Fundamental Valuation

In his May 2010 book Buying at the Point of Maximum Pessimism: Six Value Investing Trends from China to Oil to Agriculture, author Scott Phillips “introduces a half dozen investment themes that should maintain their fundamental appeal over the next five to ten years. The purpose of this book is to answer the question of what to buy during future bouts of market volatility. In sum, these themes could be thought of as CliffsNotes to be used in preparation for future tests in the stock market. These themes should help investors, at a minimum, inventory a list of investment ideas that may be applied over the years to come.” Some notable ideas from the book are: Keep Reading

Aggregate operating earnings as an indicator of future cash flows and the inflation rate as a fundamental wealth discount rate suggest a set of equity market valuation metrics, such as:

• Lagged earnings yield [Lagged E/P], where E is 12-month lagged aggregate operating earnings and P is the level of a corresponding index.
• Forward E/P, using a forecast of aggregate operating earnings for the next 12 months rather than lagged earnings. This metric underlies  the Reversion-to-Value Model.
• Lagged E/P minus the lagged inflation rate [Lagged (E/P – I)], where I is the lagged 12-month inflation rate.
• Forward (E/P – I), using forecasts for both aggregate operating earnings and the inflation rate. This metric underlies the Real Earnings Yield (REY) Model.
• Forward E/P – Lagged I, because earnings forecasts arguably get much more attention than inflation rate forecasts. This metric underlies an alternate version of the REY Model.

How much have these metrics varied for the U.S. stock market, and where do they stand now? Using monthly estimates of actual and forecasted aggregate S&P 500 operating earnings, actual and forecasted inflation rates and monthly closes of the S&P 500 Index since March 1989, we find that: Keep Reading

Should investors go with or against asset pricing bubbles? Or, should they step aside and await a “Return to Normalcy?” In their December 2009 paper entitled “Riding Bubbles”, Nadja Guenster, Erik Kole and Ben Jacobsen investigate empirically the best approach for investors to take regarding active asset bubbles. They detect bubbles within rolling historical 10-year intervals based on two criteria: (1) price advances faster than growth rates of fundamental value based on three widely used asset pricing models; and, (2) sudden accelerations in price unexplained by these models. More descriptively, a bubble is a structural break followed by abnormally positive returns, and ended with a crash. Using monthly returns and contemporaneous fundamentals for 48 value-weighted industry indexes spanning July 1926 to December 2006, they conclude that: Keep Reading

Is level of profitability alone, and in combination with other firm/stock characteristics, a useful indicator of future stock returns? In his April 2010 paper entitled “The Other Side of Value: Good Growth and the Gross Profitability Premium”, Robert Novy-Marx investigates the power of the gross profits-to-assets ratio to predict returns for individual stocks as a standalone indicator and in combination with the book-to-market ratio. Using annual firm characteristics and stock price data for a broad sample of U.S. companies spanning 1962-2009, he concludes that: Keep Reading

Does designated creditworthiness, closely related to riskiness, drive the performance of many widely acknowledged stock return anomalies? In the April 2010 revision of their paper entitled “Anomalies and Financial Distress”, Doron Avramov, Tarun Chordia, Gergana Jostova and Alexander Philipov use portfolio sorts and regressions to investigate the relationship between financial distress (low credit ratings and downgrades) and profitability for trading strategies based on: stock price momentum, earnings momentum, credit risk, analyst earnings forecast dispersion, idiosyncratic volatility, asset growth, capital investments, accruals and value. Using data for broad samples of U.S. stocks (limited by extensive information requirements) spanning October 1985 through December 2008, they conclude that: Keep Reading

Can investors enhance momentum returns for individual stocks with combination strategies that incorporate other technical and accounting indicators? In the April 2010 draft of their paper entitled “Technical, Fundamental, and Combined Information for Separating Winners from Losers”, Cheng-Few Lee and Wei-Kang Shih investigate combined momentum strategies based on past stock returns, past trading volume and sets of fundamental (accounting) indicators. They consider two distinct sets of fundamentals: Piotroski’s FSCORE for value stocks and Mohanram’s GSCORE for growth stocks. Their combined strategy is long (short) past winners (losers) with weak (strong) past relationship between returns and trading volume and high (low) fundamental scores. Using stock return/volume and firm fundamentals data for a broad sample of NYSE and AMEX non-financial stocks spanning 1982-2007 (26 years), they find that: Keep Reading

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 (GSCORE) 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: Keep Reading

In his 2010 book entitled The Little Book of Behavioral Investing: How Not to Be Your Own Worst Enemy, author James Montier states: “I…highlight some of the most destructive behavioral biases and common mental mistakes that I’ve seen professional investors make. I’ll teach you how to recognize these mental pitfalls while exploring the underlying psychology behind the mistake. Then I show you what you can do to try to protect your portfolio from their damaging influence on your returns.” Biases he surveys include: action bias, bias for stories, confirmation bias, conformity bias (herding or groupthink), conservatism (including sunk cost fallacy), disposition effect, empathy gap, endowment effect, hindsight bias, illusion of control, inattentional blindness, information overload, loss aversion, myopia, overconfidence, overoptimism, placebo effect, self-attribution bias and self-serving bias). Value investing provides the context for discussion. Citing a number of studies, he concludes that: Keep Reading

Are there ways to concentrate the predictive power of accruals for future individual stock and equity market returns? Two recent papers explore potential refinements. In the January 2010 draft of their paper entitled “Predicting Stock Market Returns with Aggregate Discretionary Accruals”, Qiang Kang, Qiao Liu and Rong Qi focus on whether aggregate discretionary accruals (distinguished from normal accruals) are a better predictor of stock market returns than aggregate total accruals. In their February 2010 paper entitled “Percent Accruals”, Nader Hafzalla, Russell Lundholm and Matt Van Winkle investigate scaling firm-level accruals by earnings rather than total assets to predict returns for individual stocks. These studies conclude that: Keep Reading

Since 7/9/09, Christophe Faugère has been publishing (almost) daily “Market Estimates” of the value of the S&P 500 Index based on Required Yield Theory (RYT). RYT views investors as: (1) requiring that U.S. stocks and bonds in aggregate prospectively provide a real after-tax yield directly related to real long-term GDP per capita growth; and then, (2) deciding between stocks and bonds based on the better after-tax real return. To test the predictive power of these market estimates, we focus on daily percentage mismatches between estimated and actual values of the S&P 500 Index [(Estimated-Actual)/Actual] and exploitability of these mismatches via S&P Depository Receipts (SPY). Using daily RYT market estimates and daily levels of the S&P 500 Index and SPY over the period 7/9/09 through 3/3/10 (160 daily model outputs), we find that: Keep Reading