Value Premium

Does the finding from long-short factor analysis that the value premium is stronger among small stocks than large stocks hold for long-only value portfolios? In his April 2022 paper entitled “Long-Only Value Investing: Does Size Matter?”, Jack Vogel investigates interactions between the value premium and market capitalization for U.S. and international stocks. The steps in his main analysis are to each year on June 30:

• Group the 3,000 largest U.S. stocks by market capitalization with non-zero market value of equity into the 1,000 largest firms (large-cap) and the 2,000 smallest (small-cap).
• Rank each group into thirds (terciles), fifths (quintiles) or tenths (deciles) based on each of: (1) book-to-market ratio (B/M); (2) earnings-to-price ratio (E/P); (3) free cash flow-to-price ratio (FCF/P); (4) earnings before interest and taxes-to-total enterprise value ratio (EBIT/TEV); and, (5) the composite rank of these four ratios.
• Measure average monthly returns over the next year of the top ranks based on either equal weights (EW) or value weights (VW).

Using the specified accounting data and stock prices for a broad sample of U.S. firms since July 1973 and for a comparable sample of international developed market firms since January 1994, all through December 2020, he finds that: Keep Reading

Do widely accepted equity factor premiums exist in data older than generally employed in academic studies? In their November 2021 paper entitled “The Cross-Section of Stock Returns before 1926 (And Beyond)”, Guido Baltussen, Bart van Vliet and Pim van Vliet look for some of the most widely accepted factor premiums in a newly assembled sample of U.S. stocks spanning January 1866 through December 1926 (61 years of additional and independent data). Specifically, they look at: size as measured by market capitalization; value as measured by dividend yield (strongly associated with earnings during the sample period); stock price momentum from 12 months ago to one month ago; short-term (1-month) return reversal; and, risk as measured by market beta. They use only those stocks which trade frequently and apply liquidity/data quality filters. To measure factor premiums, they each month for each factor:

• Regress next-month stock return versus stock factor value and compute slopes of the relationship.
• Reform a value-weighted hedge portfolio that is long (short) stocks with high (low) expected returns based on factor values to measure: (1) average factor portfolio gross return; and, (2) gross factor (CAPM) alphas and betas based on regression of factor portfolio excess return versus market excess return.

They further investigate economic explanations of factor premiums and test machine learning methods found successful with recent data. Using monthly prices, dividends and market capitalizations for 1,488 stocks in the new database, they find that:

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Trading Calendar presents full-year and monthly cumulative performance profiles for the overall stock market (S&P 500 Index) based on its average daily behavior. How much do the corresponding monthly behaviors of the various size and value/growth styles deviate from an overall equity market profile? To investigate, we consider the the following six exchange-traded funds (ETF) that cut across capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

Using monthly dividend-adjusted closing prices for the style ETFs and SPDR S&P 500 (SPY) during August 2001 through October 2021 (limited by data for IWS/IWP), we find that: Keep Reading

What is the best way to understand and anticipate variations in equity factor returns? Past research emphasizes factor return connections to business cycle variables or measures of investor sentiment (with little success). In his September 2021 paper entitled “The Quant Cycle”, David Blitz analyzes factor returns themselves to understand their variations, arguing that behavioral rather than economic forces drive them. He determines the quant cycle (bull and bear trends in factor returns) by qualitatively identifying peaks and troughs. He focuses on U.S. versions of four conventionally defined long-short factors frequently targeted by investors (value, quality, momentum and low-risk), emphasizing the most volatile (value and momentum). He also considers some alternative factors. Using monthly data for factors from the online data libraries of Kenneth French, Robeco and AQR spanning July 1963 through December 2020 (and for a reduced set of factors spanning January 1929 through June 1963), he finds that:

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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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Does the Fama-French  5-factor model (market, size, book-to-market, profitability, investment) of stock returns work for stocks worldwide? In their May 2021 paper entitled “Size, Value, Profitability, and Investment Effects in International Stock Returns: Are They Really There?”, Nusret Cakici and Adam Zaremba test the performance of the 5-factor model in global developed markets. They consider big and small stocks separately. They consider four regions (North America, Europe, Japan and Asia-Pacific), as well as the global market. They lag all accounting data by six months and calculate returns in U.S. dollars. Using data in U.S. dollars for 65,000 stocks from 23 countries during December 1987 through March 2019 (with tests starting July 1990), they find that:

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Is firm book value-to-market price ratio (B/M) obsolete due to growing importance of intangible assets? Is it still widely used by institutional investors? In their May 2021 paper entitled “Going by the Book: Valuation Ratios and Stock Returns”, Ki-Soon Choi, Eric So and Charles Wang examine continuing of B/M for value investing and implications of such use for stock returns and trading. They compare its effectiveness to: sales-to-price; gross profit-to-price; net shareholder payout-to-price; and, a composite of these three alternatives (COMP). They focus on firms with significant deviations between B/M and the other ratios to assess how investors price and trade stocks with conflicting value signals. Specifically, they each year at the end of June:

• Calculate each ratio for each firm.
• Rank firms into fifths (quintiles) based on each ratio.
• Calculate the absolute difference in quintile between B/M ranking and ranking based on other ratios (RatioSpread).

High values of RatioSpread indicate firms for which B/M disagrees with other ratios regarding whether associated stocks are value or glamor (growth). Using firm fundamentals and stock trading data for a broad sample of U.S. stocks with share price over $5 during 1980 through 2017, they find that: Keep Reading

Can investors detect when commodity futures momentum, value and carry (basis) strategies are crowded and therefore likely to generate relatively weak returns? In the March 2021 version of their paper entitled “Crowding and Factor Returns”, Wenjin Kang, Geert Rouwenhorst and Ke Tang examine how crowding by commodity futures traders affects expected returns for momentum, value and basis strategies. They define commodity-level crowding based on excess speculative pressure, measured for each commodity as the deviation of non-commercial trader net position (long minus short) from its 3-year average, scaled by open interest. They calculate crowding for a long-short strategy portfolio as the average of commodity-level crowding metrics of long positions minus the average of commodity-level crowding metrics for short positions, divided by two. They specify strategy portfolios as follows:

• Momentum – each week long (short) the equally weighted 13 commodities with the highest (lowest) past 1-year returns as of the prior week.
• Value – each week long (short) the equally weighted 13 commodities with the highest (lowest) ratios of last-week nearest futures price to nearest futures price three years ago.
• Basis – each week long (short) the equally weighted 13 commodities with the highest (lowest) basis, measured as percentage price difference between nearest and next maturity contracts as of the prior week.

For each strategy, they measure effects of crowding by measuring returns separately when strategy crowding is above or below its rolling 3-year average. Using weekly (Tuesday close) investor position data published by the Commodity Futures Trading Commission (CFTC) for 26 commodities traded on North American exchanges during January 1993 through December 2019, they find that:

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Value investing performance over the past two decades is poor. Is this underperformance a temporary consequence of an unusual macro environment, or a reflection of permanent economic/equity market changes. In their February 2021 paper entitled “Value Investing: Requiem, Rebirth or Reincarnation?”, Bradford Cornell and Aswath Damodaran survey the history and alternative approaches to value investing, with focus on its failure in recent decades. They then discuss how value investing must adapt to recover. Based on the body of value investing research through 2020, they conclude that: Keep Reading

Why have so many quantitative funds performed poorly in recent years? In his January 2021 paper entitled “The Quant Crisis of 2018-2020: Cornered by Big Growth”, David Blitz examines in detail recent (June 2018 through August 2020) performance of stock portfolios constructed from five widely accepted long-short factors:

1. Size – Small Minus Big (SMB) market capitalizations.
2. Value – High Minus Low (HML) book-to-market ratios.
3. Investment – Conservative Minus Aggressive (CMA).
4. Profitability – Robust Minus Weak (RMW).
5. Momentum – Winners Minus Losers (WML).

Using factor returns from the Kenneth French data library and additional firm/stock data for developed and U.S. markets to construct alternative factor performance tests from various start dates through August 2020, he finds that: Keep Reading