Fundamental Valuation

James O’Shaughnessy (Chairman and CEO of O’Shaughnessy Asset Management) introduces his 2011 book, What Works on Wall Street (Fourth Edition): the Classic Guide to the Best-Performing Investment Strategies of All Time, by stating: “…investors seem programmed by nature to fail at investing, forever chasing the asset class that has turned in the best performance recently and heavily discounting anything that occurred more than three to five years ago. The whole purpose of What Works on Wall Street is to dissuade investors from that course of action. Only the fullness of time shows which investment strategies are the best long-term performers, and this is doubly true after the last decade’s sorry performance. …We will make the case that equities–particularly those selected using the best long-term strategies–will go on to be the best performing assets over the next 10 and 20 years. …The fourth edition of What Works on Wall Street continues to offer readers access to long-term studies of Wall Street’s most effective investment strategies.” He uses overlapping portfolios formed monthly and rebalanced annually for all tests. Using broad sets of data on U.S. firms/stocks from either 1963 or 1926 through 2009 to extend and expand his prior quantitative analyses, he concludes that: Keep Reading

Do investors focus on dividends, thereby elevating associated stock prices as ex-dividend date approaches? In the September 2011 draft of their paper entitled “The Dividend Month Premium”, Samuel Hartzmark and David Solomon examine the price behavior of stocks with scheduled quarterly, semiannual and annual dividends during the expected dividend month and around expected ex-dividend dates. Using daily and monthly price and cash dividend data for a broad sample of U.S. stocks during January 1927 through December 2009, along with widely used risk adjustment factors, they find that: Keep Reading

Do oil price variations reliably affect returns for U.S. industry-level stock portfolios? In the June 2011 draft of their paper entitled “U.S. Industry-Level Returns and Oil Prices”, Qinbin Fan and Mohammad Jahan-Parvar apply several tests to investigate how oil price changes impact stock returns for 49 U.S. industries. They test economic significance by: (1) using a 60-month rolling historical window to model the predictive relationship between spot oil price changes and industry returns; (2) applying this relationship each month to the last observed oil price change to predict future industry returns; and, (3) investing in either industry portfolios or 4-week Treasury bills according to which has the higher expected return. They assume an industry portfolio-Treasury bill switching friction of 0.10%. Using monthly and weekly prices for West Texas Intermediate crude oil spot (January 1979 through January 2009) and nearest contract Cushing, Oklahoma light sweet crude oil futures (February 1986 through January), along with contemporaneous U.S. industry returns, they find that: Keep Reading

Does the semantic tone of an earnings announcement, as measured independently of the level of earnings surprise, affect stock price reaction. In his September 2011 paper entitled “Short-term Reactions to News Announcements”, Michal Dzielinski investigates the effect of the tone (positive, neutral or negative) of the words in earnings announcements and other company news on stock prices from two days before to ten days after release. He averages news tone for each stock by day, with news released before (after) the market close counting as current-day (next-day) news. Using daily return data and over six million automatic, real-time Thomson Reuters news sentiment (tone) measurements (including those for over 68,000 earnings announcements) for 4,750 U.S. stocks during 2003 through 2010, he finds that: Keep Reading

Can investors apply forensic accounting principles (searching for inconsistencies, irregularities and other signs of trouble) to help forecast stock returns? In their July 2011 paper entitled “To Catch a Thief: Can Forensic Accounting Help Predict Stock Returns?”, Messod Beneish, Charles Lee and Craig Nichols investigate the ability of an earnings manipulation model to predict stock returns and detect fraud. The model, which relies exclusively on financial statement data, consists of eight ratios indicative of either financial statement distortions associated with earnings manipulation or a predisposition to engage in earnings manipulation. Specifically, four of the ratios detect unusual buildup in receivables, unusual expense capitalization and dependence of profits on accruals. The other four ratios detect deteriorating gross margins and increasing administration costs, high sales growth and increasing reliance on debt financing. The model calibration period is 1982-1988, and its initial test period is 1989-1992. Using the exact model published in the Financial Analyst Journal in 1999 as applied to accounting data and stock returns for a broad sample of NYSE, AMEX, and NASDAQ over the period 1993 through 2007 (33,848 firm-year observations, excluding financial services firms and small firms), they find that: Keep Reading

A reader commented and asked: “One of the most read investing books in the U.S. is Joel Greenblatt’s The Little Book that Beats the Market, which reveals the ‘magic formula’. What do you think of it?” In response, rather than review the book, we examine the U.S. Value Direct Composite returns at Formula Investing. These returns summarize the composite performance of portfolios of professionally managed (minimum $100,000) accounts each holding approximately 24 stocks with the highest rank per the magic formula, reformed quarterly by replacing the six worst performers with the highest ranking stocks not in the portfolio. Portfolio weights are apparently about equal, subject to rotation rule constraints. According to Formula Investing:

“The S&P CompuStat database is used for the screening of U.S. listed stocks, with the exception of financials and utilities. The stocks are screened for our best ranked companies based on return on capital and earnings yield.”

“Returns are presented net of investment advisory fees and include the reinvestment of all income. For the period May 1, 2009 – December 31, 2010, the composite includes accounts that received a temporary waiver of the advisory fee. …Net returns may be reduced by additional fees (outside of investment advisory fees) and transaction costs that may be incurred in the management of the account.”

Using monthly U.S. Value Direct Composite returns as presented and contemporaneous monthly returns of the dividend-adjusted Rydex S&P 500 Equal Weight (RSP) and dividend-adjusted iShares Russell 2000 Index (IWM) as benchmarks for May 2009 through June 2011 (27 months), we find that: Keep Reading

Do stocks around the world tend to perform better around the time of annual earnings announcements by respective firms than during the rest of the year? In the June 2011 draft of their paper entitled “The Earnings Announcement Premium Around the Globe”, Brad Barber, Emmanuel De George, Reuven Lehavy and Brett Trueman investigate whether the earnings announcement premium (elevated returns during earnings announcement months) is a global phenomenon or is isolated to U.S. stocks. They employ a hedge portfolio, reformed monthly, that is long (short) stocks of firms expected (not expected) to announce annual earnings during the next month, The long and short sides are equal-weighted, and the stocks within each side are value-weighted. Using roughly 200,000 annual earnings announcements for about 28,000 firms in 46 countries during 1990 through 2009 to estimate announcement months during 1991-2010, and associated monthly stock returns, they find that: Keep Reading

Are some firms more at risk of creative destruction by new technologies? If so, does the market offer a premium to investors in such firms? In his March 2011 paper entitled “Creative Destruction and Asset Prices”, Joachim Grammig explores the concept of creative destruction as an explanation for the size effect and the value premium under the proposition that associated firms have a higher probability of being destroyed by technological change. He defines the pace of technological change as the annual percentage change in U.S. patents issued (patent activity growth). Using annual counts of newly issued patent from the U.S. Patent and Trademark Office and annual data on 25 portfolios of U.S. stocks formed by double-sorts on size and book-to-market ratio over the period 1927 through 2008, he finds that: Keep Reading

Are value stocks priced low because the companies are in financial distress? In their May 2011 paper entitled “Is the Value Premium Really a Compensation for Distress Risk?”, Wilma de Groot and Joop Huij investigate the relationships between the value premium and alternative measures of firm distress risk. Their core methodology employs monthly double-sorts on firm book-to-market ratio and each of four measures of firm financial risk: (1) financial leverage (debt-to-assets ratio); (2) a structural model of distance-to-default; (3) credit spread (between firm bonds and maturity-matched Treasuries); and, (4) credit rating. Using data to calculate these measures for the 1,500 largest U.S. firms, along with associated monthly stock prices, over the period September 1991 (limited by availability of credit spread data) through December 2009, they find that: Keep Reading

Can investors systematically benefit from the perspective that trading is the exchange of one asset for another, not the buying and selling of a single asset? In his paper entitled “Optimal Rotational Strategies Using Combined Technical and Fundamental Analysis”, third-place winner for the 2011 Wagner Award presented by the National Association of Active Investment Managers, Tony Cooper presents methods and tools designed to exploit the precept that valuations are relative. An organizing concept for these methods and tools is the Binary Decision Chart (BDC), which in one form addresses simultaneous analysis of two competing investments for the purpose of switching or weighting and in an extended form addresses combining technical analysis (based on observed price action) and fundamental analysis (indicator-based prediction). BDCs are cumulative return charts, but the horizontal axis may be a technical or fundamental indicator rather than time. More specifically, using various asset price series and indicators, he illustrates the following methods/tools: Keep Reading