Below is a weekly summary of our research findings for 11/24/14 through 11/28/14. 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.
November 28, 2014
We have updated the S&P 500 Market Models summary as follows:
- Extended Market Models regressions/rolled projections by one month based on data available through November 2014.
- Updated Market Models backtest charts and the market valuation metrics map based on data available through November 2014.
We have updated the Trading Calendar to incorporate data for November 2014.
We have updated the the monthly asset class momentum winners and associated performance data at Momentum Strategy.
November 28, 2014
The home page and “Momentum Strategy” now show preliminary asset class momentum strategy positions for December 2014. Differences in past returns among assets are large enough that there is very little chance that the top three will change by the (early) close. There is a slim possibility that the top two could switch places.
At this point, four of nine asset classes have negative cumulative returns over the past five months.
Do predictive powers of the size, value and momentum factors observed for individual stocks translate to the country level? In the November 2014 version of his paper entitled “Country Selection Strategies Based on Value, Size and Momentum”, Adam Zaremba investigates country-level value, size and momentum premiums, tests whether the value and momentum premiums are equally strong across markets of different sizes and evaluates a country-level multi-factor asset pricing model. He measures factors at the country level as:
- Value: aggregate book-to-market ratio, with aggregate 12-month earnings-to-price-ratio, cash flow-to-price ratio and dividend yield as alternatives where available.
- Size: total market capitalization of country stocks.
- Momentum: cumulative return over preceding 12, 9, 6 or 3 months excluding the last month to avoid short-term reversal.
He relies on capitalization-weighted, U.S. dollar-denominated gross total return MSCI equity indexes as available, with Dow Jones and STOXX indexes as fallbacks (an average 56 indexes per month over time). He includes discontinued country indexes. He uses one-month LIBOR as the risk-free rate. Each month, he ranks countries by value, size and momentum into value-weighted or equal-weighted fifths (quintiles). He also performs double-sorts first on size and then on value or momentum. Using monthly firm/stock data for listed stockswithin 78 country indexes as available during February 1999 through September 2014 (147 months), he finds that: Keep Reading
November 26, 2014
Are publicly traded Master Limited Partnerships attractive investments? In their June 2014 paper entitled “Master Limited Partnerships (MLPs)”, Frank Benham, Steven Hartt, Chris Tehranian and Edmund Walsh describe and summarize the aggregate performance and characteristics of publicly traded MLPs. These partnerships are predominantly owners of “toll road” energy infrastructure, U.S. oil and natural gas pipelines and resource shipping. Like real estate investment trusts (REIT), MLPs are pass-through entities for tax purposes. Their distributions to partners are not subject to double-taxation as are corporate dividends. Unlike REITs, MLPs may retain income to fund growth. The general (managing) partner of an MLP typically earns an incentive-based share of distributions larger than that of limited (passive) partners. MLPs involve tax, accounting and administrative complications associated with partnerships. Using monthly returns for the capitalization-weighted Alerian MLP Index and for other asset class indexes during January 2000 through April 2014, they conclude that: Keep Reading
Does quantitative technical analysis work reliably in currency trading? If so, where does it work best? In their May 2013 paper entitled “Forty Years, Thirty Currencies and 21,000 Trading Rules: A Large-Scale, Data-Snooping Robust Analysis of Technical Trading in the Foreign Exchange Market”, Po-Hsuan Hsu and Mark Taylor test the effectiveness of a broad set of quantitative technical trading rules as applied to exchange rates of 30 currencies with the U.S. dollar over extended periods. They consider 21,195 distinct technical trading rules: 2,835 filter rules; 12,870 moving average rules; 1,890 support-resistance signals; 3,000 channel breakout rules; and, 600 oscillator rules. They employ a test methodology designed to account for data snooping in identifying reliably profitable trading rules. They also test whether technical trading effectiveness weakens over time. In testing robustness to trading frictions, they assume a fixed one-way trading cost of 0.025%. Using daily U.S. dollar exchange rates for nine developed market currencies and 21 emerging market currencies during January 1971 through July 2011, they find that:
November 24, 2014
In which country stock markets is technical analysis likely to work best? In the October 2014 version of her paper entitled “Technical Analysis: A Cross-Country Analysis”, Jiali Fang investigates three potential cross-country determinants of technical trading profitability:
- An individualism index, measuring the degree to which individuals integrate via cultural groups.
- Market development and integrity metrics, including stock market size, stock market age, transaction costs and measures of investor protection, anti-director rights, ownership concentration and insider trading.
- Information uncertainty metrics, including aggregate market turnover, volatility of cash flow growth rate and book-to-market ratio.
She considers 26 previously studied trading rules employing only past prices, classified as: variable moving average (VMA) rules, fixed-length moving average (FMA) rules and trading range break-out (TRB) rules. VMA rules are long (short) an index when a short-term moving average is above (below) a long-term moving average. FMA rules are similar to VMA rules, but hold a newly signaled position a fixed interval of 10 days. TRB rules generate buy (sell) signals when price rises above (falls below) the resistance (support) defined by prices over a specified past interval. Tests include both regressions and model strategies that are long (short) the market index as signaled and invest in the risk-free asset when there is no signal. Using cultural metrics, daily stock market index data and economic/financial variables for 50 countries during March 1994 through March 2014, she finds that: Keep Reading
November 21, 2014
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.