Below is a weekly summary of our research findings for 2/1/16 through 2/5/16. 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.
February 5, 2016
February 5, 2016
Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index data for 1967 through 2015 (49 events), we find that: Keep Reading
How can trend following (intrinsic or absolute or time series momentum) beat the market, while ostensibly similar return chasing transfers wealth from naive to smart investors? In their January 2016 paper entitled “Return Chasing and Trend Following: Superficial Similarities Mask Fundamental Differences”, Victor Haghani and Samantha McBride offer a plausible and testable definition of return chasing and explore its differences from trend following. They characterize trend followers as mechanical and decisive and return chasers as discretionary and slow moving. For quantitative comparison, they consider three long-only, no-leverage strategies:
- 50-50 (benchmark): 50% equities and 50% U.S. Treasury bills (T-bills), rebalanced monthly.
- Trend following: 100% stocks (T-bills) when real stock market return over the past year is greater than (less than) 2.5%.
- Return chasing: increase (decrease) exposure to stocks each month by 20% of however much real stock market return exceeds (falls short of) 2.5% over the past year, holding the balance in T-bills.
They test these strategies with Robert Shiller’s long-run U.S. stock market data spanning 1871 through 2015 and with separately specified Monte Carlo simulation (5,000 runs of 20 years based on weekly simulated prices). Using these two approaches, they find that: Keep Reading
February 4, 2016
Should equity risk premium (ERP) forecasters assume in their models, because stocks always carry risk, that the premium cannot be negative? In their January 2016 paper entitled “Forecasting the Equity Risk Premium: The Ups and the Downs”, Nick Baltas and Dimitris Karyampas examine recent ERP forecasting research, with focus on simple models constrained to positive values. Their baseline model is a linear regression model that forecasts next-period S&P 500 Index excess return from either the index dividend-price ratio or the 3-month US treasury bill yield. They highlight advantages and disadvantages of models that do and do not constrain ERP to non-negative values for three types of market regimes: (1) up markets (positive actual ERP) versus down markets (negative actual ERP); (2) recessions versus expansions; and, (3) low volatility versus high volatility. Using monthly total returns for the S&P 500 Index and monthly levels of the predictive variables during January 1927 through December 2013 (with initial training period 20 years), they find that: Keep Reading
February 3, 2016
Do simple ratios such as book-to-market value and earnings-to-market price really identify value stocks? In their January 2016 paper entitled “Facts About Fictional Value Investing”, U-Wen Kok, Jason Ribando and Richard Sloan examine the effectiveness of “value” investing as implemented via sorts on simple fundamental ratios. They investigate interactions of these ratios with firm capitalization and test whether it is the value numerator or the price denominator that drives mean reversion of extreme value ratios. Using data for a broad sample of U.S. stocks with focus on recent decades, they find that: Keep Reading
Are moody investors prone to avoid risk on Monday and accept it on Friday? In his January 2016 paper entitled “Day of the Week and the Cross-Section of Returns”, Justin Birru examines how long-short U.S. stock anomaly portfolio returns vary by day of the week. His hypothesis is that pessimistic (optimistic) mood on Monday (Friday) leads to relatively low (high) returns for speculative stocks. His analysis focuses on 14 anomalies arguably tied to investor sentiment, with one side (short or long) speculative and the other side non-speculative, based on idiosyncratic volatility, lottery-like, firm age, distress, profitability, payouts, size or illiquidity. He also tests anomalies arguably unrelated to investor sentiment based on momentum, book-to-market, and asset growth. Using anomaly variable and return data for a broad sample of U.S. common stocks during July 1963 through December 2013, he finds that: Keep Reading
We have updated the the monthly asset class ETF value strategy weights and associated performance data at Value Strategy.