Objective research to aid investing decisions

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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.

Testing for Trends in Trending for U.S. Stocks and Bonds

“Market Impacts of Growth in Target Date Funds” summarizes research on potential market-wide effects of periodic rebalancing actions of Target Date Funds (TDF), which trade against momentum. One piece of evidence is that monthly autocorrelation of S&P 500 Index returns is significantly negative during 2010-2019 but not during 1986-1995 or 1996-2005. Another is that TDFs accomplish most of quarterly rebalancing within the next quarter. To assess how convincing autocorrelation findings are, we calculate rolling 5-year monthly (60-month) and quarterly (20-calendar quarter) autocorrelations of returns for:

Using monthly total (dividend-reinvested) returns for these three assets through October 2020, we find that: Keep Reading

Market Impacts of Growth in Target Date Funds

Are aggregate periodic stocks-bonds rebalancing actions of Target Date Funds (TDF), which trade against momentum, increasingly affecting U.S. stock market dynamics? In their October 2020 paper entitled “Retail Financial Innovation and Stock Market Dynamics: The Case of Target Date Funds”, flagged by a subscriber, Jonathan Parker, Antoinette Schoar and Yang Sun examine market impacts of Target Date Funds (TDFs), assets of which have grown from less than $8 billion in 2000 to more than $2.3 trillion (of roughly $21 trillion in U.S. mutual funds) in 2019. Using quarterly data on TDF holdings, monthly U.S. stock market and Vanguard Total Bond Market Index Fund (bond market) returns and monthly data for stocks held by and similar to those held by TDFs during the third quarter of 2008 through the fourth quarter of 2018 (excluding three quarters with suspect data), they find that:

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Three High-attention Earnings Announcement Clusters Drive Market?

Does the U.S. stock market respond predictably to simultaneous earnings announcements of attention-grabbing companies? In their September 2020 paper entitled “Famous Firms, Earnings Clusters, and the Stock Market”, Yixin Chen, Randolph Cohen and Zixuan Wang examine U.S. stock market (E-mini S&P 500 futures) responses to earnings announcement clusters (EAC) comprised of high-attention firms. They focus on the three most prominent pre-open (AM) and three most prominent post-close (PM) EACs in each of January, April, July and October, with each announcement weighted for prominence by associated total number of Dow Jones earnings news articles during the prior calendar year. Using earnings announcements and daily prices for S&P 500 components and minute-by-minute E-mini S&P 500 futures returns during 1999-2018, and associated earnings news articles during 1998-2018, they find that: Keep Reading

Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. We focus on monthly reward/risk (average monthly return divided by standard deviation of monthly returns) as a key performance metric. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for SACEMS assets since February 2006 and monthly S&P 500 Index level since September 2005, all through September 2020, we find that:

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Stocks for the Long Run Internationally

Are buy-and-hold stock market returns attractive over the long run globally? In their May 2020 paper entitled “Stocks for the Long Run? Evidence from a Broad Sample of Developed Markets”, Aizhan Anarkulova, Scott Cederburg and Michael O’Doherty apply a stationary block bootstrap procedure (retaining some time series features) to generate distributions of 1,000,000 each 1-month to 30-year real returns across global equity markets. They mitigate survivorship and easy data biases via broad coverage of developed countries and inclusion of market interruptions. They focus on a long-term (30-year) investment horizon, with returns accumulated in local currencies. Using monthly total (dividend-reinvested) equity index returns and consumer price indexes for 39 developed countries as available according to certain criteria during January 1841 through December 2019, they find that: Keep Reading

Behaviors and Characteristics of Top Stocks

What are typical return behaviors and firm characteristics of the best-performing and worst-performing U.S. stocks at a 10-year horizon? In his July 2020 series of papers entitled “Extreme Stock Market Performers”, Part I: Expect Some Drawdowns, Part II: Do Technology Stocks Dominate?, Part III: What are their Observable Characteristics? and Part IV: Can Observable Characteristics Forecast Outcomes?, Hendrik Bessembinder investigates returns and firm characteristics of stocks that generate the most and least total shareholder wealth (are the “best” and “worst” stocks) in each decade since 1950. Total shareholder wealth generation incorporates both cumulative return and market capitalization. Using monthly returns, market capitalizations and firm characteristics for U.S. stocks for each decade during 1950 through 2019, he finds that: Keep Reading

Best Stock Return Anomaly Double Sorts?

Are portfolios of U.S. stocks that are double-sorted to capture benefits of two complementary return anomalies attractive? In their July 2020 paper entitled “Interacting Anomalies”, Karsten Müller and Simon Schmickler test all possible double-sorted portfolios across 102 stock return anomalies (10,302 double-sorts). They employ 5×5 double-sorts, first ranking stocks into fifths (quintiles) for one anomaly and then re-sorting each of these quintiles into fifths for the second anomaly. They focus on the four “corner” portfolios involving the extreme high and low quintiles for both anomalies. They evaluate average returns, Sharpe ratios and factor model alphas of both equal-weighted (EW) and value-weighted (VW) versions of these portfolios, emphasizing performance gains from anomaly interactions. They correct for multiple hypothesis testing (data snooping bias) using the Bonferroni correction. Using trading and accounting data for a broad sample of U.S. common stocks with annual (quarterly) accounting data lagged by six (four) months during 1970 through 2017, they find that:

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Ending with the Beginning in Mind

How should investors think about the interactions between working years (retirement account contributions) and retirement years (retirement account withdrawals)? In his June 2020 paper entitled “Retirement Planning: From Z to A”, Javier Estrada integrates working and retirement periods to estimate how much an individual should save and how they should invest to achieve a desired retirement income and ultimate bequest to heirs. He illustrates his analytical solution empirically for U.S. stocks and bonds, first using a base case plus sensitivity analysis and then using Monte Carlo simulations. His base case assumes:

  • Work will last 40 years with a 60%/40% stocks/bonds retirement portfolio.
  • Retirement will last 30 years with beginning-of-year real (inflation-adjusted) withdrawals of $60,000 from a 40%/60% stocks/bonds retirement portfolio and ultimate bequest $300,000.

Using annual data for U.S. stocks (the S&P 500 Index total return), bonds (10-year U.S. Treasury notes) and U.S. inflation during 1928 through 2019, he finds that: Keep Reading

Representative Investor Returns on Stocks?

Most stock data sources present Total Return (TR), 100% reinvestment of dividends with no participation in firm rights issuances and share issuances/repurchases, as representative of investment performance. An alternative perspective is Total Return for All Shareholders (TRAS), the return for an investor who maintains a constant fraction of issued shares (see the table below). Can these two measures of returns to investors differ materially? In his May 2020 paper entitled “Total Return (TR) and Total Return for All Shareholders (TRAS). Difference for the Companies in the S&P 100”, Pablo Fernandez compares recent TR and TRAS for stocks in the S&P 100 as of April 2020 that have histories back to the end of 2004 (88 stocks). Using price, dividend, rights issuance and share issuance/repurchase data during December 2004 through April 2020, he finds that: Keep Reading

Pervasive Effects of Preference for Lottery Stocks

Is investor attraction to high-reward/high-risk (lottery) stocks a crucial contributor to stock return anomalies? In their May 2020 paper entitled “Lottery Preference and Anomalies”, Lei Jiang, Quan Wen, Guofu Zhou and Yifeng Zhu aggregate 16 measures of lottery preference into a single long-short factor via time-varying linear combination. Examples of the 16 measures are: maximum daily return last month; average of the five highest daily returns last month; difference between maximum and minimum daily returns last month; and, skewness of daily returns the past three months. They then test the ability of this lottery preference factor to help explain a set of 19 stock return anomalies previously unexplained by a widely used 4-factor (market, size, investment and profitability) model of stock returns. They further study interactions between the lottery preference factor and 11 well-known anomalies by each month during 1980-2018 double-sorting stocks first into fifths (quintiles) based on lottery preference and then within each lottery preference quintile into sub-quintiles based on each anomaly characteristic. Using firm/stock data for a broad sample of U.S. common stocks priced over $1 during January 1962 through December 2018, they find that:

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