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.

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Value Strategy Update

We have updated the the monthly asset class ETF value strategy weights and associated performance data at Value Strategy.

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 (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.

To investigate, we run backtests for 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 2016, we find that: Keep Reading

Aggregate Technological Innovation and Stock Market Returns

Does a surge in patent activity predict a surge in, or creative destruction of, equity value? To explore this question, assuming patent applications need not be approved to be exploited, we examine relationships between the growth rates of U.S. patent applications/patents as simple measures of innovation and U.S. stock market returns. Using U.S. patent activity (numbers of applications and patents) by calendar year and annual levels of  Shiller’s S&P Composite Index during 1871 through 2015  (142 years), we find that: Keep Reading

Usefulness of P/E10 as Stock Market Return Predictor

Does P/E10 (Cyclically Adjusted Price-Earnings ratio, CAPE) usefully predict U.S. stock market returns? Per Robert Shiller’s data set, P/E10 is 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. To investigate its usefulness, we consider in-sample regression/ranking and out-of-sample cumulative performance tests. Using monthly values for nominal and real S&P Composite Index (calculated as average of daily closes during the month), associated dividends, 12-month trailing real earnings and interest rates during January 1871 through March 2016, we find that: Keep Reading

Practicality of Piotroski’s FSCORE Strategy

Can a typical investor exploit the high returns reported for Piotroski’s FSCORE strategy as applied to U.S. stocks? In their October 2015 paper entitled “The Piotroski F-Score: A Fundamental Value Strategy Revisited from an Investor’s Perspective”, Christopher Krauss, Tom Kruger and Daniel Beerstecher examine whether individual investors can exploit the American Association of Individual Investors’ (AAII) interpretation of this strategy (24% gross annual return over the last decade). They consider equal-weighted and value-weighted long-only (FSCORE 8 and 9) and long-short (short the S&P 500 Index) versions of the strategy, with monthly or weekly rebalancing. They first calculate gross performance and then progressively add realistic obstacles to/costs of trading. They assume average round-trip trading frictions of 0.2% for broker commissions plus 0.5% for bid-ask spreads (but no costs for shorting the S&P 500 Index). Using AAII’s FSCORE screen to generate monthly and weekly portfolios of U.S. stocks via AAII’s Stock Investor Pro platform matched to total stock returns from Datastream during January 2005 through April 2015, they find that: Keep Reading

Economic/Market Factor Investing Heat Map

Can an approach that describes each asset class as a bundle of sensitivities to economic/market conditions improve investment decision-making? In their March 2016 paper entitled “Factor-Based Investing”, Pim Lausberg, Alfred Slager and Philip Stork develop a “heat map” to summarize how returns for seven asset classes relate to six economic/market factors. The seven asset classes are: (1) government bonds; (2) investment grade corporate bonds; (3) high-yield corporate bonds; (4) global equity; (5) real estate; (6) commodities; and, (7) hedge funds. The six economic/market factors are: (1) change in consensus forecast of next-year economic growth; (2) change in consensus forecast for next-year inflation; (3) illiquidity (Bloomberg market liquidity indexes); (4) volatility of stock market indexes; (5) credit spread (return on investment grade corporate bonds minus return on duration-matched U.S. Treasuries); and, (6) term spread (return on government bonds of duration 7-10 years minus return on government bills of duration three months). They also provide suggestions on how to use the heat map in the investment process. Using monthly asset class returns and factor estimation inputs during 1996 through 2013, they find that: Keep Reading

Comparing CAPE to Other Stock Market Valuation Ratios

Is Robert Shiller’s cyclically adjusted price-to-earnings ratio (CAPE or P/E10) a better predictor of long-term stock market performance than other valuation ratios? In his January 2016 paper entitled “Predicting Stock Market Returns Using the Shiller CAPE — An Improvement Towards Traditional Value Indicators?”, Norbert Keimling first examines whether reduced dividend payout, new accounting standards and structural changes to key stock indexes limit the comparability of current and historical CAPEs. He then investigates whether CAPE is better at forecasting long-term equity market returns than price-to-earnings ratio, price-to-cash flow ratio, price-to-book ratio, dividend yield and CAPE adjusted for payout. Based on these findings, he applies CAPE and price-to-book ratio to predict long-term total returns for 17 equity markets in local currencies. Using Shiller’s monthly data for the U.S. stock market since 1871 and monthly data for 16 other country stock market indexes since 1969, all through 2015, he finds that: Keep Reading

Breaking Down Smart Beta

What kinds of smart beta work best? In their January 2016 paper entitled “A Taxonomy of Beta Based on Investment Outcomes”, Sanne De Boer, Michael LaBella and Sarah Reifsteck compare and contrast smart beta (simple, transparent, rules-based) strategies via backtesting of 12 long-only smart beta stock portfolios. They assign these portfolios to a framework that translates diversification, fundamental weighting and factor investing into core equity exposure and style investing (see the figure below). They constrain backtests to long-only positions, relatively investable/liquid stocks and quarterly rebalancing, treating developed and emerging markets separately. Backtest outputs address gross performance, benchmark tracking accuracy and portfolio turnover. Using beta-related data for developed market stocks during 1979 through 2014 and emerging market stocks during 2001 through 2014, they find that: Keep Reading

FactSet S&P 500 Earnings Growth Estimate Evolutions

A subscriber, citing the weekly record of S&P 500 earnings growth estimates in the “FactSet Earnings Insight” historical series”, wondered whether estimate trends/revisions are exploitable. To investigate, we extract explicit S&P 500 quarterly year-over-year earnings growth estimates as recorded in this series. These data are bottom-up (firm by firm) aggregates, whether purely from analyst estimates (before any actual earnings releases), or a blend of actual earnings and estimates (during the relevant earnings season). There is a lag of about a week between the date of the FactSet report and the date the report appears in the list. Using these data and contemporaneous weekly levels of the S&P 500 Index during April 2011 through January 2016, we find that: Keep Reading

Profitability Metric Horse Race for Stocks and Sectors

Which measure of past firm profitability is most effective for forming U.S. stock and equity sector portfolios? In their October 2015 paper entitled “Portfolio Allocations Using Fundamental Ratios: Are Profitability Measures Effective in Selecting Firms and Sectors?”, John Hughen and Jack Strauss examine portfolio strategies based on four sector and firm profitability measures: gross profit, operating profit, EBITDA and an average (composite) of the three variables. They compare these portfolios to a buy-and-hold portfolio (S&P 500 Index stocks with equal sector weights) and portfolios form on cash flow, net income and book-to-market ratio. Their rankings of sectors and individual stocks include lags to ensure public availability of profit measures. Using quarterly returns and accounting data for S&P 500 Index stocks and ten associated sectors during January 1975 through April 2014 (with out-of-sample tests commencing January 1980), they find that: Keep Reading

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for June 2016 (Final)

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The asset with the highest allocation is the holding of the Best Value strategy.
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