9/ 10

Joel Greenblatt

Founder of Gotham Capital; Columbia Business School professor

Trusted — Strong track record
Reviewed July 1, 2026Also known as: Gotham Capital, Gotham Asset Management
Public record: No adverse public records found

The record

Joel Greenblatt founded the hedge fund Gotham Capital in 1985 with $7 million and produced a roughly 40-50% annualized return through 1994, when he returned outside capital — one of the strongest documented runs of its era. He is a longtime Columbia Business School professor and today co-manages Gotham Asset Management (~$32 billion).

The philosophy — made deliberately teachable

What earns Greenblatt a place in the Trusted tier is how openly he shares the method:

  • His book The Little Book That Beats the Market lays out the “Magic Formula” — a simple, rules-based system that ranks stocks by being both cheap (high earnings yield) and good (high return on capital), then holds a basket for a year.
  • His earlier You Can Be a Stock Market Genius detailed special-situation investing.
  • He built a free public website so individual investors can use the formula themselves.

This is the opposite of the black boxes and “proprietary algorithms” in this site’s Avoid tier: a documented, testable, published approach.

Why we rate this 9 / 10 — Trusted

  • A verifiable, exceptional track record at a regulated firm.
  • Radical transparency — the strategy is in print and online for anyone to scrutinize or copy.
  • A clean record and decades of teaching individual investors.

Fair cautions

  • The Magic Formula is volatile. It’s concentrated and can badly underperform for years before working — it demands patience most people lack, and real-world results since 2005 have been mixed.
  • Gotham’s headline early returns came from a small, nimble fund; don’t expect 40% a year. No honest investor promises that — and Greenblatt doesn’t.

Bottom line

A brilliant investor who published his playbook instead of hiding it. A transparent, rules-based, testable method you can study — the polar opposite of a secret “can’t-lose” system, which is always a lie.

Screen stocks like Greenblatt: AI prompt

The Magic Formula is Greenblatt’s own published, mechanical ranking system. Paste this into any AI assistant with a company or list of companies to rank:

Act as a systematic value investor applying Joel Greenblatt's published "Magic Formula"
(The Little Book That Beats the Market). For the company or companies I name, calculate
and rank on exactly two dimensions:

1. Earnings yield — a measure of cheapness. Estimate EBIT divided by enterprise value
   (operating earnings relative to the full cost of buying the company, including debt).
   Higher is cheaper.
2. Return on invested capital — a measure of quality. Estimate EBIT divided by (net
   working capital + net fixed assets). Higher means the business generates more profit
   per dollar it needs to operate.

Then:
3. Combine both rankings (Greenblatt's method: rank every company on each factor, add
   the two rank numbers, lower combined score is better) to identify "cheap AND good"
   companies rather than just cheap or just good alone.
4. Flag anything that ranks well only because of an accounting anomaly, one-time gain,
   or unsustainable margin — the formula works on normalized, recurring earnings power,
   not accounting noise.
5. Remind me of the real-world caveat: Greenblatt's own results say this works best held
   as a diversified basket over a full year and rebalanced, not as a single stock pick,
   and it can underperform for extended periods before working.

Give me the resulting rank and the specific inputs used for the calculation, so I can
verify the numbers myself. This is a mechanical screening tool, not financial advice.

Sources

Editorial opinion — verify before you act.This review is independent editorial opinion based on public information and is not financial or legal advice. Ratings can change as new facts emerge. If you are the subject of this review and believe something is inaccurate, see ourcorrections & removals policy.

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