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For Deep-value hunters

Cigar butts without the asbestos.

Net-net, sub-tangible-book, single-digit P/E plays - but with the triple-fraud check overlaid so you do not step on accounting landmines while reaching for the deep-value cigar.

· Reviewed by Javier Sanz, ValueMarkers Founder

The pain we solve for deep-value hunters

A stock at 0.6x book is either a steal or a fraud. The screener that finds it does not tell you which. We do - Piotroski + Altman + Beneish on every cheap stock.

Must-haves we built in

  • P/B < 1.0 AND P/E < 10 AND ROIC > 0
  • Altman Z-Score > 1.8 (not in distress)
  • Beneish M-Score < -1.78 (not manipulating earnings)
  • Piotroski F-Score >= 5 (improving fundamentals)
  • Insider buying in last 6 months (positive signal)

VM features tailored to you

  • Deep-value screener with Z-Score floor
  • Net-net (NCAV > MarketCap) screener
  • Glass-box DCF with bear-case sensitivity
  • Greenblatt Magic Formula - original + Piotroski-filtered version

How we filter deep-value hunter candidates

Deep-value investing is the highest-reward and highest-trap quadrant of fundamental analysis. ValueMarkers structures the workflow around three independent fraud-and-distress filters layered on top of cheapness. First, Altman Z-Score above 1.8 - this 5-factor bankruptcy-prediction model penalizes negative working capital, retained-earnings erosion, and excessive leverage. Names in the "distress zone" (Z < 1.8) statistically go bankrupt at 5-10x the rate of safe-zone names. Second, Beneish M-Score under -1.78 - this earnings-quality test flags managements potentially inflating revenue or capitalizing operating expenses. The cohort of deep-value stocks is disproportionately exposed to manipulation because management is often trying to hide structural decline. Third, Piotroski F-Score above 5 - the 9-point test favors deep-value names whose fundamentals are improving (margins expanding, leverage falling, share count stable). Joseph Piotroski's original 2000 paper showed that filtering low P/B stocks by F-Score above 5 doubled excess returns versus an unfiltered low-P/B screen. We replicate that filter in real time on 44,722 stocks across 73 exchanges.

Building the screen step by step

Start broad: P/B < 1.0 AND P/E < 12 AND market cap > $200M (excludes microcap manipulation risk). Layer Altman Z-Score > 1.8 to exclude likely-bankrupt names. Layer Beneish M-Score < -1.78 to exclude likely-manipulator names. Layer Piotroski F-Score >= 5 to keep only deep-value names with improving fundamentals. The resulting universe typically contains 40-120 names. Then sort by ROIC descending - the best deep-value picks are companies temporarily mispriced but with above-average historical capital efficiency. Buy a basket of 15-30 names, sized equal-weight, rebalanced quarterly. Historical academic literature suggests this construction outperforms unfiltered deep-value by 4-7% annualized.

Common mistakes deep-value hunters make

Three errors recur in deep-value investing. (1) Skipping the Altman filter and ending up with 20% of the basket in actual bankruptcy candidates - returns get destroyed by one or two zeros. (2) Concentrating into a single sector that is uniformly cheap (financials in 2009, energy in 2016, real estate in 2023) - macroeconomic correlation overwhelms the per-name edge. Always cap sector exposure at 25%. (3) Failing to size positions equal-weight - a deep-value strategy generates excess return on the median name, not on outliers, so equal-weighting prevents one bad pick from dominating the basket.

Case study: OPI

Office Properties Income (OPI) traded at 0.4x book and 6% yield in mid-2023. The screener flagged Altman Z-Score at 1.3 (deep distress), Beneish M-Score at -1.5 (manipulation flag), and Piotroski at 3. All three filters said skip - and by Q1 2024 OPI cut the dividend by 55% and the equity dropped another 60%. Deep-value cohort dodged this name entirely.

Case studies illustrate how the ValueMarkers screen flagged this name historically; they are research examples, not investment recommendations. See our full disclaimer.

Frequently Asked Questions

Why filter deep-value stocks by Piotroski F-Score?+
Joseph Piotroski's 2000 paper showed that the bottom quintile of stocks by P/B ratio - the deep-value cohort - contains many value traps (companies cheap because they are deteriorating) and only a small subset of genuinely undervalued names. Filtering the cohort for Piotroski F-Score above 5 doubled the cohort's excess return in his sample. The filter favors deep-value names whose fundamentals are improving (margins expanding, leverage falling) over those whose fundamentals are still deteriorating.
How do net-net stocks (NCAV strategy) fit into deep-value investing?+
Net-Net Current Asset Value (NCAV) is Benjamin Graham's most extreme deep-value test: market cap less than (current assets minus total liabilities). Companies passing the NCAV screen are theoretically trading below liquidation value. The strategy works historically but produces very few qualifying names in modern markets (typically 5-25 globally at any time, mostly microcap). ValueMarkers offers a net-net screener; we recommend layering Altman + Beneish + Piotroski filters on top because the cohort skews heavily toward distressed and manipulated names.
What is the Altman Z-Score and how should deep-value investors use it?+
The Altman Z-Score is a 5-factor model predicting bankruptcy probability over the next 1-2 years. A Z-Score above 2.99 is the safe zone, 1.81-2.99 is the gray zone, and below 1.81 is the distress zone (high bankruptcy probability). For deep-value investors, the Z-Score is the single most important filter: deep-value baskets that include distressed-zone names get destroyed by 1-3 bankruptcies, while baskets restricted to safe-zone names produce excess returns more reliably.
How do I size deep-value positions to manage risk?+
Deep-value strategies work statistically on the median name, not on outliers. Equal-weighting positions (rather than weighting by conviction or by market cap) prevents one bad pick from dominating the basket. Most academic research supports basket sizes of 15-30 names with rebalancing every 3-6 months. Concentration into 3-5 high-conviction names is much riskier; the cohort's excess return comes from the law of large numbers, not stock-picking skill.

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