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Fundamental Valuation

What fundamental measures of business success best indicate the value of individual stocks and the aggregate stock market? How can investors apply these measures to estimate valuations and identify misvaluations? These blog entries address valuation based on accounting fundamentals, including the conventional value premium.

Predictive Power of Aggregate Versus Firm-specific Earnings

Do aggregate earnings (for example, for the S&P 500) predict stock market behavior? In their 2008 paper entitled “Aggregate Earnings, Firm-Level Earnings and Expected Stock Returns”, Turan Bali, Ozgur Demirtas and Hassan Tehranian analyze the predictability of stock returns using market, industry and firm-level earnings. Using monthly stock return data and quarterly fundamentals data for a broad range of U.S. stocks focused mostly on the period from mid-1972 through 2002, they conclude that: Keep Reading

A Few Notes on Full of Bull

In the 2009 edition of his book, Full of Bull, author Stephen McClellan seeks to “expose the puzzling and deceptive behavior of Wall Street that so disadvantages individual investors, tripping them up in their attempts to invest properly and rationally. It unscrambles the confounding practices of the Street in terms a layperson can comprehend. …Once armed with an insider’s understanding of all the Street’s subtleties, you can be your own investment analyst.” Stephen McClellan was a securities analyst for 32 years. The principal messages of the book are: Keep Reading

A Few Notes on Quantitative Strategies for Achieving Alpha

In his 2009 book, Quantitative Strategies for Achieving Alpha, flagged by Jeff Partlow, author Richard Tortoriello “seeks to determine empirically the major fundamental and market-based drivers of future stock market returns” by testing over 1,200 alternative investment strategies. He believes “that the quantitative approaches outlined in this book can provide a proven way to generate investment ideas for the qualitative investor as well as a discipline that can help improve investment results.” Richard Tortoriello is an equity research analyst with Standard & Poor’s. The principal elements of the book are: Keep Reading

Required Yield Theory of Asset Valuation

What aggregate return thresholds are critical to investors in deciding whether to accept or reject equity and bonds for investment portfolios? In their December 2008 paper entitled “A Required Yield Theory of Stock Market Valuation and Treasury Yield Determination”, Christophe Faugere and Julian Van Erlach argue that investors first require that U.S. stocks and bonds in aggregate prospectively provide a real after-tax earnings yield directly related to real long-term GDP per capita growth. Investors then decide between stocks and bonds based on the better after-tax real return. Applying this Required Yield Theory (RYT) to quarterly data over the period 1953-2006, they find that: Keep Reading

Predicting Crashes for Individual Stocks

Can investors tell when management is unsustainably propping up the stock price of a company? In their June 2008 paper entitled “Identifying Overvalued Equity”, Craig Nichols and M. D. Beneish devise and test a method for predicting stock price declines that integrates observable accounting, operating, investing and financing data indicative of management efforts to sustain overvaluation. Specifically, they relate future stock returns to an overvaluation score (O-Score, ranging from zero to five points) calculated by assigning one point each for the following indications: (1) likely earnings overstatement; (2) high sales growth; (3) low operating cash flow to total assets; (4) an acquisition in the last five years; and, (5) unusual amounts of equity issuance in the past two years. Using 27,427 firm-year observations over the period 1993-2004 (with financial services and very small companies excluded), they conclude that: Keep Reading

Valuation Forecasting Fly-off: Discounted Cash Flow vs. Comparables

Is the discounted cash flow (DCF) method or the comparables method more accurate in forecasting equity valuations? In their April 2009 preliminary paper entitled “Exploring the Accuracy of DCF and Comparables Valuation Methods by Using Ex-Post Market Data as Forecasts”, Friedrich Sommer, Arnt Woehrmann and Andreas Wömpener use both truly historical data and “best” forecasts of basic firm fundamentals (such as earnings) to compare the forecasting power of the DCF and comparables methods. They define “best” forecasts of input data as actual results inserted retroactively (see the chart below). They consider multiple variations of both the DCF and comparables methods. Using fundamentals and stock price data for the period 1998 through 2006 to support truly historical and retroactive valuation calculations as of 12/31/2001 for 89 U.S. public companies via DCF and over 200 U.S. public companies via comparables, they conclude that: Keep Reading

Predictive Power of the Gap Between Stock Earnings Yield and T-note Yield

Does the gap between the aggregate stock market forward-looking earnings yield and the yield on 10-year Treasury notes (T-note) predict future stock market and bond returns? In the November 2008 update to his paper entitled “The FED Model and Expected Asset Returns”, Paulo Maio examines the statistical and economic significance of the Fed model as an indicator of future stock market and bond returns. Said differently, he investigates whether mean reversion in stock and bond yields results in mean reversion of the yield gap. Using monthly data for a broad U.S. stock index and T-notes, and for contemporaneous benchmark indicators, over the period July 1954 through December 2003, he concludes that: Keep Reading

Speed/Determinants of Stock Price Reversion

Do mispriced stocks systematically revert to value under the long-term guidance of information traders? If so, what factors affect the rate of reversion for a particular stock? In their March 2009 paper entitled “How Quickly do Equity Prices Converge to Intrinsic Value?”, Dennis Capozza and Ryan Israelsen investigate the predictability of the reversion of stock price to a changing fundamental valuation baseline. They use annual fundamental (intrinsic) values for stocks from AFG Research, as derived from from estimates of future earnings based on growth rate and the decay of the spread between return on equity and cost of capital. Using monthly return/trading data, annual intrinsic value data and firm characteristics for 8,845 stocks over the period 1997 through 2006, they conclude that: Keep Reading

Performance of Fundamental-weighted Indexes in Europe

Capitalization-weighted stock indexes arguably incorporate a performance drag by overweighting overvalued stocks and underweighting undervalued stocks. In their February 2009 paper entitled “Fundamental Indexing: An Analysis of the Returns, Risks and Costs of Applying the Strategy”, Roel Houwer and Auke Plantinga examine the raw and risk-adjusted returns of hypothetical indexes of European stocks weighted by dividend, book value, revenue and operating income. They take the capitalization-weighted Stoxx 600 Index as a benchmark. Using monthly stock returns and firm fundamental data for the Stoxx 600, along with relevant risk-adjustment data, for the period 1993-2007, they conclude that: Keep Reading

De-Snooping Market Timing Rules Based on Fundamental and Sentiment Indicators

Some analysts fail to account for data snooping bias in their analyses of market timing indicators. This bias amounts to incorporating pure luck into results by testing many different rule variations or parameter settings within rules (or inhaling the “secondary smoke” of other analysts who have already screened a set of rules/parameters). This luck does not persist out-of-sample. Do any market timing rules generate outperformance after correcting for this bias? In their February 2009 paper entitled “Data Snooping and Market-Timing Rule Performance”, Andreas Neuhierl and Bernd Schlusche assess the profitability of a comprehensive set of simple and complex market timing rules based on fundamental indicators and investor sentiment indicators after correcting for data snooping bias. Simple rules derive from a single indicator, and complex rules derive from multiple indicators. Using thousands of simple and complex rules based on data for the S&P 500 to time the daily close of the S&P 500 index over the period 1980-2007, they conclude that: Keep Reading

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