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Weekly Summary of Research Findings: 11/17/14 – 11/21/14

Below is a weekly summary of our research findings for 11/17/14 through 11/21/14. 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

Stock Returns Around Thanksgiving

Does the Thanksgiving holiday, a time of families celebrating plenty, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the three trading days before and the three trading days after the holiday. Using daily closing levels of the S&P 500 Index for 1950-2013 (64 events), we find that: Keep Reading

“Sell in May” Over the Long Run

Does the conventional wisdom to “Sell in May” (and “Buy in November”, hence also termed the “Halloween Effect”) work over the long run, perhaps due to biological/psychological effects of seasons (such as Seasonal Affective Disorder)? To check, we turn to the long run data set of Robert Shiller. This data set includes monthly levels of the S&P Composite Index, calculated as average of daily closes during the month. This method of calculation deviates from that most often used for return calculations, but arguably suppresses noise in daily data. We split the investing year into two half-years (seasons): May through October, and November through April. Using S&P Composite Index levels, associated dividend yields and contemporaneous long-term interest rates (comparable to yields on 10-year U.S. Treasury notes) from the Shiller data set spanning April 1871 through October 2014 (287 six-month returns), we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for October 2014. The actual total (core) inflation rate for October is lower than (a little higher than) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

Four-factor Model of Corporate Bond Returns

Do factor models predict returns for corporate bonds as they do for stocks? In their October 2014 paper entitled “Factor Investing in the Corporate Bond Market”, Patrick Houweling and Jeroen van Zundert develop and test a four-factor (size, low-risk, value and momentum) model of future corporate bond returns. Each month for investment grade and high yield bond market segments separately, they construct an equally-weighted long-only portfolio consisting of the 10% of bonds with the highest exposure to each factor. They hold portfolios for 12 months, resulting in 12 overlapping portfolios for each segment and factor. Specifically, the factor portfolios are:

  1. Size – the 10% of bonds with the smallest company index weights, calculated as the sum of market value weights of all company bonds in the index that month.
  2. Low-risk – a combination of rating and maturity. For investment grade, the portfolio holds the 10% of bonds rated AAA to A- and having the shortest maturities. For high yield, the portfolio holds the 10% of bonds rated BB+ to B- and having the shortest maturities. On average, the maturity threshold is 2.8 (3.7) years for investment grade (high yield).
  3. Value – the 10% of bonds with the largest percentage gaps between actual credit spread and credit spread indicated by monthly regressions of credit spread on rating.
  4. Momentum – the 10% of bonds with the highest return relative to duration-matched U.S. Treasuries from six months ago to one month ago (with a skip-month to avoid reversal).

They evaluate factor portfolio performance based on excess return of constituent corporate bonds versus duration-matched U.S. Treasuries (thereby focusing on the default premium component of corporate bond returns). To estimate trading frictions, they model bid-ask spreads based on maturity and rating (the longer maturity or the lower the rating, the larger the estimated trading friction). Portfolio-level trading frictions are sums of frictions for all bonds traded. Using monthly data for all bonds in the Barclays U.S. Corporate Investment Grade index and the Barclays U.S. Corporate High Yield index during January 1994 through December 2013 (about 800,000 investment grade and 300,000 high yield bond-month observations), they find that: Keep Reading

Momentum-driven Turn-of-the-month Effect in Commodity Futures

Is the Commodity Trading Advisor (CTA) segment so crowded that flows of funds into or out of them around the turn of the month materially affect prices? In the October 2014 version of his paper entitled “The MOM-TOM Effect: Detecting the Market Impact of CTA Trading”, Otto Van Hemert explores whether the trend-following or time series momentum (MOM) style employed by many CTAs is so crowded that inflows around the turn of the month (TOM) affect momentum strategy returns. He notes that most CTA-managed funds offer monthly liquidity, thereby concentrating flows at month ends. He defines TOM as the last two days of a month plus the first day of the next month. He tests whether there is an above average return for MOM strategies during TOM (MOM-TOM effect). He uses the Newedge CTA Index (an equal-weighted aggregate of the largest CTAs open to new investments) and the Newedge Trend Index (an equal-weighted aggregate of the MOM style CTAs that are open to new investments) as proxies for the overall market and the MOM style, respectively. Using daily returns for these two indexes during January 2000 through March 2014, he finds that: Keep Reading

Market Liquidity Necessary for Momentum Strategy Profitability?

Is there a way to predict when stock price momentum strategies will thrive or crash? In the October 2014 update of their draft paper entitled “Time-Varying Momentum Payoffs and Illiquidity”, Doron Avramov, Si Cheng and Allaudeen Hameed investigate the relationship between future momentum strategy profitability and market illiquidity. They measure momentum conventionally as the average gross monthly return of a portfolio that is each month long the value-weighted tenth (decile) of common stocks with the highest and short the value-weighted decile of common stocks with the lowest returns from 12 months ago to one month ago (with a skip-month to avoid short-term reversal). Their stock illiquidity metric is the Amihud measure (average daily price impact per monetary volume traded over the past month), and they measure market illiquidity as the value-weighted average stock illiquidity. Using daily and monthly prices and market capitalizations for a broad sample of U.S. common stocks, monthly equity risk factors, investor sentiment and firm earnings data as available during January 1926 through December 2011, they find that: Keep Reading

140-year Stock Momentum Strategy Crash Test

What conditions foretell stock momentum strategy crashes? In their October 2014 paper entitled “Momentum Trading, Return Chasing, and Predictable Crashes”, Benjamin Chabot, Eric Ghysels and Ravi Jagannathan examine stock momentum strategy performance for both widely used historical U.S. data (starting in 1926 through 2012) and for a hand-collected sample of stocks listed on the London Stock Exchange during 1866 to 1907. They consider two methods of measuring momentum strategy returns. One is the gross return to the Fama-French momentum factor portfolio. The other is the gross return to a portfolio that is each month long (short) the value-weighted 30% of stocks with the highest (lowest) returns per the Fama-French momentum decile portfolios. Both methods define momentum conventionally as the return from 12 months ago to one month ago, with a skip-month before portfolio formation to avoid short-term reversal. They focus on conditions that precede momentum strategy crashes based on a model that considers three factors: (1) the risk-free rate; (2) past stock market return; and, (3) past momentum strategy return. Using the specified stock return data sets, they find that: Keep Reading

Weekly Summary of Research Findings: 11/10/14 – 11/14/14

Below is a weekly summary of our research findings for 11/10/14 through 11/14/14. 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

Consumer Sentiment and Stock Market Returns

The business media and expert commentators sometimes cite the monthly University of Michigan Consumer Sentiment Index as an indicator of U.S. economic and stock market health, generally interpreting a jump (drop) in sentiment as good (bad) for future consumption and stocks. The release schedule for this indicator is mid-month for a preliminary reading on the current month and end-of-month for a final reading. Is this indicator in fact predictive of U.S. stock market behavior in subsequent months? Using monthly final Consumer Sentiment Index data from the Federal Reserve Bank of St. Louis, augmented by more recent data from Bloomberg and contemporaneous monthly levels of the S&P 500 Index during January 1978 through October 2014 (442 monthly sentiment readings), we find that: Keep Reading

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