Fundamental Valuation

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

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

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

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

Is there a straightforward way to incorporate current business/economic climate into equity market valuation ratios? In their September 2016 paper entitled “Generalized Financial Ratios to Predict the Monthly Equity Market Premium”, Andres Algaba and Kris Boudt introduce and test a generalized price-dividend ratio (GDPR) that takes into account recent business and discount rate conditions, as follows:

Where P is equity market (index) price, D is aggregate market dividend, the beta exponent for D accounts for changes in the kinds of companies dominating the market (those that retain versus those that pay out earnings) and the lambda multiplier for D accounts for variation in the discount rate used to evaluate dividend streams. The t subscripts indicate that all vary over time. They estimate beta and lambda via regressions using rolling historical windows of five or nine years (representing two views of business cycle length). They test the ability of GPDR to predict the U.S. equity market premium (ERP) using inception-to-date forecasting regressions, without and with a rule that switches to the historical average ERP when recent (last three and six months) GPDR predictions are poor. They use the historical average ERP as a benchmark. They employ the first nine years of data to estimate initial GPDR. They then use the next 20 years (1956-1975) for the first predictive regression, leaving 39 years for out-of-sample monthly ERP predictions (1976-2014). To assess the economic value of using GPDR to predict ERP, they consider a risk-averse, mean-variance optimizing investor who each month reallocates across equities and U.S. Treasury bills (T-bills). This investor employs a 5-year rolling window to estimate volatility, does not sell short and limits leverage to 1.5 with one-way trading friction 0.1%. Using monthly levels of the S&P 500 Index, monthly 12-month historical dividends and monthly 3-month T-bill yield as the risk-free rate during January 1947 through December 2014, they find that: Keep Reading

What variables best predict U.S. stock market returns? In his June 2016 paper entitled “Which Variables Predict and Forecast Stock Market Returns?”, David McMillan examines the power of 25 variables to predict excess return (relative to the 3-month U.S. Treasury bill yield) of Shiller’s S&P Composite Index both in-sample and out-of-sample. He chooses variables based on connectedness to expected cash flow/dividends and risk and assigns them to five groups:

1. Financial ratios: dividend-price, price-to-earnings, cyclically adjusted price-to-earnings (CAPE or P/E10), Tobin’s Q and market capitalization-to-Gross Domestic Product (GDP).
2. Economic:  GDP cycle, GDP acceleration (rate of change in GDP growth), consumption growth, 10-year to 3-month Treasuries term spread and inflation.
3. Labor: wage growth, unemployment, natural rate of unemployment, productivity growth and labor market conditions.
4. Housing: house price growth, house affordability, home ownership, housing supply and new house sales.
5. Other: University of Michigan Consumer Sentiment, Purchasing Managers Index, National Financial Conditions Index, leverage and non-financial leverage.

He employs regressions to test in-sample predictive power. He then tests out-of-sample forecasts starting in 2000 using various forecast methods and accuracy measures and considering both single-variable and multi-variable models. Using the specified data series as available during 1973 through 2014, he finds that: Keep Reading