In the early 1980s, Joel Greenblatt authored a paper on buying stocks selling below liquidation value and ran some backtests with impressive results.
He named it 'How the Small Investor Can Beat the Market'.
Here are the key takeaways 🧵
1/ Greenblatt did not believe in the efficient market hypothesis, a common belief at the time, and felt that this shared belief limited investors.
"Since this theory concludes that bargain purchases are impossible, academics argue attempts to outperform the market are futile".
2/ While the numbers may differ today, institutions still neglect certain stocks; because many of them are too small or illiquid.
For this reason, Greenblatt believed there will always be unrecognised value in the market.
3/ His paper sought to combine Benjamin Graham's approximation of liquidation value (below) with the PE ratio. This process was intended only to be a rough screening device to sort out likely prospects from the thousands available.
He excluded firms with TTM losses.
4/ He constructed four unique portfolios and compared their performance to that of the OTC and Value Line indexes across 18 four-month periods from 1972 to 1978.
Variables included price/liquidation value and PE.
5/ An initial $100 investment across the OTC and Value Line indexes would return $88 and $79, respectively.
After six years, portfolios 1 through 4 returned a range between $248 and $517.
6/ The portfolio with the lowest value with respect to PE and LV was found to be the highest-performing vehicle.
Portfolios with floating PEs beat the indices but underperformed those with fixed ratios.
7/ We can assume that the PE value was considerably higher for portfolios 1 and 2, because Triple-A yields ranged from 7% to 9% during the period.
8/ Portfolio 1 had, collectively, the highest values for LV (≤ 1.0) and PE (5.5 to 7) and performed the worst. The study suggests that searching for profitable stocks that have an LV ≤ 0.85 and PE ≤ 5 is the most attractive fishing spot for stock pickers.
9/ Limitations to the study included;
• Reconciling returns
• Dividends & fees
• Sample period
• Sample size
• Market cap threshold
• Portfolio size
E.g, the avg # of stocks in the sample portfolios was ~15. Concentrated by some people’s standards.
10/ Why did it work?
The criterion produced results based on the “large 2nd tier of stocks” that are forgotten and inefficiently valued by the market.
The study sought to exploit stocks that were undervalued and protected by large asset values and strong balance sheets.
11/ While Greenblatt's paper is unlikely to work as well today, the fact that a retail investor can exploit underexamined pockets of the market remains true, decades later.
Get screening!
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In 1991, Seth Klarman wrote a book, Margin of Safety, that is rumoured to have printed only a few thousand copies.
No longer in print, but packed with superb insights, Klarman once said he
“endeavoured to make the book timeless".
🧵 Our 9 favourite lessons:
1. The 80/20 rule:
"The first 80 percent of the available information is gathered in the first 20 percent of the time spent. The value of in-depth fundamental analysis is subject to diminishing marginal returns".
2. The down market test:
"A market downturn is the true test of an investment philosophy. Securities that have performed well in a strong market are usually those for which investors have had the highest expectations".
In several of Peter Lynch's old books, he shared a charting technique later dubbed the "Peter Lynch Chart".
"A quick way to tell if a stock is overpriced is to compare the price line to the earnings line".
A quick guide to producing these charts in Koyfin 👇
1) These charts are sometimes called 'Fair Value' chart lines, where you plot a range of constant valuation multiples to visualise where the company trades in relation to those bands.
Example: Apple trades at 27x earnings with a 10Y mean of 21.3x earnings.
2) To reproduce this, we use the multiplier transformation in Koyfin, allowing you to multiply or divide the underlying data of a multiple.
Open up the historical graph, add a ticker and decide which multiple you wish to plot (here, we use price/sales).
Mauboussin & Callahan just shared a paper on the psychology of expected value.
"How often you are right is not all that matters. What is vital is how much money you make when you are right versus how much you lose when you are wrong".
🧵 Our 8 favourite highlights:
1/ Most investors misprice extreme outcomes - markets often overpay for "lottery" stocks (low probability, high payoff) and underprice extreme downside risks.
"Many outcomes investors call black swans are really gray swans—known unknowns".
2/ Volatility kills compounding. Your long-term returns aren’t the average of your annual returns. Volatility drag means big drawdowns hurt wealth more than most realize.
Example: The GraniteShares 3x Long MicroStrategy ETF lost money in 2024 despite its underlying stock rising 358%.