Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for April 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for April 2021 (Final)
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Equity Premium

Governments are largely insulated from market forces. Companies are not. Investments in stocks therefore carry substantial risk in comparison with holdings of government bonds, notes or bills. The marketplace presumably rewards risk with extra return. How much of a return premium should investors in equities expect? These blog entries examine the equity risk premium as a return benchmark for equity investors.

Impacts of Frictions on Factor Models of Stock Returns

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. FF5C – FF5 with a cash flow-based profitability factor.
  6. FF6C – 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

Re-examining Equity Factor Research Replicability

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

Factor Model of Stock Returns Based on Who Owns the Stocks

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:

Keep Reading

New Subclass of Retail Investors?

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:

Keep Reading

Disproportionate Influence of Retail Investors?

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

Alternative Yield Discount (Inflation) Rates

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

Only One Way to Win?

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

Simple Sector ETF Momentum Strategy Update/Extension

“Simple Sector ETF Momentum Strategy” investigates performances of simple momentum trading strategies for the following nine sector exchange-traded funds (ETF) executed with Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Here, we update the principal strategy and extend it by adding equally weighted combinations of the top two and top three sector ETFs, along with corresponding robustness tests and benchmarks. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly dividend-adjusted closing prices for the sector ETFs and SPDR S&P 500 (SPY), 3-month U.S. Treasury bill (T-bill) yield and S&P 500 Index level during December 1998 through December 2020, we find that: Keep Reading

Valuation-based Stock Market Return Expectations

What performance should investors expect from the S&P 500 Index based on price-to-earnings (P/E) and Cyclically-Adjusted Price-to-Earnings (CAPE, or P/E10)? In their November 2020 paper entitled “Extreme Valuations and Future Returns of the S&P 500”, Shaun Rowles and Andrew Mitchell take a layered “regression upon a regression” approach to predict S&P 500 Index returns and level. First, to estimate future returns, they run a linear regression on P/E, P/E10, S&P 500 dividend yield, inflation, 10-year U.S. Treasury note yield, historical 1-year, 3-year, 5-year and 10-year S&P 500 Index returns and percentiles of many of these variables within their respective historical distributions. Then, they run separate linear regressions to predict 1-year, 3-year, 5-year and 10-year future annualized returns. Finally, they run a linear regression to model current S&P 500 Index level for comparison to actual current level. Using Robert Shiller’s U.S. stock market and economic data spanning January 1871 through June 2020, they find that: Keep Reading

Seasonal Timing of Monthly Investment Increments

A subscriber requested evaluation of three retirement investment alternatives, assuming a constant increment invested at the end of each month, as follows:

  1. 50-50: allocate each increment via fixed percentages to stocks and bonds (for comparability, we use 50% to each).
  2. Seasonal 1: during April through September (October through March), allocate 100% of each increment to stocks (bonds).
  3. Seasonal 2: during April through September (October through March), allocate 100% of each increment to bonds (stocks).

The hypothesis is that seasonal variation in asset class allocations could improve overall long-term investment performance. We conduct a short-term test using SPDR S&P 500 ETF Trust (SPY) as a proxy for stocks and iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) as a proxy for bonds. We then conduct a long-term test using Vanguard 500 Index Fund Investor Shares (VFINX) as a proxy for stocks and Vanguard Long-Term Investment-Grade Fund Investor Shares (VWESX) as a proxy for bonds. Based on the setup, we focus on terminal value as the essential performance metric. Using total (dividend-adjusted) returns for SPY and LQD since July 2002 and for VFINX and VWESX since January 1980, all through December 2020, we find that: Keep Reading

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