Systematic & Quantitative Valuation
What Works on Wall Street
O'Shaughnessy tests stock-picking rules on decades of US market data and finds that no single value ratio is the answer: scoring stocks on several ratios at once and averaging the scores sorts cheap from expensive more reliably, and recent momentum helps inside the cheapest group.
The big picture
James P. O'Shaughnessy, who ran systematic equity portfolios at Bear Stearns before building O'Shaughnessy Asset Management, wrote the book as a large back-test. Instead of arguing from stories or famous investors, he takes simple rules (buy the lowest price-to-sales stocks, buy the highest dividend yielders, buy last year's winners) and checks how each one did over long stretches of US market history, in a broad All Stocks universe and in a Large Stocks universe. The first edition came out with McGraw-Hill in 1996; the fourth edition of 2011 is the current one, with new data reaching back to 1926 for some tests and the value-composite method at its centre.
The results are lopsided in a useful way. The most expensive stocks on almost any ratio lagged badly, often by more than the cheapest stocks beat the market. Single ratios worked, but each had long bad patches of its own. Averaging several ratios into one composite score gave a steadier sort, and adding recent price momentum inside the cheapest group helped avoid stocks that were cheap because they were still falling. Why it matters now: when a handful of large growth names carry the index, a reader can use a multi-ratio composite to see how far the market has stretched away from what the accounts support, without betting on any one ratio.
The 3 strategic pillars
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Test the rule, not the story
A stock-picking idea only earns trust after it has been run mechanically over many market cycles. The book treats every popular rule the same way: define it precisely, apply it every year to a whole universe of stocks, and look at the full record, including the worst stretches.
Each test sorts the universe by one factor into ten equal groups (deciles), holds each group for a year, rebalances and repeats. What matters is the gap between the top and bottom decile, how often a strategy beat the universe over rolling five- and ten-year windows, and its worst decline. A factor that works only in one decade or only for tiny stocks is set aside.
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Several value ratios beat one
Price-to-earnings, price-to-sales, price-to-book, EBITDA to enterprise value and price-to-cash-flow each capture part of what cheap means, and each fails in its own way. Averaging them removes much of that noise, which is why the fourth edition builds its value work around composites.
Every stock gets a percentile score from 1 to 100 on each ratio, the scores are averaged, and the average is ranked again into deciles. One version of the composite adds shareholder yield, the dividend yield plus the buyback yield, so that cash returned to owners counts as cheapness too. The pack flips every ratio into a yield so that higher always means cheaper and gives a blank ratio a neutral 50, both its own conventions.
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Momentum inside the cheap group
A cheap stock can stay cheap or keep falling. The book's answer is to combine value with trend: first take the cheapest tenth of the market on the composite, then prefer the names whose prices have been rising over recent months.
The fourth edition's Trending Value approach takes the top decile of the composite that includes shareholder yield and orders it by six-month price change, holding the 25 or 50 best. The pack shows the same ordering as a research view, and separately uses a small momentum score only as a tie-breaker in the overall rank, so that value stays the main sort.
What a Closelooknet reader does with it
The working use is a ranking sheet: a reader types the fundamentals of their own list of companies, and every name gets six ratio scores, one composite, a rank and a decile. The main mistake it prevents is leaning on a single ratio, such as a low price-to-earnings that comes from a one-off gain, or a low price-to-book at a company whose assets are worth less than the books say. A second mistake is ignoring the expensive end: the book's clearest finding is how poorly the richest decile has done over long periods, so the bottom of the ranking deserves as much attention as the top. The decile summary also keeps expectations honest, because a value sort can lag for years before it pays.
The bridge to the Closelooknet approach
Two neighbours on the shelf make the same bet with fewer or different parts. The Little Book That Still Beats the Market ranks on just two columns, earnings yield and return on capital, and so mixes one cheapness ratio with one quality measure; O'Shaughnessy uses no quality measure in the composite and instead spreads cheapness over five or six ratios. Quantitative Value goes the other way: it tests the same ratios, keeps only EBIT to enterprise value, and puts forensic screens and a quality rank around it. Expected Returns shows value and momentum as two of the few style premia found across all markets, the wider frame for this book's composite plus trend. On Closelooknet, the Company Scoring System ranks a fixed universe cross-sectionally with valuation as one separate module, and the Valuation Gap framework covers the cheapness side. The Factor Regime page compares a momentum tilt with a low-volatility tilt, a daily read on whether trend is being rewarded, and the Company Screener ranks the coverage universe on separate score layers, a list a reader can check against a value composite. The earnings yield, EBITDA multiple, FCF yield and momentum entries define the inputs.
Action-Kit — from theory to practice
Tooling & data
| What you need | Where to get it | Cost |
|---|---|---|
| Company filings Net income, sales, book equity, operating cash flow, dividends paid and share repurchases and issuance for the ratio inputs | SEC EDGAR (US) and company investor-relations pages Net buybacks come from the cash-flow statement: repurchases minus proceeds from share issuance. | Free |
| Stock screener with valuation columns Export P/E, P/S, P/B, EV/EBITDA, price to cash flow and six-month performance for a list of companies in one pass | Finviz Screener ratios use the vendor's own definitions; convert them to yields (1 / ratio) before ranking and check a few against filings. | Freemium |
| Published value and momentum factor returns Long histories of value-minus-growth and momentum portfolios to see how long value sorts have lagged in the past | Kenneth French Data Library Academic factor portfolios, not the book's composites; useful for the size of past losing streaks. | Free |
The formulas
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Ratios as yields (higher = cheaper)
E/P = NI / MC; S/P = Sales / MC; B/P = BV / MC; EBITDA/EV; CF/P = OCF / MC; ShYld = (Div + NetBuyback) / MC- MC = market cap; NI = net income; BV = book equity
- EV = enterprise value; OCF = operating cash flow
- Div = dividends paid; NetBuyback = repurchases − issuance
Using yields instead of ratios keeps negative earnings at the bottom of the sort instead of letting them look cheap.
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Percentile score per factor
score = 1 + 99 × (# names with a lower yield) / (N − 1)- N = number of names with a value for that factor
Pack convention: a blank factor scores a neutral 50.
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Value composite
VC = Σ (w_i × score_i) / Σ w_i- score_i = the six factor scores
- w_i = weights (equal by default; shareholder yield at 0 gives a five-ratio composite)
Rank on VC, then decile = min(10, 1 + int(10 × (rank − 1) / N)); decile 1 = cheapest.
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Momentum tie-breaker and top-decile order
key = VC + t × score(6m return); top-decile order = rank of 6m return within decile 1- t = tie-break weight (pack default 0.01)
- 6m return = price change over the last six months
The top-decile order mirrors the book's Trending Value idea; in the pack it is a research view, not a list to act on.
Applied Pack · free members
What Works on Wall Street Value Composite Pack
Your own fundamentals in, six value-ratio scores, a composite, a rank and a decile out, with a small momentum tie-breaker and the top-decile momentum order. Software for your own research, never signals.
- WhatWorks_ValueComposite.xlsx — READ ME, Settings (minimum market cap, six amber factor weights, tie-break weight, list size), Universe (60 input rows → six yields, percentile scores, composite, rank, decile, top-decile momentum order) and Decile Summary (names, average composite, 6-month return and each yield per decile); live formulas, amber input cells
- oshaughnessy_value_composite.py — stdlib-only: reads a CSV of your own fundamentals and prints the ranked table, the decile summary and the top-decile momentum order, same math as the workbook
- value_composite_sample.csv — EXAMPLE_ rows made up for the format only
- README.txt — which parts are the book's and which are the pack's stand-ins, definitions, formulas, how to run, limits and the educational-use disclaimer
Pack security
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Educational templates — a research diary companion, not investment advice.
Closelooknet publishes a market diary, not investment advice. This condensed read restates the book's ideas in our own words for education — for the author's full argument, go to the source.