We have updated the the monthly asset class ETF momentum winners and associated performance data at Momentum Strategy.

We have updated the Trading Calendar to incorporate data for August 2015.

**Objective research and reviews to aid investing decisions**

| Tuesday, September 1, 2015

**Research Categories:**- Momentum Investing
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**August 31, 2015**

We have updated the the monthly asset class ETF momentum winners and associated performance data at Momentum Strategy.

We have updated the Trading Calendar to incorporate data for August 2015.

**August 31, 2015**

The home page and “Momentum Strategy” now show preliminary asset class ETF momentum strategy positions for September 2015. The difference in past returns between first and second place is large. The differences among second, third and fourth places are small, so their order could easily change by the close. Only Cash has a positive return over the ranking interval.

Risk-averse investors following the strategy may want to consider the findings in “SACEMS with Three Copies of Cash”.

**August 31, 2015**

A reader proposed: “I recently found something interesting while analyzing the ratio of the equal-weighted S&P 500 Index to its market capitalization-weighted counterpart. Whenever this ratio declines (out of an uptrend), the market crashes (July 2007, September-October 2008, July 2011). Also, when this ratio starts rising, the recovery commences (April 2009). The indicator seems to warn of problematic times ahead. …Perhaps this ratio provides insight into whether money is moving into the market (ratio rising) or out of the market (ratio falling). Could you take a look at this to see whether this ratio is a great indicator?” To investigate, we employ S&P 500 SPDR (SPY) and Rydex S&P 500 Equal Weight (RSP) as tradable proxies for the capitalization-weighted and equal-weighted S&P 500 Index, respectively. Using weekly and monthly dividend-adjusted values of SPY and RSP from the end of April 2003 (limited by data for RSP) through July 2015 (641 weeks), *we find that:* Keep Reading

**August 28, 2015**

Below is a weekly summary of our research findings for 8/24/15 through 8/28/15. 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

**August 28, 2015**

Is implied volatility of implied volatility, interpretable as a measure of changes in investor fear level, a useful indicator of future stock market returns or VIX futures returns? To investigate, we examine relationships between the CBOE VVIX Index, a measure of the expected volatility of the 30-day forward level of the S&P 500 Implied Volatility Index (VIX) derived from prices of VIX options, and future returns for SPDR S&P 500 (SPY)and iPath S&P 500 VIX Short-Term Futures (VXX). Using daily levels of VVIX and daily adjusted closes for SPY and VXX as available during January 2007 (VVIX inception) through mid-August 2015, *we find that:* Keep Reading

**August 27, 2015**

Does the new Fama-French five-factor model of stock returns explain a wider range of anomalies than the workhorse Fama-French three-factor model. In the June 2015 update of their paper entitled “Dissecting Anomalies with a Five-Factor Model”, Eugene Fama and Kenneth French examine the power of their five-factor model of stock returns to explain five anomalies not explicitly related to the five factors model and known to cause problems for the three-factor model (market beta, net share issuance, volatility, accruals, momentum). The five-factor model adds profitability (robust minus weak, or RMW) and investment (conservative minus aggressive, or CMA) factors to the three-factor model (market, size and book-to-market factors). The size, book-to-market, profitability and investment factor portfolios are reformed annually using data that are at least six months old (in contrast, the momentum factor portfolio is reformed monthly). Using data for a broad sample of U.S. firms and associated stocks during July 1963 through December 2014, *they find that:* Keep Reading

**August 26, 2015**

Does the set of variables that have the strongest correlations with subsequent U.S. stock market returns over the prior decade usefully predict market returns out-of-sample? In the July 2015 draft of their paper entitled “A Practitioner’s Defense of Return Predictability”, Blair Hull and Xiao Qiao apply this correlation screening approach to a set of 20 published stock market forecasting variables encompassing technical indicators, macroeconomic variables, return-based predictors, price ratios and commodity prices. Their horizon for historical daily correlation measurements and out-of-sample forecasts is 130 trading days (about six months). Every 20 days just before the market close, they employ regressions using the most recent ten years of data to: (1) determine the form of each forecasting variable (raw value, exponentially-weighted moving average or log value minus exponentially-weight moving average) that maximizes its daily correlation with 130-day returns; and, (2) estimate variable coefficients to predict the return for the next 130 days. For the next 20 days, they then use the estimated coefficients to generate expected returns and take a (market on close) position in SPDR S&P 500 (SPY) eight times the expected return in excess of the risk-free rate (capped at 150% long and 50% short). They consider three expected return models:

- Kitchen sink – employing regression coefficients for all 20 forecasting variables (but with four of the variables compressed into a composite).
- Correlation Screening – employing regression coefficients only for forecasting variables having absolute correlations with subsequent 130-day market returns at least 0.10 over the past ten years.
- Real-time Correlation Screening – same as Correlation Screening, but excluding any forecasting variables not yet discovered (published).

They assume: trading frictions of two cents per share of SPY bought or sold; daily return on cash of the three-month U.S. Treasury bill yield minus 0.3%; and, interest on borrowed shares of the Federal Funds Rate plus 0.3%. To limit trading frictions, they adjust positions only when changes in expected market return reach a threshold of 10%. They ignore tax implications of trading. Using daily total returns for SPY, the 3-month Treasury bill yield and vintage (as-released) values of the 20 forecast variables during 6/8/1990 through 5/4/2015, *they find that:* Keep Reading

**August 25, 2015**

A subscriber requested tests exploring whether a recent death cross for the Dow Jones Industrial Average (DJIA) portends an index crash. To investigate, we consider two ways of evaluating DJIA performance after death crosses and conversely defined golden crosses:

- Behavior of the index during the 126 trading days (six months) after death and golden crosses.
- Behavior of the index between converse crosses (death cross-to-golden cross, and golden cross-to-death cross).

We focus on distributions of average returns and maximum drawdowns during specified periods. We also check robustness by repeating DJIA tests on the S&P 500 Index. Using daily DJIA closes during October 1928 through mid-August 2015 and daily S&P 500 Index closes during January 1950 through mid-August 2015, *we find that:* Keep Reading