SledgeKey Backtests
Does the Magic Formula actually work? We backtested it honestly.
The part that should bother you
The Magic Formula lost to an index fund. For nearly ten years.
Run Joel Greenblatt's famous screen honestly, with every company that later delisted still in the universe and still held to its last traded price, and it returns 14.74% a year. The S&P 500 returned 15.28% over the same window.
Run it the flattering way, against only the companies still listed today, and the number does not move. This screen takes the twenty largest companies that clear its two thresholds, and over these nine and three quarter years not one of those holdings left the exchange, so the survivor-only universe and the honest one produce the same portfolio at every rebalance.
That is the result nobody selling a formula leads with: a sensible, disciplined value screen, run with real rigor over a real decade, made good money and still finished about half a point a year behind a plain index fund.
The measurement problem is real too, and it is not a constant you can subtract, or even a sign you can assume. On this screen the honest and survivor-only runs are identical. On a Piotroski-style screen the survivor-only run overstates the result by 3.06 points a year, phantom performance worth $74,005 on a $100,000 stake. On midcap value the bias runs the other way: the honest run beat the survivor-only run by 1.51 points a year, because the companies it held that later delisted were mostly bought out at a premium, and a universe that forgets them forgets the premiums too. A smallcap value run came out 0.49 points in that same direction, and a deep value screen on our homepage 1.07 points in the other. What drives the bias is not how many of your holdings died. It is how they died, and no survivor-only database can tell you that.
One note on sourcing. The Magic Formula, Piotroski, and midcap numbers above each have a full public writeup you can check. The smallcap run is internal for now and appears on the midcap page as a cross-reference.
So the question worth your time is not whether Greenblatt's formula works. It is whether the number your own backtest gave you was ever real.
Run this screen with the dead companies left inFree account, no card. Loads this exact screen, ready to change.
The Magic Formula comes from Joel Greenblatt's The Little Book That Beats the Market. The idea is to buy good companies at cheap prices by ranking every stock on two things at once, how cheap it is against its operating earnings and how much it earns on the capital it puts to work, then holding the names that score best on the two measures combined. The book made it famous by reporting that this simple, mechanical portfolio beat the market by a wide margin over its test years, which is exactly the kind of claim worth checking against honest data.
We ran a version of the Magic Formula over nearly ten years, January 2017 to September 2026. Our screen keeps Greenblatt's two pillars, buying companies that are cheap on enterprise value relative to EBITDA and that earn a high return on their assets, and it holds twenty of them in equal weight, rebalanced once a year. The part that matters is how the universe was built. Every company listed on each rebalance date was eligible, including the ones that were later bought out, sanctioned off the exchange, or delisted for any other reason, and any holding that left the market was booked out at its last traded price rather than erased from the record. That is the opposite of how most free backtesters work, and it is the difference between a flattering number and an honest one.
Over this window the screen turned $100,000 into $379,350, a return of 14.74% a year. A plain S&P 500 index fund still did better, by about half a point a year, and measuring that gap honestly is the whole point of running it this way.
Growth of $100,000 · 2017–2026
The Magic Formula screen against the S&P 500, both starting from $100,000 in January 2017, with delisted companies kept in the strategy the whole way through.
View the data as a table (calendar year-end values)
| Year | Magic Formula | S&P 500 |
|---|---|---|
| 2017 | $122,674 | $119,995 |
| 2018 | $136,726 | $127,361 |
| 2019 | $155,968 | $148,109 |
| 2020 | $179,084 | $175,813 |
| 2021 | $198,770 | $219,434 |
| 2022 | $200,759 | $201,434 |
| 2023 | $223,467 | $230,582 |
| 2024 | $291,883 | $306,717 |
| 2025 | $331,171 | $350,488 |
| 2026 | $379,350 | $397,034 |
The exact screen
The full configuration is below exactly as it ran, so anyone who wants to reproduce the result or argue with it can start from the same table we did.
| Value filter | EV/EBITDA of 16 or lower (trailing twelve months) |
| Quality filter | Return on assets of 5% or higher (trailing twelve months) |
| Market cap | $1 billion and up |
| Selection | The 20 qualifying companies with the largest market cap at each rebalance |
| Weighting | Equal weight, 10% maximum position size |
| Rebalance | Once a year (10 rebalances over the window) |
| Transaction cost | 0.10% per trade ($1,967 in modeled costs over the run) |
| Initial capital | $100,000 |
| Benchmark | SPY, the S&P 500, over the same window |
| Universe | NYSE + NASDAQ operating companies (no SPACs, REITs, ETFs, or funds). Point-in-time: eligibility at each rebalance reflects the companies listed on that date. |
| Delisting treatment | Any holding that later delisted was booked out at its frozen last traded price, never dropped from the history. |
Two things separate this from the letter of Greenblatt's method. He ranks the whole universe on both measures and buys the best combined scores, where our screen sets a threshold on each measure and then takes the twenty largest companies that clear both. He also defines cheapness as earnings before interest and taxes over enterprise value and pairs it with a specific return-on-capital formula, where we use EV/EBITDA and return on assets, which are close relatives of those measures rather than exact matches. The spirit is the same, cheap companies that earn well on their capital, and the shape of the result carries over, though a strict rank-based build of the formula could land at a different final number.
The result against the index
| Metric | Magic Formula | S&P 500 |
|---|---|---|
| Total return | 279.35% | 297.03% |
| Annual return (CAGR) | 14.74% | 15.28% |
| Final value | $379,350 | $397,034 |
| Sharpe ratio | 0.88 | 0.81 |
| Volatility (ann.) | 13.89% | 16.18% |
| Max drawdown | -27.97% | -33.72% |
| Calmar ratio | 0.53 | 0.45 |
| Winning months | 79 of 116 | 77 of 116 |
| Total trades | 234 | n/a |
| Avg holding period | 695 days | n/a |
| Annual turnover | 97.4% | n/a |
A note on survivorship. Run this same screen the way most free backtesters quietly do, against only the companies still listed today, and it reports the same 14.74% a year. That is unusual, and it is worth understanding rather than celebrating. The screen holds the twenty largest qualifying companies, and large, cheap, profitable companies rarely leave the exchange; over this window none of its holdings did, so there was nothing for a survivor-only universe to forget. On screens that reach further down the market the two runs part ways, and not always in the direction the textbooks predict: the Piotroski screen is overstated by 3.06 points a year in a survivor-only universe, while midcap value is understated by 1.51.
Your own screen has a number like this. You have almost certainly never seen it, because the tool you ran it in deleted the evidence before you asked.
Find out what yours isThe companies it held that later delisted
None. Every company this screen held between January 2017 and September 2026 is still listed today. That is a property of the selection rule, the twenty largest companies clearing both thresholds, more than a virtue of the formula: companies of that size get bought out less often than midcaps and almost never fail outright. It also means this page cannot show you survivorship bias at work. For that, read the Piotroski and midcap value writeups, where the honest and survivor-only runs diverge by three points in one direction and a point and a half in the other.
Now run yours.
Every setting on this page is reproducible in the app, on the same point-in-time universe with delisted companies kept in. Load this exact screen in one tap, then change whatever you disagree with.
Load the Magic Formula screenFree Starter account, no card, no expiry. 2 years of point-in-time history on Starter, the full decade on SledgeKey+ at $220 a year.
What this backtest does not prove
A page like this earns its credibility in this section, so here is what the numbers above do not establish.
The strategy lost to the index. Nearly fifteen percent a year over almost a decade is real money, and it still fell short of simply owning the S&P 500, which returned about half a point more a year, though with a deeper drawdown along the way. Anyone reading this as a market-beating system should keep looking. What the run shows is that a sensible, disciplined value screen made good absolute returns and trailed a plain index fund, narrowly, over this particular decade.
This is our reading of the Magic Formula, not Greenblatt's exact recipe. As described above, the classic method ranks the universe on earnings yield and return on capital and buys the top scorers, where we set thresholds on EV/EBITDA and return on assets and hold the twenty largest that qualify. A stricter rank-based build would pick a different set of names and could post a different number, higher or lower.
One window, one configuration. This is a single run of nearly ten years across an era that was hard on value and kind to megacap growth, the exact stretch where the S&P was hardest to beat. Different dates, market-cap bands, or thresholds will produce different results, and the size of any survivorship gap shifts with them.
No delistings is not the same as no risk. The absence of casualties over this window says the screen fished where companies rarely die, not that it cannot hold one that does. A different decade, or a lower market-cap floor, would put failures and forced exits back into the book, and the frozen-price booking below would then matter.
Frozen-price booking is conservative but imperfect. When a company delists, the run books the position out at its last traded price. For acquisitions that lands near the deal price; for a distressed or forced exit the real proceeds to a retail holder could be worse than the frozen mark. The modeled 0.10% per trade also covers commissions and typical slippage at large-cap liquidity, and real execution in a stressed market runs worse than any flat assumption.