 2969.74 | 7.85 | 75.6 | 317.1 |
| 2023 | 94,612 | 1,385 | 2379.31 | 6.13 | 69.3 | 231.4 |
| 2022 | 99,498 | 1,400 | 2173.23 | 5.95 | 65.6 | 193.2 |
| 2021 | 97,948 | 1,401 | 2730.40 | 9.62 | 80.4 | 229.1 |
| 2020 | 108,037 | 1,338 | 2121.56 | 6.47 | 54.0 | 167.3 |
| 2019 | 104,945 | 1,344 | 1484.01 | 5.60 | 37.1 | 122.5 |
| 2018 | 94,321 | 1,290 | 1229.55 | 4.88 | 33.3 | 119.0 |
| 2017 | 101,406 | 1,323 | 1018.85 | 3.90 | 23.7 | 102.5 |
| 2016 | 101,339 | 1,396 | 1014.53 | 3.81 | 23.5 | 103.1 |
| 2015 | 103,452 | 1,449 | 948.95 | 3.67 | 21.9 | 90.9 |
Source: Kogan, Papanikolaou, Seru and Stoffman (2017, QJE) | Authors' extended patent data through 2024 Rows are sorted newest first. The yearly sum is in billions of 1982 dollars; the distribution columns are in millions of 1982 dollars. Methodology
Citations and value, before the truncation wall
For patents issued through 2019, higher forward-citation buckets also carry higher patent values. The median patent in the zero-citation bucket is $2.47m; in the 50+-citation bucket it is $6.04m. This cut stops at 2019 because the file’s cites counts are only observed through 2024, and recent patents have not had time to be cited.
| Forward citations | Patents | Median value, 1982 USD m | P90 value, 1982 USD m | Average value, 1982 USD m |
|---|---|---|---|---|
| 0 | 462,791 | 2.47 | 20.8 | 8.79 |
| 1 | 313,476 | 2.56 | 18.7 | 8.11 |
| 2-4 | 617,625 | 2.76 | 18.8 | 8.27 |
| 5-9 | 543,641 | 3.03 | 19.8 | 8.80 |
| 10-19 | 461,258 | 3.52 | 22.6 | 10.09 |
| 20-49 | 347,514 | 4.41 | 28.0 | 12.75 |
| 50+ | 181,037 | 6.04 | 39.8 | 18.76 |
Source: Kogan, Papanikolaou, Seru and Stoffman (2017, QJE) | Authors' extended patent data through 2024 Citation buckets use patents issued through 2019 only, so each patent had at least five calendar years to accumulate forward citations in the 2024-vintage file. Methodology
What this data cannot tell you
- The two halves of this page are different vintages. Shiller’s file ends 2026-07. The Damodaran snapshot is dated 2026-01-05. No sentence should put a number from one beside a number from the other, and none here does.
- CAPE is not comparable across countries or accounting regimes. It is a ratio of a price index to a ten-year average of reported earnings. Two markets whose firms write down goodwill differently, or whose index composition differs, produce CAPEs that cannot be ranked against each other. This page shows one market only: the S&P composite.
- Shiller’s earnings are trailing and smoothed. The denominator of CAPE at any month is the mean of the previous 120 months of real earnings, so a CAPE reading responds to a profit collapse slowly and by construction.
earnings_nominalis missing for the last 4 months of the file (it ends 2026-03) whilecaperuns to 2026-07. - The forward-return test rests on 14 independent windows. Everything else is the same data counted more than once. 11 of the 14 leave-one-out refits fail to clear the 5 percent bar. Any claim about significance here is a claim about 12 degrees of freedom.
- Damodaran cannot be time-sliced. One vintage, 2026-01-05. The file’s per-year beta columns are withheld for the labeling reason documented below, and no other column carries a date, so these pages show no industry trend, no re-rating, and no before-and-after.
- KPSS cannot identify a named firm here. The patent file carries CRSP
permno, not a ticker or legal name. This estate carries no license-clean permno crosswalk, so the innovation block is aggregate only by design. - Recent citation counts are right-censored. The authors’
citesfield is updated through 2024. A patent issued in 2023 or 2024 has had little or no time to accumulate forward citations, so the citation table stops at 2019. - Cells are missing. All 35 data columns these pages display were counted for nulls across the 188 industry-region rows of their tables. The 14 that have any are named in full below. Nulls are shown as “n/a” and never imputed:
Column Nulls Industry (region) agg_mktcap_to_net_income_all_firms 10 of 188 Broadcasting (Global); Drugs (Biotechnology) (Global); Real Estate (Development) (Global); Broadcasting (US); Chemical (Diversified) (US); Drugs (Biotechnology) (US); Electrical Equipment (US); Electronics (Consumer & Office) (US); Entertainment (US); Software (Internet) (US) ev_ebit_all_firms 9 of 188 Bank (Money Center) (Global); Banks (Regional) (Global); Brokerage & Investment Banking (Global); Bank (Money Center) (US); Banks (Regional) (US); Brokerage & Investment Banking (US); Coal & Related Energy (US); Electronics (Consumer & Office) (US); Software (Internet) (US) peg_ratio 9 of 188 Shipbuilding & Marine (Global); Chemical (Diversified) (US); Electronics (Consumer & Office) (US); Insurance (Life) (US); Paper/Forest Products (US); Real Estate (Development) (US); Real Estate (General/Diversified) (US); Reinsurance (US); Rubber& Tires (US) ev_ebit_pos_ebitda_firms 8 of 188 Bank (Money Center) (Global); Banks (Regional) (Global); Brokerage & Investment Banking (Global); Bank (Money Center) (US); Banks (Regional) (US); Brokerage & Investment Banking (US); Coal & Related Energy (US); Electronics (Consumer & Office) (US) roic 8 of 188 Bank (Money Center) (Global); Banks (Regional) (Global); Brokerage & Investment Banking (Global); Financial Svcs. (Non-bank & Insurance) (Global); Bank (Money Center) (US); Banks (Regional) (US); Brokerage & Investment Banking (US); Financial Svcs. (Non-bank & Insurance) (US) ev_ebitda_all_firms 7 of 188 Bank (Money Center) (Global); Banks (Regional) (Global); Brokerage & Investment Banking (Global); Bank (Money Center) (US); Banks (Regional) (US); Brokerage & Investment Banking (US); Electronics (Consumer & Office) (US) ev_ebitda_pos_ebitda_firms 6 of 188 Bank (Money Center) (Global); Banks (Regional) (Global); Brokerage & Investment Banking (Global); Bank (Money Center) (US); Banks (Regional) (US); Brokerage & Investment Banking (US) agg_mktcap_to_net_income_profitable_firms 3 of 188 Chemical (Diversified) (US); Electronics (Consumer & Office) (US); Rubber& Tires (US) expected_growth_5yr 3 of 188 Chemical (Diversified) (US); Real Estate (General/Diversified) (US); Reinsurance (US) std_dev_operating_income_10yr 3 of 188 Bank (Money Center) (US); Brokerage & Investment Banking (US); Electronics (Consumer & Office) (US) trailing_pe 3 of 188 Chemical (Diversified) (US); Electronics (Consumer & Office) (US); Rubber& Tires (US) roe 2 of 188 Retail (Building Supply) (US); Tobacco (US) current_pe 1 of 188 Electronics (Consumer & Office) (US) price_to_book 1 of 188 Tobacco (US) - The multi-year beta columns are withheld.
yr_2022throughyr_2025andaverage_beta_multiyearexist in the source. The US spreadsheet labels the average column “Average (2022-2026)” and the Global spreadsheet labels the same column “Average (2020-24)”, so it does not span the same years in the two regions. It is also not the arithmetic mean of the four year columns: across the 192 rows it equals that mean in 0 of them. Rather than print a label that would be wrong for one region or the other, the five columns are omitted.
Full construction, the trap register and the reproduction checks are on the methodology page.