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Value Allocations for June 2019 (Final)
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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.

Combined Simple Value-Momentum Asset Class ETF Strategy

The Value Strategy tracks the performance of two versions of the “Simple Asset Class ETF Value Strategy”  (SACEVS), which seeks diversification across a small set of asset class exchange-traded funds (ETF) plus a monthly tactical edge from potential undervaluation of term, credit and equity risk premiums relative to historical averages. The two versions are: (1) most undervalued premium (Best Value); and, (2) weighting all undervalued premiums according to respective degree of undervaluation (Weighted).

The Momentum Strategy tracks the performance of three versions of the “Simple Asset Class ETF Momentum Strategy” (SACEMS), which seeks strategic diversification across asset classes via ETFs plus a monthly tactical edge from intermediate-term momentum. The three versions, all based on total ETF returns over recent months, are: (1) top one of nine ETFs (Top 1); (2) equally weighted top two (EW Top 2); and, (3) equally weighted top three (EW Top 3).

As of today, we commence tracking performance of Combined Value-Momentum Strategy (SACEVS-SACEMS), seeking diversification across asset classes and two widely accepted anomalies. This strategy holds SACEVS Best Value and SACEMS EW Top 3 with equal weights and end-of-month rebalancing coincident with SACEVS and SACEMS portfolio reformations.

Exploitability of Deep Value across Asset Classes

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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SACEVS with Margin

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS)? SACEVS each month allocates funds to one or more of the following three asset class exchange-traded funds (ETF), plus cash, based on relative valuations:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

To investigate effects of margin, we augment SACEVS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for Best Value (which picks the most undervalued premium) and Weighted (which weights all undervalued premiums according to degree of undervaluation) variations of SACEVS. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly total (dividend-adjusted) returns for the specified assets during July 2002 (limited by TLT and LQD) through October 2017, we find that: Keep Reading

Financial Distress, Investor Sentiment and Downgrades as Asset Return Anomaly Drivers

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

Firm Innovation Group Performance Persistence

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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Earnings Acceleration as Stock Return Predictor

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

Aggregate Firm Events as a Stock Return Anomaly

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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Return Forecasts Good Enough for Mean-variance Optimization?

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 (ICCadj). They then employ ICCadj 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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Analyst Uncertainty as a Super-anomaly

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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Alternative Tests of P/E10 Usefulness

In response to the market timing backtest in “Usefulness of P/E10 as Stock Market Return Predictor”, subscribers suggested two modifications for exploiting P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE):

  1. Instead of binary signals that buy (sell) stocks when P/E10 crosses below (above) its historical average, use a scaled allocation to stocks that considers how far P/E10 is from average.
  2. Instead of holding cash when not in stocks, hold 10-year government bonds (with risk of capital loss on the bonds).

To investigate, we backtest these modifications. Using monthly data from Robert Shiller, including S&P Composite Index level, associated dividends, 10-year government bond yields and values of P/E10 during January 1871 through March 2017, we find that: Keep Reading

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