Fundamental Valuation

Does the earnings yield (inverse of price-to-earnings ratio, or E/P) usefully predict returns for individual stocks? In their April 2011 paper entitled “Reexamination of the Earnings-Price Anomaly by the Buy-Sell Strategy”, Hsin-Yi Yu and Li-Wen Chen test a long-only strategy that forms monthly value-weighted portfolios based on time-series sorting rather than cross-sectional sorting. Time-series sorting ranks stocks according to current E/P of each relative to its range over the prior decade. The strategy tested buy stocks near the top of their respective ten-year ranges and subsequently sells them when they move to the bottom. Intuitively, stocks near the top (bottom) of their respective historic E/P ranges are likely to be undervalued (overvalued). For reference, they also test a strategy that forms portfolios based on cross-sectional sorting by current E/P and held for a fixed interval, while noting that such sorts make little sense because average E/P varies considerably by industry. Using earnings and price data for all common stocks listed on NYSE, AMEX and NASDAQ from January 1962 to December 2010, they find that: Keep Reading

Is the accruals anomaly still on solid ground? In their paper entitled “The Accrual Anomaly”, Patricia Dechow, Natalya Khimich, and Richard Sloan review the origin of and subsequent research on the accruals anomaly. They characterize accruals as “the piece of earnings that is ‘made up’ by accountants” as opposed to the balance coming from cash flow. Using the original analysis and updated firm accounting and stock return data for the period 1970 through 2007, they find that: Keep Reading

Do the stocks of firms that get the most bang per research buck tend to outperform? In the March 2011 update of their paper entitled “Innovative Efficiency and Stock Returns”, David Hirshleifer, Po-Hsuan Hsu and Dongmei Li investigate the relationship between innovative efficiency (IE) and future stock returns. They consider three alternative definitions of IE:  (1) patents granted per dollar of R&D capital investment two years previous; (2) patents granted per dollar of R&D expenditures two years previous; and, (3) adjusted patent citations (a measure of patent quality) per dollar of cumulative R&D expense over the five years ending two years previous. The two-year lag between patent activity and investment in R&D reflects the average patent application-grant delay. Return predictability tests involve annually reformed value-weighted stock portfolios comprised of six intersections derived from independent sorts at the end of each February into: small or big market capitalization; and, low, middle or high IE. Using accounting and patent data for a broad sample of U.S. firms over the period 1981 through 2006, and associated stock returns and risk adjustment factors through February 2008, they find that: Keep Reading

Does aggregate investor sentiment affect the strength of well-known U.S. stock return anomalies? In their January 2011 paper entitled “The Short of It: Investor Sentiment and Anomalies”, Robert Stambaugh, Jianfeng Yu and Yu Yuan explore the interaction of aggregate investor sentiment with 11 cross-sectional stock return anomalies. Their approach reflects expectations that: (1) overpricing of stocks is more common than underpricing due to short-sale constraints; and, (2) a high sentiment level amplifies overpricing. Specifically, they consider the effect of investor sentiment on hedge portfolios that are long (short) the highest(lowest)-performing) value-weighted deciles of stocks sorted on: financial distress (two measures), net stock issuance, composite equity issuance, total accruals, net operating assets, momentum, gross profit-to-assets, asset growth, return-on-assets and investment-to-assets. They use a long-run sentiment index derived from principal component analysis of six sentiment measures: trading volume as measured by NYSE turnover; the dividend premium; the closed-end fund discount; the number of and first-day returns on Initial Public Offerings; and, the equity share in new issues. They measure anomaly alphas relative to the three-factor model (adjusting for market, size, book-to-market). Using monthly sentiment and stock return anomaly data as available over the period July 1965 through January 2008, they find that: Keep Reading

Do technical indicators add value to fundamental indicators in assessing broad stock market valuation? In their March 2011 paper entitled “Forecasting the Equity Risk Premium: The Role of Technical Indicators”, Christopher Neely, David Rapach, Jun Tu and Guofu Zhou examine the powers of technical and fundamental indicators to predict stock market returns. They consider 12 variations of three stock market index technical indicators: (1) relative values of two moving averages (1 month versus 3, 6, 9 and 12 months); (2) return momentum (past 3, 6, 9 and 12 months); and, (3) relative values of two on-balance volume moving averages (1 month versus 3, 6, 9 and 12 months). They consider 14 fundamental indicators ranging from stock market valuation ratios to Treasury yields, yield spreads and the default spread. They compare mean squared equity risk premium forecast errors for technical and fundamental indicators to that for the historical average premium. They also compare the average utility gain for a mean-variance investor who allocates monthly between stocks and Treasury bills based on either technical or fundamental market forecasts to that for an investor who uses the historical average premium. Finally, they generate equity risk premium forecasts based on a rolling principal component analysis that encapsulates the predictive powers of the 26 technical and fundamental indicators into three or four variables. Using monthly price and volume data for the dividend-adjusted S&P 500 Index and monthly readings of the 14 U.S. fundamental indicators as available over the period 1927 through 2008 (1926-1959 for in-sample optimization and 1960–2008 for out-of-sample testing), along with NBER business expansion and contraction dates, they find that: Keep Reading

In their March 2011 paper entitled “The Shrinking Space for Anomalies”, George Jiang and Andrew Zhang investigate the robustness of ten well-known anomalies by iteratively “shrinking the stock space” in two ways to determine whether and how the anomalies really work. The ten anomaly variables are: size, book-to-market ratio, momentum, two liquidity measures, idiosyncratic volatility, accrual, capital expenditure, sales growth and net share issuance. The first way of “shrinking the stock space” involves: (1) ranking the universe of stocks by each of the ten anomaly variables into deciles; (2) iteratively trimming deciles from side of a variable distribution that a hedge portfolio would sell and the side that a hedge portfolio would buy; and, (3) retesting the strength of the anomaly associated with the variable after each iterative trimming. The second way of “shrinking the stock space” involves: (1) trimming from the sample stocks with the smallest market capitalizations and the most extreme book-to-market ratios until size, book-to-market and momentum no longer have significant four-factor alphas for value-weighting and equal equal-weighting (thereby “perfecting” the sample for the four-factor model); and, (2) retesting the strength of the anomalies associated with the other seven variables using the perfected sample. This approach obviates weaknesses in alpha measurement via the commonly applied but imperfect three-factor (market, size, book-to-market) and four-factor (plus momentum) risk models. Using firm characteristics and trading data for all non-financial NYSE, AMEX, and NASDAQ common stocks over the period July 1962 through December 2007, they find that: Keep Reading

Which firm fundamentals predict associated stocks returns, and which ones do not? In their February 2011 paper entitled “Returns Premia on Company Fundamentals”, Kateryna Shapovalova, Alexander Subbotin and Thierry Chauveau assess the significance, stability and interplay of excess returns for individual stocks as predicted by widely used firm fundamentals. Specifically, they consider: book-to-price ratio; earnings-to-price ratio; sales-to-price ratio; cash flow-to-price ratio; dividend yield; market capitalization; growth in sales per share over the past one, three and five years; growth in earnings per share over the past one, three and five years; forecasted growth of earnings per share next year; forecasted long-term growth in earnings per share; forecasted earnings-to-price ratio; five-year average reinvested fraction of return on equity (internal growth); and, for control purposes, past returns over one, three and 12 months. Their methodology is direct stock-by-stock rather than portfolio-mediated, with the values of fundamentals across stocks normalized to a range of zero to one. They impose a three-month lag for accounting data to ensure public availability. Using monthly/quarterly firm fundamentals and monthly total stock returns for 9,363 NYSE-listed firms during 1979 through 2008, they find that: Keep Reading

Do online forums of arguably well-informed investors pay off for their members? In their February 2011 paper entitled “Talking Your Book: Social Networks and Price Discovery”, Wesley Gray and Andrew Kern study the sharing of valuation beliefs by professional investors via a social network. Specifically, they focus on ValueInvestorsClub.com, “designed to facilitate idea sharing among…250 [screened but anonymous] fundamentals-based managers (primarily hedge fund managers) who post detailed summaries of their investment analyses to the website. Once an idea is posted, it is visible to the other club members. Forty-five days after the idea is initially shared within this small community, the club grants public access to the investment thesis through a ‘guest access’ feature available to anyone with an email address.” Using approximately 2,000 long and 250 short recommendations for publicly traded common stocks shared via ValueInvestorsClub.com during 2000 through 2008, along with associated stock return, firm fundamentals and institutional ownership (SEC Form 13F) data, they find that: Keep Reading

What is the state, from an investor’s perspective, of research on the power of accounting and fundamentals to predict stock returns? In their September 2010 paper entitled “Accounting Anomalies and Fundamental Analysis: A Review of Recent Research Advances”, Scott Richardson, Irem Tuna and Peter Wysocki present an overview of post-2000 research on accounting anomalies and fundamental analysis geared toward forecasting future earnings and stock returns. They include results from matched 2009 surveys of 201 investment practitioners and 63 accounting academics on relevant beliefs about this research. They also present a new analysis of how expected risk and expected transaction costs affect the accrual and post-earnings announcement drift (PEAD) anomalies. Using for this new analysis accounting data (lagged three months) and stock returns for 1,000 relatively liquid U.S. stocks over the period 1979 through 2008, they find that: Keep Reading

Is the number of institutional owners of a stock, arguably a proxy for general investor awareness and demand, an important factor in current and future pricing of the stock? In their February 2011 paper entitled “What Makes Stock Prices Move? Fundamentals vs. Investor Recognition”, Scott Richardson, Richard Sloan and Haifeng You investigate the role of institutional ownership breadth in size-adjusted stock price dynamics. They focus on institutional investors with greater than $100 million in equity holdings, as reported quarterly to the SEC via Form 13F. They measure institutional ownership breadth as the number of institutions holding a particular stock relative to the number of institutions holding any given stock. They measure firm size based on total assets. They impose a three-month lag on data to ensure calculations use only publicly available information. Using stock returns, institutional ownership data and accounting data for a broad sample of U.S. firms over the period 1986 through 2008 (35,526 firm years), they find that: Keep Reading