Classic Paper: Mohanram’s Efficient Growth Investing

We occasionally select for retrospective review an all-time “best selling” research paper of the past few years from the General Financial Markets category of the Social Science Research Network (SSRN). Here we summarize the April 2004 version of the paper entitled “Separating Winners from Losers among Low Book-to-Market Stocks using Financial Statement Analysis” (download count over 5,800) by Partha Mohanram. The study tests the ability of a stock scoring system based on eight binary signals derived from profitability and growth-specific financial measures (see the list below) to predict future returns. Using stock returns and firm fundamentals for the fifth of a broad sample of U.S. firms with the lowest book-to-market ratios over the period 1979-1999, the author concludes that:

• All eight signals exhibit a statistically significant ability to distinguish between stocks with strong and weak returns in historical data.
• A hedge strategy that is long (short) firms with growth stock scores 6, 7 or 8 (0 or 1), rebalanced annually, consistently earns significant excess returns. Specifically, average raw (size-adjusted) returns during the first and second years after portfolio formation are:
  • 8.2% and 9.0% (-6.0% and -4.2%) for the entire sample of growth stocks.
  • 17.4% and 15.1% (3.3% and 2.4%) for the stocks with the highest scores.
  • -4.0% and 0.4% (-17.9% and -13.3%) for the stocks with the lowest scores.
• The hedge strategy has positive returns in all 21 years. In 17 years, returns exceed 10%.
• Results indicate that, on a size-adjusted basis, the strategy is more effective in identifying big losers than winners.
• Results are generally robust to sorts on market capitalization, book-to-market ratio, accruals, stock price, analyst following, exchange listing, past returns (momentum) and inclusion/exclusion of Initial Public Offerings.
• Firms with high scores tend to generate positive earnings surprises, suggesting that the market fails to grasp the future implications of current fundamentals.

The author’s eight binary growth stock discriminators (mostly scored within industry) are:

• ROA – net income before extraordinary items scaled by beginning of the year total assets
• Cash Flow ROA – cash flow from operations scaled by beginning of the year total assets
• Accrual – cash flow from operations minus net income
• ROA variance over the past five years
• Sales variance over the past five years
• R&D Intensity – R&D expenditure scaled by beginning of the year total assets
• Capital Expenditure Intensity – capital expenditure scaled by beginning of the year total assets
• Advertising Intensity – advertising expenditure scaled by beginning of the year total assets

Each of the eight signals yields either a good (1) or bad (0) indication. The total score for a company in a given year therefore ranges from 0 to 8. About 11% of the 20,000+ company observations earned a total score of 6, 7, or 8; about 16% earned a score of 0 or 1.

All return calculations exclude trading frictions.

In summary, tailored fundamental analysis may be able to identify growth stock mispricing and thereby earn substantial abnormal returns.

Compare and contrast this approach with that of Joseph Piotroski for value stocks in “Piotroski’s Efficient Value Investing”.

You May Also Enjoy...

• Piotroski’s Efficient Value Investing
• Classic Paper: Company Valuation Methods
• Classic Paper: Returns from Commodity Futures
• Classic Paper: Empirical Overview of Commodity Futures
• Classic Paper: Physical Inventories and Commodity Futures Returns