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Fundamental Valuation

What fundamental measures of business success best indicate the value of individual stocks and the aggregate stock market? How can investors apply these measures to estimate valuations and identify misvaluations? These blog entries address valuation based on accounting fundamentals, including the conventional value premium.

A Better P/E10?

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

Best Firm Profitability Metric Worldwide?

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

Financial Analysts 25% Optimistic?

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

SACEMS and SACEVS Changes for Coordination and Liquidity

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

Robustness of Accounting-based Stock Return Anomalies

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

Combining Stock Fundamentals Trend and Price Momentum

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

Expected Investment Growth as Stock Return Predictor

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

How Investors Really Treat Dividends

Do investors treat stock dividends as part of total returns, or do they view them as a separate income stream? In their December 2016 paper entitled “The Dividend Disconnect”, Samuel Hartzmark and David Solomon investigate whether trading and pricing of stocks exhibit a “free dividend” fallacy (disregard for the fact that dividends directly debit stock price as paid). Specifically, they test whether investors: (1) consider both dividends and capital gains when evaluating stock performance; (2) view dividend stocks differently based on market conditions/competing sources of return; and, (3) reinvest dividends and capital gains differently. Using daily individual trader data during January 1991 through November 1996, quarterly institutional and mutual fund holdings data (SEC filings) during 1980 through 2015 and contemporaneous daily stock and stock index prices, return and dividend data, they find that: Keep Reading

Exploiting P/E10 to Time the U.S. Stock Market

Is the relationship between Cyclically Adjusted Price to Earnings Ratio (CAPE, or P/E10) and future long-term stock market returns evidence of market inefficiency? In other words, can investors exploit P/E10 to beat the market? In their November 2016 paper entitled “Shiller’s CAPE: Market Timing and Risk”, Valentin Dimitrov and Prem Jain examine whether investors with a 10-year investment horizon can beat the market by holding either the S&P 500 Index or 10-year U.S. Treasury notes (T-notes) as a low-risk alternative according to whether P/E10 is low or high. Their methodology is comparison of averages and volatilities (standard deviations) of future 10-year nominal total returns by ranked tenth (decile) of monthly P/E10. They assume reinvestment of dividends and interest at a monthly frequency. Using monthly values of P/E10, stock market total returns (including dividends) and T-note yields from Robert Shiller’s database during January 1871 through August 2016, they find that: Keep Reading

Exploiting Manufactured Earnings Surprises

Is there a way to tell which corporate executives are manipulating earnings? In their November 2016 paper entitled “Expectations Management and Stock Returns”, Jinhwan Kim and Eric So examine the relationship between firm incentives to manage earnings and stock returns around earnings announcements. They define an expectations management incentives (EMI) indicator that combines three groups of incentives:

  1. Attention – the extent of external scrutiny of reported earnings, consisting of analyst coverage and institutional ownership.
  2. Resources – the capacity to manage expectations, consisting of cash reserves and shareholder equity.
  3. Pressure – unsustainable growth expectations, measured by trailing sales growth.

Specifically, monthly EMI is average percentile rank of analyst coverage, institutional ownership, shareholder equity per share, cash per share and sales growth, divided by the difference between the maximum and minimum percentiles of these characteristics, all as of 12 months ago. Using the specified data and associated returns for a broad sample of U.S. stocks encompassing about 420,000 quarterly earnings announcements during 1985 through 2015, they find that: Keep Reading

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