Fundamental Valuation

Research (see “Asset Growth Rate as a Return Indicator” and “Asset Growth a Bad Sign for Stocks Everywhere?”) indicates that stocks of firms with high asset growth rates tend subsequently to underperform the market. Does this finding translate to the overall stock market? In the April 2014 version of his paper entitled “Asset Growth and Stock Market Returns: a Time-Series Analysis”, Quan Wen examines whether the asset growth anomaly observed at the firm level applies in aggregate to the U.S. stock market. He also investigates whether any aggregate effect is predominantly behavioral or risk-based. He estimates aggregate growth rate quarterly as the market capitalization-weighted sum of firm-level percentage changes in book value of total assets. To ensure all asset data is known to investors, he relates asset growth rate to returns two quarters later. Using quarterly U.S. stock market excess returns (relative to the risk-free rate), asset growth rates for listed U.S. firms that employ calendar year accounting, analyst forecasts/revisions, stock returns around earnings announcements, and data required for comparison of asset growth with other U.S. stock market indicators during 1972 through 2011, he finds that: Keep Reading

In the introduction to his 2014 book entitled Global Value: How to Spot Bubbles, Avoid Market Crashes, and Earn Big Returns in the Stock Market, author Mebane Faber, ponders: “Can we or can’t we predict when a bubble is occurring? Below we [try] to find an objective way to identify bubbles, avoid their popping, and invest in their aftermath.” He focuses on the the cyclically adjusted price-to-earnings (CAPE) ratio (or P/E10) as a bubble indicator, including a variety of charts. Based on concepts originally developed and refined by Benjamin Graham, David Dodd, Robert Shiller and many others, he concludes that: Keep Reading

Can the best traders reliably exploit the ex-dividend effect (the tendency for dividend-paying stocks to fall by less than the dividend amount after paying the dividend)? In their March 2014 paper entitled “Ex-Dividend Profitability and Institutional Trading Skill”, Tyler Henry and Jennifer Koski examine whether highly skilled traders bearing very low transaction costs (some institutions) successfully exploit this effect. They use actual transaction prices and actual transaction costs. They segment their sample period into three regimes: Regime 1 is pre-decimalization and pre-tax reform that equalizes capital gains and dividend tax rates; Regime 2 is post-decimalization and pre-tax reform; and, Regime 3 is post-decimalization and post-tax reform. They specify the ex-dividend trading window as days -5 through +5 relative to ex-dividend day 0. Using a large proprietary set of institutional common stock buy and sell transactions and associated transaction costs, and contemporaneous dividends, returns, bid/ask prices and trading volumes for those stocks, during 1999 through 2007 (24,741 ex-dividend events for 1,351 firms), they find that: Keep Reading

Is the conventional wisdom that traders can scalp part of cash dividends by buying stocks just before ex-dividend day and selling just after reliable across exchanges? In their January 2014 paper entitled “Ex-Dividend Day Stock Price Behavior – the NASDAQ Evidence”, Shishir Paudel and Sabatino Silveri investigate whether dividend-paying NASDAQ stocks exhibit ex-dividend day price behaviors similar to those of more closely studied dividend-paying NYSE stocks. They measure ex-dividend day price drops with the price-drop ratio (PDR), the ratio of the change in stock price to the amount of the cash dividend. Using a sample of 49,325 (90,867) cash dividends for NASDAQ (NYSE) stocks during 1983 through 2011 (ignoring dividends less than $0.05), they find that: Keep Reading

Are “quality” country stock markets good places to invest? In his December 2013 paper entitled “Quality Investing and the Cross-Section of Country Returns”, Adam Zaremba investigates whether aggregate financial quality determines country stock market performance. Specifically, he uses two-month lagged 12-month data for listed firms to calculate the following eight quality metrics at the country level in each of 66 countries:

1. Return on Assets (ROA) – ratio of net income to average assets.
2. Return on Equity (ROE) – ratio of net income to average common equity.
3. Profit Margin (PM) – ratio of net income to sales.
4. Operating Margin (OM) – ratio of operating income to sales.
5. Gross Margin (GM) – ratio of gross income (sales minus cost of goods sold) to sales.
6. Assets to Debt (AD)- ratio of total assets to short-term plus and long-term borrowings.
7. EBITDA to Debt (ED) – ratio of EBITDA to short-term plus long-term borrowings.
8. Current Ratio (CR) – ratio of current assets to current liabilities.

He uses MSCI country indexes to measure country market returns. He also calculates for each country the total market capitalization, aggregate book-to-market ratio and 12-month market return momentum. He tests relationships between country-level quality factors and returns by constructing portfolios of the equally weighted top 30% (high-quality), middle 40% (mid-quality) and bottom 30% (low-quality) of country markets based on each quality metric. He also measures for each quality metric the performance of a fully collateralized portfolio that is each month long (short) the equally weighted 30% of country markets with the highest (lowest) quality. To test sensitivity to the currency used, he perform all calculations separately in U.S. dollars, euros and yen. Using monthly accounting and return data as specified during May 2000 through October 2013, he finds that: Keep Reading

Why does the gross profitability stock-screening strategy work? In their December 2013 paper entitled “Factoring Profitability”, Michael Branch, Lisa Goldberg and Ran Leshem explore the drivers of the gross profitability strategy for U.S. stocks. Specifically, they examine the contributions of factors in the conventional Fama-French-Carhart four-factor (FFC4) model of equity returns and the Barra USE4 12-factor model of equity returns to an idealized, industry-neutral gross profitability strategy. They define gross profitability as revenue minus cost of goods sold divided by assets. Idealized means a hedge portfolio that is each month long (short) stocks of firms in the top (bottom) fifth of gross profitability with no rebalancing frictions. Industry-neutral means constraining industry weights in the portfolio to equal those in the market index. Using stock price and accounting data and contemporaneous asset pricing model factor values as available during July 1963 through December 2012, they find that: Keep Reading

Do firm efforts to pay shareholders directly (via dividends) and indirectly (via share repurchases and paydown of debt) translate to stock outperformance? In their May 2012 paper entitled “Enhancing the Investment Performance of Yield-Based Strategies”, flagged by a subscriber, Wesley Gray and Jack Vogel compare aggregate performance statistics of stocks ranked by the following four progressively comprehensive yield metrics:

1. DIV: dividend yield.
2. PAY1: payout encompassing dividend plus share repurchase yield.
3. PAY2: payout encompassing dividend plus net repurchase (repurchase minus issuance) yield.
4. SHYD: comprehensive shareholder yield encompassing dividend plus net repurchase plus net debt paydown (annual difference in debt load divided by market capitalization) yield.

They focus on annually rebalanced, value-weighted portfolios with financial stocks excluded. Using monthly return, dividend, stock repurchase/issuance, debt load and other accounting data for a broad sample of U.S. stocks during 1971 through 2011, they find that: Keep Reading

What do insiders regard as red flags for corporate earnings manipulation? In their May 2013 paper entitled “Earnings Quality: Evidence from the Field”, Ilia Dichev, John Graham, Campbell Harvey and Shiva Rajgopal report earnings quality insights from Chief Financial Officers (CFO) of publicly owned companies via 169 responses to an anonymous online survey, plus 12 telephone interviews. They invited survey participation via emails in late October 2011 and closed the survey in early December 2011. Firms of responding CFOs are mostly from the manufacturing (38%), banking/finance/insurance (16%) and healthcare/pharmaceuticals (8%) sectors, and about 27% have annual revenues greater than $10 billion. Based on survey results, CFOs believe that: Keep Reading

What is the state of cyclically adjusted price-earnings ratios (CAPE, P/E10 or Shiller PE), stock index level divided by average real earnings over the past ten years, across country equity markets worldwide? In his October 2013 paper entitled “What the Shiller PE Says About Global Equity Markets: Update 2013”, Joachim Klement updates expected returns for equity markets around the world based on P/E10 (see “Predictive Power of P/E10 Worldwide”). He adjusts P/E10 for economic conditions for each country via regression of P/E10 versus real GDP growth, real per capita GDP growth, real interest rate and inflation. Using stock index level, P/E10 and economic data for 20 developed and 18 emerging equity markets as available through September 2013, he finds that: Keep Reading

Which long-term smoothed (10-year average) stock valuation metric works best to screen stocks? In the end-of-September 2013 version of their paper entitled “On the Performance of Cyclically Adjusted Valuation Measures”, Wesley Gray and Jack Vogel compare the abilities of five valuation ratios expressed as yields to predict U.S. stock returns, as follows: 

1. 10-year average real earnings to market capitalization (CA-EM)
2. 10-year average real book values to market capitalization (CA-BM)
3. 10-year average real earnings before interest, taxes, depreciation and amortization to total enterprise value (CA-EBITDA/TEV)
4. 10-year average real free cash flow to total enterprise value (CA-FCF/TEV)
5. 10-year average real free gross profits to total enterprise value (CA-GP/TEV)

They adjust for inflation using the monthly U.S. Consumer Price Index (CPI). Each year on June 30 (with accounting data lagged to ensure availability), they sort stocks into tenths (deciles) based on each ratio. They then calculate monthly decile returns over the next 12 months based on equal initial weights, with either annual (next June 30) or monthly decile reformation. For monthly reformation, they recalculate market capitalizations (but not valuation ratios) each month. They also test a stock return momentum overlay, each month splitting each valuation ratio top decile into high-momentum and low-momentum halves, with momentum defined as cumulative return from 12 months ago to one month ago. They do not account for trading frictions or taxes. Using stock prices and accounting data for U.S. firms with at least 10 years of the required accounting data and market capitalizations above the 40th percentile for NYSE-listed stocks, and contemporaneous CPI data, during 1962 through 2012, they find that: Keep Reading