Equity Premium

Are stock and sector momentum strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight momentum-oriented equity ETFs, all currently available, in order of longest to shortest available histories:

• Invesco DWA Momentum Portfolio (PDP) – invests at least 90% of assets in approximately 100 U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly. Benchmark is iShares Russell 3000 (IWV).
• Invesco DWA SmallCap Momentum (DWAS) – invests at least 90% of assets in approximately 200 small-capitalization U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly. Benchmark is iShares Russell 2000 (IWM).
• iShares Edge MSCI USA Momentum Factor (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares Russell 1000 (IWB).
• First Trust Dorsey Wright Focus 5 (FV) – holds five equally weighted sector and industry ETFs selected via a proprietary relative strength methodology, reformed twice a month. Benchmark is IWV.
• iShares MSCI Intl Momentum Factor (IMTM) – holds international developed market large-capitalization and mid-capitalization stocks with relatively high momentum. Benchmark is iShares MSCI ACWI ex U.S. (ACWX).
• SPDR Russell 1000 Momentum Focus (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed. Benchmark is IWB.
• First Trust Dorsey Wright Dynamic Focus 5 (FVC) – similar to FV but with added risk management via an increasing allocation to cash equivalents when relative strengths of more than one-third of the universe diminish relative to a cash index, reformed twice a month. Benchmark is IWV.
• JPMorgan U.S. Momentum Factor (JMOM) – holds U.S. common stocks with strong risk-adjusted momentum. Benchmark is IWB.

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). We assign benchmark ETFs according to momentum fund descriptions. Using monthly returns for the eight momentum funds and respective benchmarks as available through April 2021, we find that: Keep Reading

What are the different ways of estimating the equity risk premium, and which one is best? In his March 2021 paper entitled “Equity Risk Premiums (ERP): Determinants, Estimation and Implications – The 2021 Edition”, Aswath Damodaran updates a comprehensive overview of equity risk premium estimation and application. He examines why different approaches to estimating the premium disagree and how to choose among them. Using data from multiple countries (but focusing on the U.S.) over long periods through the end of 2020, he concludes that: Keep Reading

Are exchange-traded funds (ETF) focused on Initial Public Offerings of stocks (IPO) attractive? To investigate, we consider three of the largest IPO ETFs and one recent Special Purpose Acquisition Company (SPAC) ETF, all currently available with moderate trading volumes, in order of longest to shortest available histories:

• First Trust US Equity Opportunities ETF (FPX) – seeks gross investment results that track price and yield of the IPOX-100 U.S. Index.
• Renaissance IPO ETF (IPO) – reflects approximately the top 80% of new public firms weighted by free float capitalization with a 10% cap for any one position. Large IPOs enter quickly and others enter during quarterly reviews, while all exit two years after initial trade date.
• First Trust International Equity Opportunities ETF (FPXI) –  seeks gross investment results that track price and yield of the IPOX International Index.
• Defiance Next Gen SPAC Derived ETF (SPAK) – 60% weight to IPOs derived from SPACs and 40% weight to common stock of newly listed SPACs, excluding warrants.

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). For all these ETFs, we use SPDR S&P 500 (SPY) as the benchmark. Using monthly returns for the IPO ETFs and SPY as available through March 2021, we find that:

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Should investors consider adding Special Purpose Acquisition Company (SPAC) Initial Public Offerings (IPO), which boomed in 2020, to their portfolios? In the March 2021 revision of their paper entitled “SPACs”, Minmo Gahng, Jay Ritter and Donghang Zhang examine investor returns on SPACs during the two phases of their lifecycle:

1. SPAC phase– from SPAC IPO to five trading days before completion of target business merger or, if no merger within 24 months, liquidation. To measure investor returns for this phase, they assume purchases of one SPAC unit (usually a common share and warrants) at IPO prices for 114 SPACs during January 2010 through May 2018. The investor sells each component at the higher of the market price or the redemption price.
2. deSPAC phase – from the first trading day as a merged company. For this phase, there are 114 completed mergers (105 having warrants), including 97 from the above SPAC phase plus 17 additional SPACs.

Using price data for U.S. SPACs (not those traded in Over-The-Counter markets) during January 2010 through October 2020, they find that:

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How much does accounting for equity factor portfolio maintenance frictions affect usefulness of factor models of stock returns. In their March 2021 paper entitled “Model Selection with Transaction Costs”, Andrew Detzel, Robert Novy-Marx and Mihail Velikov examine effects of transaction costs on six leading models of stock returns:

1. FF5 – Fama-French 5-factor model (market, size, book-to-market , investment and accruals-based profitability, reformed annually).
2. FF6 – FF5 plus a momentum factor.
3. HXZ4 – Hou, Xue, and Zhang 4-factor model (market, size, investment and profitability, all reformed monthly).
4. BS6 – Barillas-Shanken 6-factor model (market, size, book-to-market reformed monthly, investment, return on equity and momentum).
5. FF5_C – FF5 with a cash flow-based profitability factor.
6. FF6_C – FF6 with a cash flow-based profitability factor.

They compare model effectiveness based on maximum squared Sharpe ratio (SR²), which measures how closely a model approaches the in-sample efficient frontier for all test assets. They measure transaction costs using stock-level effective bid-ask spread. Using data to calculate all factors employed by the six models and effective spreads during January 1972 through December 2017, they find that: Keep Reading

Several recent papers find that most studies identifying factors that predict stock returns are not replicable or derive from snooping of many factors. Is there a good counter-argument? In their January 2021 paper entitled “Is There a Replication Crisis in Finance?”, Theis Ingerslev Jensen, Bryan Kelly and Lasse Pedersen apply a Bayesian model of factor replication to a set of 153 factors applied to stocks across 93 countries. For each factor in each country, they each month:

1. Sort stocks into thirds (top/middle/bottom) with breakpoints based on non-micro stocks in that country.
2. For each third, compute a “capped value weight” gross return (winsorizing market equity at the NYSE 80th percentile to ensure that tiny stocks have tiny weights no mega-stock dominates).
3. Calculate the gross return for a hedge portfolio that is long (short) the third with the highest (lowest) expected return.
4. Calculate the corresponding 1-factor gross alpha via simple regression versus the country portfolio.

They further propose a taxonomy that systematically assigns each of the 153 factors to one of 13 themes based on high within-theme return correlations and conceptual similarities. Using firm and stock data required to calculate the specified factors starting 1926 for U.S. stocks and 1986 for most developed countries (in U.S. dollars), and 1-month U.S. Treasury bill yields to compute excess returns, all through 2019, they find that: Keep Reading

Is following the lead of certain types of equity investors as effective as using widely accepted factor models of stock returns? In their March 2021 paper entitled “What Do the Portfolios of Individual Investors Reveal About the Cross-Section of Equity Returns?”, Sebastien Betermier, Laurent Calvet, Samuli Knüpfer and Jens Kvaerner construct a factor model of stocks returns based on demographics of the individual investors who own them. They construct investor factors by each year reforming portfolios that are long (short) the 30% of stocks with the highest (lowest) expected returns based on holdings-weighted investor demographics and then measuring returns of these hedge portfolios the following year. They compare these investor factors to conventional factors constructed from firm/stock characteristics. Using anonymized demographics and direct stock holdings of Norwegian investors (an average 365,000 per year), and associated firm/stock characteristics and returns (over 400 stocks listed on the Oslo Stock Exchange), during 1997 through 2018, they find that:

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How has the market environment changed with the introduction of zero-commission trading and associated interest in trading among many inexperienced users? In their January 2021 paper entitled “Zero-Commission Individual Investors, High Frequency Traders, and Stock Market Quality”, Gregory Eaton, Clifton Green, Brian Roseman and Yanbin Wu examine market implications of growth in trading by a new subclass of retail investors represented by Robinhood users, focusing on January 2020 through August 2020 when the number of Robinhood users becomes very large. They isolate Robinhood user impacts by comparing market behaviors during Robinhood outages (real-time complaints by at least 200 Robinhood users on DownDetector.com) to those during similar times of day the prior week. They rely on the Reddit WallStreetBets forum and lagged trading activity to identify which stocks Robinhood users would have traded during outages. Using hourly (normal market hours) breadth of stock ownership data for Robinhood users from Robintrack (stocks with minimum average ownership 500 and daily minimum owners 50) and associated stock trading data during July 2018 through August 2020 (when the RobinTrack dataset ends), they find that:

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How can the retail trader tail wag the market dog? In their February 2021 paper entitled “The Equity Market Implications of the Retail Investment Boom”, Philippe van der Beck and Coralie Jaunin quantify impacts of the Robinhood-catalyzed retail trading boom on the U.S. stock market. They focus on the early part of the COVID-19 pandemic, during which retail trading soars and institutional investors rebalance their portfolios. They approximate retail trading based on account holdings data from RobinTrack and institutional rebalancing based on SEC Form 13F filings. Using RobinTrack account U.S. common stock holdings data as available through the first half of 2020 (discontinued August 2020) and institutional common stock holdings as disclosed in 13F filings during January 2005 through June 2020, they find that: Keep Reading

Investors arguably expect that investments generate returns in excess of the inflation rate. Do different measures of the inflation rate indicate materially different yield discounts? To investigate, we relate 12-month trailing S&P 500 annual operating earnings yield (E/P), S&P 500 12-month trailing annual dividend yield, 10-year U.S. Treasury note (T-note) yield and 3-month U.S. Treasury bill (T-bill) yield to four measures of annual U.S. inflation rate:

1. Non-seasonally adjusted inflation rate based on the total Consumer Price Index (CPI) from the Bureau of Labor Statistics (retroactive revisions of seasonal adjustments interfere with historical analysis).
2. Non-seasonally adjusted inflation rate based on core CPI from the Bureau of Labor Statistics.
3. Inflation rate based on the Personal Consumption Expenditures: Chain-type Price Index (PCE) from the Federal Reserve Bank of St. Louis.
4. Trimmed mean PCE from the Federal Reserve Bank of Dallas.

Using monthly data for all variables during March 1989 (limited by earnings data) through December 2020, we find that… Keep Reading