Fundamental Valuation

Do markets exploitably misprice stocks? In their July 2017 paper entitled “Global Market Inefficiencies”, Söhnke Bartram and Mark Grinblatt assess the exploitability of deviations from fair stock prices worldwide based on publicly known accounting data. Specifically, they each month:

1. Regress market capitalizations of all stocks sampled on 21 annual accounting variables from most recently disclosed balance sheets and income/cash flow statements. Returns play no role, suppressing potential snooping bias. 
2. Calculate percentage mispricings of stocks as regression residuals (unexplained by accounting variables) divided by market capitalization.
3. Rank stocks into fifths (quintiles) based on within-country percentage mispricings.
4. Group stocks in the same quintile across countries globally, by developed or emerging, or by region (Europe, Asia Pacific, Americas, and Africa/Middle East).
5. Calculate next-month equal-weighted and value-weighted gross returns and alphas by quintile and for a hedge portfolio that is long (short) the most underpriced (overpriced) quintiles. Alphas derive from eight risk factors (market, size, book-to-market, investment, profitability, momentum, short-term reversal, and long-term reversal) calculated by region.
6. Estimate the impact of quintile portfolio rebalancing frictions on gross returns/alphas.

To suppress rebalancing frictions, they also consider a holding interval of 12 months (averaging returns of 12 overlapping portfolios formed each month and held for a year). They run tests controlling for past returns, earnings surprises, country effects and other possible sources of abnormal returns. Using monthly values of 21 annual accounting variables for more than 25,000 non-financial firms, associated stock returns and stock trading frictions from 36 countries during March 1993 through December 2016, they find that: Keep Reading

Are high-quality stocks worth the price? In the June 2017 update of their paper entitled “Quality Minus Junk”, Clifford Asness, Andrea Frazzini and Lasse Pedersen investigate whether high-quality stocks outperform low-quality stocks. They define high-quality stocks as those that are profitable, growing, safe and well-managed. Specifically, they compute a single quality score for each stock by averaging scores for three components calculated as follows:

• Profitability – average of rankings for (high) gross profits/assets, return on equity, return on assets, cash flow/assets, gross margin and fraction of earnings that is cash.
• Growth – average of rankings for (high) prior five-year growth rates for each of the six profitability measures.
• Safety – average of rankings for (low) market beta, idiosyncratic volatility, leverage, bankruptcy risk and volatility of return on equity.

They consider two modes of analysis: quality-sorted portfolios and quality-minus-junk (QMJ) long-short factor portfolios. Quality-sorted portfolios are by value-weighted tenths (deciles), reformed at the end of each calendar month. QMJ factor portfolio return is the average return on two value-weighted top 30% of quality portfolios (big stocks and small stocks separately) minus the average return on two value-weighted bottom 30% of quality portfolios (big stocks and small stocks separately), reformed monthly by sorting first on size and then on quality. For both modes, global portfolios are value-weighted composites of country portfolios in U.S. dollars. Using characteristics and returns for a broad sample of U.S. stocks since June 1957 and samples of stocks from 24 developed markets (including the U.S.) since June 1989, and contemporaneous U.S. Treasury bill yield as the risk-free rate, all through December 2016, they find that:

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Do investors fixate on price-to-earnings ratio (P/E) and thereby create trading opportunities as P/Es change? In his June 2017 paper entitled “P/E Ratios and Value Investor Attention”, Jordan Moore examines market responses to U.S. common stocks sorted by earnings yield (inverse of P/E). He defines P/E as the ratio of stock price to the sum of the last available four quarters of net earnings excluding extraordinary items divided by current shares outstanding. For monthly tests, he assumes that earnings become available at the close of the last trading day of the reporting month. For daily and weekly tests, he assumes that earnings become available at the close of the first trading after earnings release date. He separately analyzes stocks with (published) positive and (generally unpublished) negative earnings yields. For comparison, he similarly calculates current monthly book-to-market ratios and sorts stocks by that alternative valuation metric. Using the specified accounting and price data for a broad sample of U.S. common stocks during 1973 through 2015, he finds that: Keep Reading

Is there a way to enhance the ability of the cyclically-adjusted price-to-earnings ratio (P/E10 or CAPE) to predict U.S. stock market returns by incorporating real interest rates? In their June 2017 paper entitled “Improving U.S. Stock Return Forecasts: A ‘Fair-Value’ Cape Approach”, Joseph Davis, Roger Aliaga-Diaz, Harshdeep Ahluwalia and Ravi Tolani introduce “fair-value” CAPE that accounts for a dynamic, positive relationship between real 10-year U.S. Treasury note (T-note) yield (cost of capital) and real earnings yield (return on equity). They hypothesize that a lower real T-note yield should imply a lower earnings yield and thus a higher fair-value CAPE. Their use of fair-value CAPE to forecast stock market return involves:

• Each month, execute a multiple vector autoregression of the logarithms of the following five variables separately for each of the last 12 months: (1) inverse of CAPE; (2) expected real T-note yield based on a 10-year U.S. inflation forecast; (3) U.S. inflation; (4) realized S&P 500 Index price volatility over the last 12 months; and, (5) realized volatility of changes in real T-note yield over the last 12 months. Their 10-year inflation forecast is the average of 120 monthly forecasts generated via autoregression of the U.S. consumer price index over a 30-year rolling window.
• Each month, forecast 10-year stock market return (see the chart below) by summing: (1) percentage change in CAPE from the preceding vector autoregression; (2) constant earnings growth equal to its long-term average; and, (3) dividend yield calculated as earnings yield times the historical payout ratio.

They then compare out-of-sample forecasts of 10-year U.S. stock market returns for 1960 through 2016 and 1985 through 2016 generated by fair-value CAPE and two conventional CAPEs: Shiller CAPE based on Generally Accepted Accounting Principles (GAAP); and, Siegel CAPE based on National Income and Product Accounts (NIPA) earnings. Using Shiller’s data and NIPA earnings during 1950 through 2016, they find that: Keep Reading

Which firm profitability metric best predicts stock returns? In their May 2017 paper entitled “Alternative Profitability Measures and Cross Section of Expected Stock Returns: International Evidence”, Nusret Cakici, Sris Chatterjee and Yi Tang compare abilities of 12 profitability ratios to predict stock returns across four regions (North America, Europe, Japan and Asia-Pacific). They consider three measures of profitability: gross profit (GP); operating income (OI); and, earnings before interest and taxes (EBIT). They consider four scaling variables: enterprise value (EV); book value of assets (BA); market value of equity (ME); and, book value of equity (BE). Specifically, at the end of June each year, they rank stocks by region into fifths (quintiles) based on each of the 12 profitability metrics and hold the quintiles for one year. They measure the predictive power of each metric as average monthly return to a portfolio that is annually long (short) the top (bottom) quintile. Using firm accounting data and monthly stock returns for 23 developed country equity markets allocated to one of the four regions during January 1991 through December 2016, they find that: Keep Reading

How accurate are consensus firm earnings forecasts worldwide at a 12-month horizon? In his May 2016 paper entitled “An Empirical Study of Financial Analysts Earnings Forecast Accuracy”, Andrew Stotz measures accuracy of consensus 12-month earnings forecasts by financial analysts for the companies they cover around the world. He defines consensus as the average for analysts coverings a specific stock. He prepares data by starting with all stocks listed in all equity markets and sequentially discarding:

1. Stocks with market capitalizations less than $50 million (U.S. dollars) as of December 2014 or the last day traded before delisting during the sample period.
2. Stocks with no analyst coverage.
3. Stocks without at least one target price and recommendation.
4. The 2.1% of stocks with extremely small earnings, which may results in extremely large percentage errors.
5. All observations of errors outside ±500% as outliers.
6. Stocks without at least three analysts, one target price and one recommendation.

He focuses on scaled forecast error (SFE), 12-month consensus forecasted earnings minus actual earnings, divided by absolute value of actual earnings, as the key accuracy metric. Using monthly analyst earnings forecasts and subsequent actual earnings for all listed firms around the world during January 2003 through December 2014, he finds that: Keep Reading

We developed the Simple Asset Class ETF Momentum Strategy (SACEMS) about six years ago and the Simple Asset Class ETF Value Strategy (SACEVS) about two years ago independently, focusing on the separate logic of asset choices for each. As tested in “SACEMS-SACEVS Mutual Diversification”, these two strategies are mutually diversifying, so combining them works better in some ways than using one or the other. Beginning May 2017, we are making four changes to these strategies for ease of implementation and combination, with modest compromises in logic. Specifically, we are: Keep Reading

Do accounting-based stock return anomalies exist in samples that precede and follow those in which researchers discover them? In their November 2016 paper entitled “The History of the Cross Section of Stock Returns”, Juhani Linnainmaa and Michael Roberts examine the robustness of 36 accounting-based stock return anomalies, with initial focus on profitability and investment factors. Anomalies tested consists of six profitability measures, four earnings quality measures, five valuation ratios, 10 growth and investment measures, five financing measures, three distress measures and three composite measures. For each anomaly, they compare pre-discovery, in-sample and post-discovery anomaly average returns, Sharpe ratios, 1-factor (market) and 3-factor (market, size, book-to-market) model alphas and information ratios. Key are previously uncollected pre-1963 data. They assume accounting data are available six months after the end of firm fiscal year and generally employ annual anomaly factor portfolio rebalancing. Using firm accounting data and stock returns for a broad sample of U.S. stocks during 1918 through December 2015, they find that: Keep Reading

Are trend in stock fundamentals and stock price momentum mutually reinforcing return predictors? In their January 2017 paper entitled “Dual Momentum”, Dashan Huang, Huacheng Zhang and Guofu Zhou combine a measure of fundamentals trend and past return to form a U.S. stock portfolio designed to exploit the powers of both to select outperforming stocks. To construct their measure of fundamentals trend, they each month:

1. For each stock, collect the last eight quarters of seven variables: return on equity; return on assets; earnings per share; accrual-based operating profit to equity; cash-based operating profit to assets; gross profit to assets; and, net payout ratio.
2. For each stock, calculate four moving averages for each fundamental variable over the last 1, 2, 4 and 8 quarters (for a total of 28 moving averages per stock).
3. Across all stocks, relate next-month excess stock return to the most recent 28 fundamentals moving averages via multiple regression to obtain 28 fundamentals trend betas.
4. For each fundamentals beta for each stock, calculate an expected beta as the average of the last 12 monthly betas.
5. For each stock, calculate a fundamentals-implied return (FIR) by applying the 28 expected betas to the most recently available 28 fundamentals moving averages.

They then each month rank stocks into value-weighted fifths (quintiles) based on FIR. Separately, they each month rank the same stocks into value-weighted quintiles based on conventional price momentum (cumulative return from 12 months ago to one month ago). Using quarterly fundamentals and monthly returns for a broad sample of U.S. stocks during January 1973 through September 2015, they find that: Keep Reading

Do stocks with expectations of high capital expenditures (growth opportunities) outperform those with expectations of low capital expenditures? In their December 2016 paper entitled “Expected Investment Growth and the Cross Section of Stock Returns”, Jun Li and Huijun Wang examine the power of expected investment growth (EIG) to predict cross-sectional stock returns. They construct EIG for each stock monthly in two steps:

1. Regress actual investment (capital expenditures) growth jointly versus prior-month momentum (stock return from 12 months ago to two months ago), q (firm market value divided by capital) and cash flow.
2. Apply the resulting regression betas to latest momentum, q and cash flow values to project next-month EIG.

They measure the EIG factor premium as gross average return to a portfolio that is each month long (short) the value-weighted tenth, or decile, of stocks with the highest (lowest) EIGs. They consider an array of tests to measure the strength and robustness of this factor premium. Using monthly data for a broad sample of U.S. stocks (excluding financial and utility stocks) during July 1953 through December 2015, they find that: Keep Reading