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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.

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Bringing Order to the Factor Zoo?

From a purely statistical perspective, how many factors are optimal for explaining both time series and cross-sectional variations in stock anomaly/stock returns, and how do these statistical factors relate to stock/firm characteristics? In their July 2018 paper entitled “Factors That Fit the Time Series and Cross-Section of Stock Returns”, Martin Lettau and Markus Pelger search for the optimal set of equity factors via a generalized Principal Component Analysis (PCA) that includes a penalty on return prediction errors returns. They apply this approach to three datasets:

  1. Monthly returns during July 1963 through December 2017 for two sets of 25 portfolios formed by double sorting into fifths (quintiles) first on size and then on either accruals or short-term reversal.
  2. Monthly returns during July 1963 through December 2017 for 370 portfolios formed by sorting into tenths (deciles) for each of 37 stock/firm characteristics.
  3. Monthly excess returns for 270 individual stocks that are at some time components of the S&P 500 Index during January 1972 through December 2014.

They compare performance of their generalized PCA to that of conventional PCA. Using the specified datasets, they find that: Keep Reading

Avoiding Negative Stock Market Returns

Is there an exploitable way to predict when short-term stock market return will be negative? In his June 2018 paper entitled “Predictable Downturns”, Carter Davis tests a random forest regression-based forecasting model to predict next-day U.S. stock market downturns. He uses the value-weighted return of a portfolio of the 10 U.S. stocks with the largest market capitalizations at the end of the prior year minus the U.S. Treasury bill (T-bill) yield as a proxy for excess market return. He employs a two-step test process:

  1. Use a rolling 10-year historical window of 143 input variables (economic, equity factor, market volatility, stock trading, calendar) to find when the probability of negative portfolio daily excess return is at least 55%.
  2. Calculate whether the average portfolio gross excess return of all such days is in fact significantly less than zero.

He corrects for data snooping bias associated with the modeling approach. He further investigates which input variables are most important and tests a market timing strategy that holds the 10-stock portfolio (T-bills) when predicted portfolio return is negative (non-negative) as specified above. Using data for the input variables and returns for test portfolio stocks during July 1926 through July 2017, he finds that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and annual returns and volatilities. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through June 2018, we find that:

Keep Reading

Excluding Bad Stock Factor Exposures

The many factor-based indexes and exchange-traded funds (ETFs) that track them now available enable investors to construct multi-factor portfolios piecemeal. Is such piecemeal construction suboptimal? In their July 2018 paper entitled “The Characteristics of Factor Investing”, David Blitz and Milan Vidojevic apply a multi-factor expected return linear regression model to explore behaviors of long-only factor portfolios. They consider six factors: value-weighted market, size, book-to-market ratio, momentum, operating profitability and investment(change in assets). Their model generates expected returns for each stock each month, and further aggregates individual stock expectations into factor-portfolio expectations holding all other factors constant. They use the model to assess performance differences between a group of long-only single-factor portfolios and an integrated multi-factor portfolio of stocks based on combined rankings across factors. The focus on gross monthly excess (relative to the 10-year U.S. Treasury note yield) returns as a performance metric. Using data for a broad sample of U.S. common stocks among the top 80% of NYSE market capitalizations and priced at least $1 during June 1963 through December 2017, they find that: Keep Reading

Are Low Volatility Stock ETFs Working?

Are low volatility stock strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider eight of the largest low volatility ETFs, all currently available, in order of longest to shortest available histories:

We focus on monthly return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly returns for the low volatility stock ETFs and their benchmark ETFs as available through June 2018, we find that: Keep Reading

T-bills Beat Most Stocks?

Does conventional reward-for-risk wisdom about the long-run performance of the U.S. stock market translate to the typical stock? In the May 2018 update of his paper entitled “Do Stocks Outperform Treasury Bills?”, Hendrik Bessembinder compares the performance of the typical U.S. stock to that of the 1-month U.S. Treasury bill (T-bill) over monthly, annual, decade and life-of-stock horizons. He also performs simulations to gauge the effectiveness of holding just one stock and of diversifying across portfolios of five, 25, 50 and 100 stocks. Using monthly total (dividend-reinvested) returns for 25,967 U.S. common stocks while listed during July 1926 through December 2016, he finds that: Keep Reading

Better Five-factor Model of Stock Returns?

Which factor models of stock returns are currently best? In their June 2018 paper entitled “q5,  Kewei Hou, Haitao Mo, Chen Xue and Lu Zhang, introduce the q5 model of stock returns, which adds a fifth factor (expected growth) to the previously developed q-factor model (market, size, asset growth, return on equity). They measure expected growth as 1-year, 2-year and 3-year ahead changes in investment-to-assets (this year total assets minus last year total assets, divided by last year total assets) as forecasted monthly via predictive regressions. They define an expected growth factor as average value-weighted returns for top 30% 1-year expected growth minus bottom 30% 1-year expected growth, calculated separately and further averaged for big and small stocks. They examine expected growth as a standalone factor and then conduct an empirical horse race of recently proposed 4-factor, 5-factor (including q5) and 6-factor models of stock returns based on their abilities to explain average return differences for value-weighted extreme tenth (decile) portfolios for 158 significant anomalies. Using monthly return and accounting data for a broad sample of non-financial U.S. common stocks during July 1963–December 2016, they find that:

Keep Reading

Doubling Down on Size

“Is There Really an Size Effect?” summarizes research challenging the materiality of the equity size effect. Is there a counter? In their June 2018 paper entitled “It Has Been Very Easy to Beat the S&P500 in 2000-2018. Several Examples”, Pablo Fernandez and Pablo Acin double down on the size effect via a combination of market capitalization thresholds and equal weighting. Specifically, they compare values of a $100 initial investment at the beginning of January 2000, held through April 2018, in:

  • The market capitalization-weighted (MW) S&P 500.
  • The equally weighted (EW) 20, 40, 60 and 80 of the smallest stocks in the S&P 1500, reformed either every 12 months or every 24 months.

All portfolios are dividend-reinvested. Their objective is to provide investors with facts to aid portfolio analysis and selection of investment criteria. Using returns for the specified stocks over the selected sample period, they find that:

Keep Reading

Unemployment Rate and Stock Market Returns

The financial media and expert commentators sometimes cite the U.S. unemployment rate as an indicator of economic and stock market health, generally interpreting a jump (drop) in the unemployment rate as bad (good) for stocks. Conversely, investors may interpret a falling unemployment rate as a trigger for increases in the Federal Reserve target interest rate (and adverse stock market reactions). Is this indicator in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using the monthly unemployment rate from the U.S. Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index data for the period January 1950 through April 2018 (820 months), we find that: Keep Reading

Employment and Stock Market Returns

U.S. job gains or losses are a prominent element of the monthly investment-related news cycle, with the financial media and expert commentators generally interpreting changes in employment as an indicator of future economic and stock market health. One line of reasoning is that jobs generate personal income, which spurs personal consumption, which boosts corporate earnings and lifts the stock market. Are employment trends in fact predictive of U.S. stock market behavior in subsequent months, quarters and years? Using monthly seasonally adjusted nonfarm employment data from the U.S. Bureau of Labor Statistics (BLS) and contemporaneous S&P 500 Index data for the period January 1950 through April 2018 (820 months), we find that: Keep Reading

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