Fundamental Valuation

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

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

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

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

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

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

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

Is there a way to suppress the volatility and drawdowns of a mixed value and momentum stock strategy while retaining most of its benefit? In his September 2015 paper entitled “Learning to Play Offense and Defense: Combining Value and Momentum from the Bottom up, and the Top Down”, Mebane Faber examines the feasibility of a strategy that combines market valuation and market trend timing (defense) with a mixed value and momentum stock selection strategy (offense). Specifically:

For offense, he each month: (1) ranks stocks by each of price-to-earnings, price-to-book and earnings before interest and taxes-to-total enterprise value ratios and then re-ranks them by the average of the three separate value rankings; (2) ranks stocks by each of 3-month, 6-month and 12-month past returns and then re-ranks them by the average of the three separate momentum rankings; and, (3) forms an equally weighted portfolio of the top 100 value and top 100 momentum stocks and holds for three months (three overlapping portfolios).

For defense, he each month: (1) hedges half of the portfolio by shorting the S&P 500 Index if the long-term real earnings yield for the S&P 500 (inverse of the Cyclically Adjusted Price-Earnings ratio, CAPE or P/E10 as calculated by Robert Shiller, minus the most recently available actual 12-month U.S. inflation rate) is in the 20% of its lowest inception-to-date monthly values; and, (2) hedges half of the portfolio by shorting the S&P 500 Index if the index is below its 12-month simple moving average. 

The overall portfolio can therefore be 100% long “offense” stocks, 50% hedged or market neutral. He does not account for costs of portfolio reformations or hedging. Using monthly total returns for all NYSE stocks in the top 60% of market capitalizations, monthly levels of the S&P 500 Total Return Index and monthly values of CAPE during 1964 through 2014, he finds that: Keep Reading