Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for September 2022 (Final)

Momentum Investing Strategy (Strategy Overview)

Allocations for September 2022 (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.

Finding Stocks with Persistent Momentum

Can investors improve the performance of stock momentum portfolios by isolating stocks that “hold” their momentum? In their April 2022 paper entitled “Enduring Momentum”, Hui Zeng, Ben Marshall, Nhut Nguyen and Nuttawat Visaltanachoti exploit firm characteristics to identify stocks that continue to be winners or losers after selection as momentum stocks (stocks with enduring momentum). They measure momentum by each month ranking stocks into equal-weighted tenths, or deciles, based on past 6-month returns, with the top (bottom) decile designated winners (losers). They then develop a model that uses information from 37 firm characteristics to estimate each month the probability that each winner or loser stock will continue as a winner or loser during each of the next six months. They verify that the model reasonably predicts momentum persistence and proceed to test the economic value of the predictions by each month reforming an enduring momentum hedge portfolio that is long (short) the 10 equal-weighted winner (loser) stocks with the highest probabilities of remaining winners (losers) and holding the portfolio for six months. They compare the performance of this portfolio to that of a conventional momentum portfolio that is each month long the entire winner decile and short the entire loser decile, also held for six months. Using returns for a broad sample of U.S. common stocks priced over $1.00 and 37 associated firm characteristics during January 1980 through December 2018, they find that: Keep Reading

SACEVS with SMA Filter

The “Simple Asset Class ETF Value Strategy” (SACEVS) allocates across 3-month Treasury bills (Cash, or T-bill), iShares 20+ Year Treasury Bond (TLT), iShares iBoxx $ Investment Grade Corporate Bond (LQD) and SPDR S&P 500 (SPY) according to the relative valuations of term, credit and equity risk premiums. Does applying a simple moving average (SMA) filter to SACEVS allocations improve its performance? Since many technical traders use a 10-month SMA (SMA10), we apply SMA10 filters to dividend-adjusted prices of TLT, LQD and SPY allocations. If an allocated asset is above (below) its SMA10, we allocate as specified (to Cash). This rule does not apply to any Cash allocation. We focus on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios (using average monthly T-bill yield during a year as the risk-free rate for that year) of SACEVS Best Value and SACEVS Weighted portfolios. We compare to baseline SACEVS as currently tracked and to the SMA rule applied to a 60%-40% monthly rebalanced SPY-TLT benchmark portfolio (60-40). Finally, we test sensitivity of main findings to varying the SMA lookback interval. Using SACEVS historical data, monthly dividend-adjusted closing prices for the asset class proxies and yield for Cash during July 2002 (the earliest all funds are available) through March 2022, we find that:

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Stock Market Earnings Yield and Inflation Over the Long Run

How does the U.S. stock market earnings yield (inverse of price-to-earnings ratio, or E/P) interact with the U.S. inflation rate over the long run? Is any such interaction exploitable? To investigate, we employ the long run dataset of Robert Shiller. Using monthly data for the S&P Composite Stock Index, estimated aggregate trailing 12-month earnings and dividends for the stocks in this index, and estimated U.S. Consumer Price Index (CPI) during January 1871 through February 2022 (over 151 years), and estimated monthly yield on 1-year U.S. Treasury bills (T-bills) since January 1951, we find that:

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Climate Solutions Stocks

Are firms offering products and services purported to mitigate climate change compelling investments? In the February 2022 revision of their paper entitled “Climate Solutions Investments”, Alexander Cheema-Fox, George Serafeim and Hui Wang analyze international reports, regional net zero frameworks, research papers and news to develop a list of 164 key words/phrases associated with climate change solution business areas. They apply these key words/phrases to firm descriptions to identify 632 actively traded pure plays in climate solutions. They then characterize geographies, accounting fundamentals and valuation ratios for this sample and construct monthly rebalanced value-weighted and equal-weighted climate solutions portfolios (CSP). Using monthly firm fundamentals and stock trading data for these 632 firms from the end of 2010 through October 2021, they find that: Keep Reading

Consumer Credit and Consumer Discretionary Sector Returns

“Consumer Credit and Stock Returns” finds that expansion (contraction) of consumer credit, available monthly from the Federal reserve with a delay of about five weeks, has little or no power to predict overall stock market returns. Might consumer credit be useful in predicting returns for just the consumer discretionary sector, as proxied by Consumer Discretionary Select Sector SPDR Fund (XLY)? Using monthly seasonally adjusted total U.S. consumer credit and monthly dividend-adjusted prices for XLY as available during December 1998 (inception of XLY) through January 2022, we find that: Keep Reading

Luxury Goods Stock Premium

Do stocks of firms focused on luxury goods outperform those of more prosaic companies? In his June 2019 paper entitled “Demand-Driven Risk and the Cross-Section of Expected Returns”, Alejandro Lopez-Lira examines aggregate performance of firms selling goods with high income elasticity (luxury goods), assuming that such firms are particularly exposed to demand-driven risk (consumption shocks). Hypothesizing that advertising, customer support and new feature development costs are relatively high for such firms, he proposes three accounting-based measures of demand-driven risk exposure:

  1. Indirect cost ratio (selling, general and administrative expenses, divided by cost of goods sold plus selling, general and administrative expenses).
  2. Indirect costs-to-net sales ratio.
  3. One minus the direct costs-to-net sales ratio.

He excludes financial, utilities, mining, petroleum refining and pharmaceuticals firms from analysis due to their insulation from consumer demand. Each June, he ranks remaining firms into fifths (quintiles) based on their indirect cost ratios, with the highest quintile most exposed to demand-driven risk. He then tracks monthly returns of the value-weighted quintiles over the next year. He further investigates interactions of demand-driven risk with competitive pressure, measuring the latter via textual analysis of Form 10-K submittals to gauge competitor product similarities/sales. Using annual accounting data, monthly stock prices and annual Form 10-Ks for the specified firms and contemporaneous monthly factor model returns as available during January 1962 through December 2016, he finds that:

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Accounting for Past Return to ESG Stocks

Does past performance of Environmental, Social, and Corporate Governance (ESG) stocks derive mostly from shift in demand from other stocks to ESG stocks? In his September 2021 paper entitled “Flow-Driven ESG Returns”, Philippe van der Beck examines whether flow of investor dollars toward ESG mutual funds explains aggregate performance of ESG stocks, as follows:

  • Construct an ESG portfolio that aggregates quarterly holdings of U.S. equity mutual funds that assert sustainability mandates.
  • Measure perceived sustainability of each stock by calculating the deviation of its ESG portfolio weight from its market portfolio weight.
  • Estimate the price pressure due to a flow of dollars into ESG mutual funds.
  • Combine perceived stock sustainability and price pressure to explore sensitivity of past ESG portfolio returns to level of dollar flow into ESG mutual funds.

Using mutual fund descriptions (with respect to importance of sustainability in investment decisions) and quarterly Form 13F mutual fund holdings data during 2000 through 2020, and underlying stock prices through the first quarter of 2021, he finds that:

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Stock Index Earnings-Returns Lead-lag

A subscriber asked about the lead-lag relationship between S&P 500 earnings and S&P 500 Index returns. To investigate, we relate actual aggregate S&P 500 operating and as-reported earnings to S&P 500 Index returns at both quarterly and annual frequencies. Earnings forecasts are available well in advance of returns. Actual earnings releases for a quarter occur throughout the next quarter. Using quarterly S&P 500 earnings and index levels during March 1988 through June 2021 and September 2021, respectively, we find that: Keep Reading

Do High-dividend Stock ETFs Beat the Market?

A subscriber asked about current evidence that high-dividend stocks outperform the market. To investigate, from a practical perspective, we compare performances of five high-dividend stock exchange-traded funds (ETFs) with relatively long histories to that of SPDR S&P 500 (SPY) as a proxy for the U.S. stock market. The five high-dividend stock ETFs are:

  • iShares Select Dividend (DVY), with inception November 2003.
  • PowerShares Dividend Achievers ETF (PFM), with inception September 2005.
  • SPDR S&P Dividend ETF (SDY), with inception November 2005.
  • WisdomTree Dividend ex-Financials ETF (DTN), with inception June 2006.
  • Vanguard High Dividend Yield ETF (VYM), with inception November 2006.

For each of these ETFs, we compare average monthly total (dividend-reinvested) return, standard deviation of total monthly returns, monthly return-risk ratio (average monthly return divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) to those for SPY over matched sample periods. We also look at alphas and betas for the five ETFs based on simple regressions of monthly returns versus SPY returns. Using monthly total returns for the five high-dividend stock ETFs and SPY over available sample periods through September 2021, we find that:

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Examining Disruptive/Transformational Thematic Indexes

Leading index providers have introduced thematic stock indexes to address transformative macroeconomic, geopolitical or technological trends (for example, cybersecurity, robotics, autonomous vehicles and clean power). How do these indexes relate to standard asset pricing models? In his August 2021 paper entitled “Betting Against Quant: Examining the Factor Exposures of Thematic Indices”, David Blitz examines the performance characteristics of these indexes based on widely used factor models of stock returns and discusses why investors may follow these indexes via exchange-traded funds (ETF) despite unfavorable factor exposures. He considers 36 S&P indexes (narrower, equal-weighted) and 12 MSCI indexes (broader, capitalization-weighted) with at least three years of history. Using monthly returns for these 48 indexes and for components of the Fama-French 5-factor (market, size, book-to-market, profitability and investment) model and the momentum factor as available during June 2013 through April 2021, he finds that:

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