Fundamental Valuation

As espoused by many market commentators, do positive (negative) earnings surprises in fact predict upward (downward) stock market movement? In their January 2010 paper entitled “Aggregate Market Reaction to Earnings Announcements”, William Cready and Umit Gurun investigate the relationship between earnings announcement surprises and market returns, focusing on the days surrounding earnings news. Using quarterly earnings announcements for a broad sample of firms spanning January 3, 1973 through June 21, 2006 (413,687 announcements) and contemporaneous values of a combined NYSE/AMEX index, they find that: Keep Reading

A reader inquired about the Zacks services. The core belief of Zacks Investment Research is: “Earnings estimate revisions are the most powerful force impacting stock prices.” This belief underlies the Zacks “quantitative model to harness the power of earnings estimate revisions – the direction, the degree of change, and surprises – along with other important variables to create the Zacks Rank.” The Zacks Rank feeds a range of information services that Zacks offers to investors. Should investors expect that portfolios built on the Zacks Rank will substantially outperform the market? Based on information available about the Zacks Rank and associated managed accounts/funds, we find that: Keep Reading

Conventional wisdom holds that a low market valuation ratio and a high market volatility both relate positively to future market return. Do valuation ratio and volatility therefore relate negatively to each other with some consistency? If not, why not? In their November 2009 paper entitled “What Ties Return Volatilities to Price Valuations and Fundamentals?”, Alexander David and Pietro Veronesi investigate the relationships between stock and bond valuations and their volatilities in the context of varying investor beliefs about future economic growth and inflation. Using S&P 500 operating earnings from Standard & Poor’s, daily closes of the S&P 500 Index, daily bond yield/return data and monthly values of the Consumer Price Index over the period 1958 through 2008 (51 years), they conclude that: Keep Reading

Should investors look for consistent predictability, or upside surprises, in quarterly earnings announcements? In his November 2009 paper entitled “Meeting Analyst Forecasts and Stock Returns”, Ioan Mirciov investigates relationships between announced earnings (relative to analyst forecasts) and long-run future stock performance. Using earnings forecasts, actual earnings, stock returns and firm characteristics data for a broad sample of U.S. stocks over the period 1993-2007, he concludes that: Keep Reading

In his 2009 book Fire Your Stock Analyst!: Analyzing Stocks On Your Own (2nd Edition), author Harry Domash “describes practical step-by-step strategies for finding, researching, and evaluating investment candidates…one for growth stocks, and the other for value investors. …The methods described make use of information readily available to anyone connected to the Internet, but in new ways…” The target audience of the book encompasses “investors willing to put in the time and effort it takes to find and research profitable stock investments.” The culminations of the book (Chapter 18) are separate value stock and growth stock “analysis scorecards.” Some notable points from the book are: Keep Reading

Do investors efficiently process the information in extreme quarterly earnings? In their November 2009 paper entitled “Post Loss/Profit Announcement Drift”, Karthik Balakrishnan, Eli Bartov and Lucile Faurel examine whether investors fully price the implications of current quarterly losses/profits for future losses/profits. They consider three alternative definitions for profit/loss, all scaled by beginning-of-quarter total assets: (1) earnings before extraordinary items and discontinued operations; (2) earnings before extraordinary items, discontinued operations and special items; and, (3) net income. Using stock return, financial and risk adjustment data for a broad sample of 15,143 distinct firms (458,693 firm-quarters) over the period 1976-2005 (120 quarters), they conclude that: Keep Reading

Is the value premium readily accessible for individual investors? Which value strategy works best? In his May 2009 article entitled “Can Individual Investors Capture The Value Premium?”, Patrick Larkin uses a ranking methodology to compare the performances of Joel Greenblatt’s magic formula and seven other one and two-factor growth at a reasonable price (GARP) and value strategies. The portfolios for the eight strategies derive from rankings on: (1) the magic formula, a combination of return on capital (ROC) and the ratio of earnings before interest and taxes to Enterprise Value ((EBIT/EV); (2) a combination of return on assets (ROA) and earnings yield (E/P); (3) a combination of return on equity (ROE) and E/P; (4) EBIT/EV alone; (5) E/P alone; (6) a combination of book-to-market ratio (B/M) and market capitalization (Size); (7) B/M alone; and, (8) Size alone. Each month, the author forms equally weighted portfolios of the 30 highest-ranking stocks for each of these eight strategies. Using monthly stock return and GARP-value metric data for a broad sample of firms with market capitalizations over $50 million during December 1998-2006 (97 months), he finds that: Keep Reading

A reader asked: “Does buying Exchange-Traded Funds (ETF) currently trading below Net Asset Value (NAV) provide a real advantage in the long run? In other words, are they ‘bound’ to reach NAV at some point? Does buying a fund which is trading at a discount to NAV and shorting a fund within the same sector that is trading at a premium to NAV make sense?” Keep Reading

A reader asked: “I was wondering if you can weigh in on Tobin’s q as a predictor of stock returns.” Keep Reading

What fundamental and technical factors are optimum for stock selection, and how well do they work? In the October 2008 draft of their paper entitled “Case Closed”, flagged by a reader, Robert Haugen and Nardin Baker present a model of future stock returns based on multiple regressions of 12 factors they find most significant in predicting monthly returns. Using monthly data for a sample of U.S. stocks over the 45-year period 1963-2007, they conclude that: Keep Reading