Objective research to aid investing decisions
Value Allocations for Jun 2018 (Final)
Cash TLT LQD SPY
Momentum Allocations for Jun 2018 (Final)
1st ETF 2nd ETF 3rd ETF
CXO Advisory

Investing Research Articles

Page 2 of 25012345678910...Last »

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for May 2018. The actual total (core) inflation rate for May is higher than (slightly higher than) forecasted.

Intrinsic Momentum or SMA for Avoiding Crashes?

A subscriber suggested comparing intrinsic momentum (IM), also called absolute momentum and time series momentum, to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate across a wide variety of economic and market conditions, we measure the long run performances of entry and exit signals from IM over past intervals of one to 12 months (IM1 through IM12) and SMAs ranging from 2 to 12 months (SMA2 through SMA12. We consider two cases for IM signals: (1) in stocks (cash) when past return is positive (negative); and, (2) in stocks (cash) when average monthly past return is above (below) the average monthly risk-free rate, proxied by the 3-month U.S. Treasury bill (T-bill) yield, over the same measurement interval. The rule for SMAs is: in stocks (cash) when current level is above (below) the SMA. Using monthly T-bill yield and monthly level of the Dow Jones Industrial Average (DJIA) during January 1934 through April 2018 (over 84 years), we find that: Keep Reading

Is There Really an Size Effect?

Do small market capitalization stocks really outperform big ones, as strongly implied by the prominence of the size effect in published research and factor models? In their May 2018 paper entitled “Fact, Fiction, and the Size Effect”, Ron Alquist, Ronen Israel and Tobias Moskowitz survey the body of research on the size effect and employ simple tests to assess claims made about it. Based on published and peer-reviewed academic papers and on tests using data for U.S. stocks and equity factor premiums, international developed and emerging market stocks and stock indexes, U.S. bonds and various currencies as available through December 2017, they find that: Keep Reading

Weekly Summary of Research Findings: 6/4/18 – 6/8/18

Below is a weekly summary of our research findings for 6/4/18 through 6/8/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Industry vs. Academia on Asset Quality

How well do different measures of stock quality perform as portfolio screens? In the May 2018 update of paper entitled “Does Earnings Growth Drive the Quality Premium?”, Georgi Kyosev, Matthias Hanauer, Joop Huij and Simon Lansdorp review commonly used quality definitions, test their respective powers to predict stock returns and analyze usefulness in constructing international stocks and corporate bonds settings. They consider the following definitions of quality:

  • Industry – return on equity (ROE); earnings-to-sales ratio (margin); annual growth in ROE; total debt-to-common equity (leverage); and, earnings variability.
  • Academia – gross profitability; accruals; and, net stock issues.

To compare predictive powers, at the end of each month they rank assets into fifths (quintiles) based on each metric and examine equally weighted performances of these quintiles. They calculate gross annualized average excess returns (relative to the risk-free rate) and gross annualized Sharpe ratios for the top and bottom quintiles and the difference between these two quintiles (top-minus-bottom). They also calculate four-factor (market, size, book-to-market and momentum) alphas for top-minus-bottom portfolios. They further analyze equally weighted combinations of all industry metrics and all academic metrics. They consider the largest stocks globally, regionally and from emerging markets. For robustness, they also consider samples of investment-grade and high-yield corporate bonds (with a 12-month rather than one-month holding interval). Using samples of relatively large non-financial common stocks for developed markets (starting December 1985) and emerging markets (starting December 1992) and samples of investment-grade and high-yield corporate bonds (starting January 1994) through December 2014, they find that: Keep Reading

Firm Sales Seasonality as Stock Return Predictor

Do firms with predictable sales seasonality continually “surprise” investors with good high season (bad low season) sales and thereby have predictable stock return patterns? In their May 2018 paper entitled “When Low Beats High: Riding the Sales Seasonality Premium”, Gustavo Grullon, Yamil Kaba and Alexander Nuñez investigate firm sales seasonality as a stock return predictor. Specifically, for each quarter, after excluding negative and zero sales observations, they divide quarterly sales by annual sales for that year. To mitigate impact of outliers, they then average same-quarter ratios over the past two years. They then each month:

  1. Use the most recent average same-quarter, two-year sales ratio to predict the ratio for next quarter for each firm.
  2. Rank firms into tenths (deciles) based on predicted sales ratios.
  3. Form a hedge portfolio that is long (short) the market capitalization-weighted stocks of firms in the decile with the lowest (highest) predicted sales ratios.

Their hypothesis is that investors undervalue (overvalue) stocks experiencing seasonally low (high) sales. They measure portfolio monthly raw average returns and four alphas based on 1-factor (market), 3-factor (market, size, book-to-market), 4-factor (adding momentum to the 3-factor model) and 5-factor (adding profitability and investment to the 3-factor model) models of stock returns. Using data for a broad sample of non-financial U.S common stocks during January 1970 through December 2016, 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

Benefits of Volatility Targeting Across Asset Classes

Does volatility targeting improve Sharpe ratios and provide crash protection across asset classes? In their May 2018 paper entitled “Working Your Tail Off: The Impact of Volatility Targeting”, Campbell Harvey, Edward Hoyle, Russell Korgaonkar, Sandy Rattray, Matthew Sargaison, and Otto Van Hemert examine return and risk effects of long-only volatility targeting, which scales asset and/or portfolio exposure higher (lower) when its recent volatility is low (high). They consider over 60 assets spanning stocks, bonds, credit, commodities and currencies and two multi-asset portfolios (60-40 stocks-bonds and 25-25-25-25 stocks-bonds-credit-commodities). They focus on excess returns (relative to U.S. Treasury bill yield). They forecast volatility using realized daily volatility with exponentially decaying weights of varying half-lives to assess sensitivity to the recency of inputs. For most analyses, they employ daily return data to forecast volatility. For S&P 500 Index and 10-year U.S. Treasury note (T-note) futures, they also test high-frequency (5-minute) returns transformed to daily returns. They scale asset exposure inversely to forecasted volatility known 24 hours in advance, applying a retroactively determined constant that generates 10% annualized actual volatility to facilitate comparison across assets and sample periods. Using daily returns for U.S. stocks and industries since 1927, for U.S. bonds (estimated from yields) since 1962, for a credit index and an array of futures/forwards since 1988, and high-frequency returns for S&P 500 Index and 10-year U.S. Treasury note futures since 1988, all through 2017, they find that:

Keep Reading

Weekly Summary of Research Findings: 5/29/18 – 6/1/18

Below is a weekly summary of our research findings for 5/29/18 through 6/1/18. These summaries give you a quick snapshot of our content the past week so that you can quickly decide what’s relevant to your investing needs.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Page 2 of 25012345678910...Last »
Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts
Popular Subscriber-Only Posts