Fundamental Valuation

“Usefulness of P/E10 as Stock Market Return Predictor” investigates whether P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) usefully predicts U.S. stock market returns over the long run. That analysis employs Robert Shiller’s data set, which defines P/E10 as inflation-adjusted S&P Composite Index level divided by average monthly inflation-adjusted 12-month trailing earnings of index companies over the last ten years. Do more timely country P/E10 series work for timing country stock markets and trading pairs of country stock markets? Within each country market, higher (lower) P/E10 suggests overvaluation (undervaluation). Across countries, variation in P/E10 gaps arguably indicates which country markets are relatively overvalued and undervalued. To investigate, we consider:

• P/E10 time series for Germany, Japan and the U.S. evaluated separately over available sample periods using DAX, Nikkei 225 and S&P 500 indexes, respectively. We also look at separately timing SPDR S&P 500 (SPY) and iShares MSCI Japan (EWJ).
• Japan P/E10 versus U.S. P/E10 for pair trading of SPY versus EWJ over the available sample period.

Using monthly data for the three P/E10s, the three associated stock market indexes, SPY, EWJ and 3-month U.S. Treasury bill (T-bill) yield as available during December 1981 through December 2017, we find that: Keep Reading

Is value investing particularly profitable when the price spread between cheap and expensive assets (the value spread) is extremely large (deep value)? In their November 2017 paper entitled “Deep Value”, Clifford Asness, John Liew, Lasse Pedersen and Ashwin Thapar examine how the performance of value investing changes when the value spread is in its largest fifth (quintile). They consider value spreads for seven asset classes: individual stocks within each of four global regions (U.S., UK, continental Europe and Japan); equity index futures globally; currencies globally; and, bond futures globally. Their measures for value are:

• Individual stocks – book value-to-market capitalization ratio (B/P).
• Equity index futures – index-level B/P, aggregated using index weights.
• Currencies – real exchange rate based on purchasing power parity.
• Bonds – real bond yield (nominal bond yield minus forecasted inflation).

For each of the seven broad asset classes, they each month rank assets by value. They then for each class form a hedge portfolio that is long (short) the third of assets that are cheapest (most expensive). For stocks and equity indexes, they weight portfolio assets by market capitalization. For currencies and bond futures, they weight equally. To create more deep value episodes, they construct 515 sub-classes from the seven broad asset classes. For asset sub-classes, they use hedge portfolios when there are many assets (272 strategies) and pairs trading when there are few (243 strategies). They conduct both in-sample and out-of-sample deep value tests, the latter buying value when the value spread is within its top inception-to-date quintile and selling value when the value spread reverts to its inception-to-date median. Using data as specified and as available (starting as early as January 1926 for U.S. stocks and as late as January 1988 for continental Europe stocks) through September 2015, they find that:

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What firm/asset/market conditions signal mispricing? In the November 2017 version of their paper entitled “Bonds, Stocks, and Sources of Mispricing”, Doron Avramov, Tarun Chordia, Gergana Jostova and Alexander Philipov investigate drivers of U.S. corporate stock and bond mispricing based on interactions among asset prices, financial distress of associated firms and investor sentiment. They measure financial distress via Standard & Poor’s long term issuer credit rating downgrades. They measure investor sentiment primarily with the multi-input Baker-Wurgler Sentiment Index, but they also consider the University of Michigan Consumer Sentiment index and the Consumer Confidence Index. They each month measure asset mispricing by:

1. Ranking firms into tenths (deciles) based on each of 12 anomalies: price momentum, earnings momentum, idiosyncratic volatility, analyst forecast dispersion, asset growth, investments, net operating assets, accruals, gross profitability, return on assets and two measures of net share issuance.
2. Computing for each firm the equally weighted average of its anomaly rankings, such that a high (low) average ranking indicates the firms’s assets are relatively overpriced (underpriced).

Using monthly firm, stock and bond data for a sample of U.S. firms with sufficient data and investor sentiment during January 1986 through December 2016, they find that: Keep Reading

Do firms that acquire patents in similar technologies persistently perform similarly? In the October 2017 draft of their paper entitled “Technology and Return Predictability”, Jiaping Qiu, Jin Wang and Yi Zhou examine monthly performance persistence of stocks grouped by similarity in recent firm patent activity. Specifically, they:

1. Record the patent activity of each firm by patent class over the most recent three calendar years.
2. Quantify similarity of this patent activity for each pair of firms.
3. Segregate firms into innovation groups based on patent activity similarity (top fifth of quantified similarities).
4. For each month during the next calendar year:
   • Rank stocks into fifths (quintiles) based on average prior-month, similarity-weighted return of their respective groups.
   • Form a hedge portfolio that is long (short) the equal-weighted or value-weighted stocks in the highest (lowest) return quintile.

They focus on gross average monthly return and stock return factor model alphas of the hedge portfolio as evidence of firm innovation group performance persistence. Using firm patent information by technology class during 1968 through 2010, and monthly stock data, quarterly institutional holdings and analyst coverage for a broad sample of U.S. stocks priced greater than $1 during 1968 through 2011, they find that:

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Do strongly accelerating firm earnings identify future outperforming stocks? In the October 2017 revision of their paper entitled “Earnings Acceleration and Stock Returns”, Shuoyuan He and Ganapathi Narayanamoorthy investigate the power of earnings acceleration (quarter-over-quarter change in earnings growth, which is year-over-year change in quarterly earnings) to predict abnormal stock returns. They test a hedged trading strategy that long (short) the equal-weighted tenth, or decile, of stocks with the highest (lowest) earnings acceleration for two holding intervals: (1) starting two days after earnings announcement and ending on day 30; and, (2) starting two days after earnings announcement and ending one day after the next quarterly earnings announcement. They allocate new earnings accelerations to deciles based on the prior-quarter distribution of values of earnings acceleration. They define abnormal return as that in excess of the capitalization-weighted market return. Using quarterly firm characteristics and earnings data and daily returns for a broad sample of U.S. stocks, excluding financial and utility stocks, during January 1972 through December 2015, they find that: Keep Reading

Should investors view stock returns around recurring firm events in aggregate as an exploitable anomaly? In their October 2017 paper entitled “Recurring Firm Events and Predictable Returns: The Within-Firm Time-Series”, Samuel Hartzmark and David Solomon review the body of research on relationships between recurring firm events and future stock returns. They classify events as predictable (1) releases of information or (2) corporate distributions, with some overlap. Information releases include earnings announcements, dividend announcements, earnings seasonality and predictable increases in dividends. Corporate distributions cover dividend ex-days, stock splits and stock dividends. They specify a general trading strategy to exploit these events that is long (short) stocks of applicable firms during months with (without) predictable events. They use market capitalization weighting but, since there are often more stocks in the short side, they scale short side weights downward so that overall long and short sides are equal in dollar value. Based on the body of research and updated analyses based on firm event data and associated stock prices from initial availabilities through December 2016, they conclude that:

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Are there stock return forecasts good enough to make mean-variance optimization work as a stock portfolio allocation strategy? In their October 2017 paper entitled “Mean-Variance Optimization Using Forward-Looking Return Estimates”, Patrick Bielstein and Matthias Hanauer test whether firm implied cost of capital (ICC) based on analyst earnings forecasts is effective as a stock return forecast for mean-variance portfolio optimization. They derive ICC annually for each stock as the internal rate of return (discount rate) implied by a valuation model that equates forecasted cash flows, derived from analyst earnings forecasts, to market valuation. To refine ICC estimates, they correct predictable analyst forecast errors (slow reactions to news) by including a standardized, rescaled momentum variable based on return from 12 months ago to one month ago (ICC_adj). They then employ ICC_adj to specify annual (each June 30) mean-variance optimized (maximum Sharpe ratio) long-only stock allocations (with maximum weight 5%) based on stock return covariances calculated from returns over the last 60 months. For benchmarks, they consider the value-weighted market portfolio (VW), the equal-weighted market portfolio (EW), the minimum variance portfolio (MVP) and a maximum Sharpe ratio portfolio based on 5-year moving average actual returns (HIST). They focus on U.S. stocks, which have relatively broad analyst coverage. They test robustness of findings with data from selected international developed markets, different return variable specifications, different subperiods and impact of transaction costs. Using monthly data for the 1,000 U.S. common stocks with the biggest prior-month market capitalizations since June 1985 and the 250 biggest stocks in each of Europe, UK and Japan since 1990, all through June 2015, they find that:

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Does uncertainty about future firm earnings underlie stock factor returns? In their August 2017 paper entitled “Uncertainty, Momentum, and Profitability”, Claire Liang, Zhenyang Tang and Xiaowei Xu examine relationships between analyst uncertainty about current-year firm earnings and four U.S. stock return anomalies. They each month estimate uncertainty for each stock as square root of the average squared differences between individual analyst forecasts for current-year earnings and reported earnings per share, divided by stock price. They then each month sort firms into fifths (quintiles) by:

• Uncertainty –  as specified.
• Price momentum – stock returns from 12 months ago to one month ago.
• Earnings momentum – most recently announced quarterly earnings minus earnings from the same quarter one year ago, divided by the standard deviation of seasonal differences in earnings for the previous eight quarters.
• Operating profitability – annual revenue minus cost of goods sold, interest expense and selling, general, and administrative expenses, divided by book equity for the last fiscal year.
• Return on equity – earnings before extraordinary items from the most recent quarter divided by prior-quarter book equity.

They calculate gross monthly returns for each factor via an equal-weighted or value-weighted hedge portfolio that is each month long (short) the quintile of stocks with the highest (lowest) factor values. They test the power of uncertainty to explain other factor returns via regressions against uncertainty factor returns. Since some stocks may not have analyst coverage, they test whether idiosyncratic volatility and earnings forecast dispersion are effective substitutes for uncertainty. Using the specified monthly data for all NYSE/AMEX/NASDAQ stocks priced at least $1 during 1983 through 2013, they find that:

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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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