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Weekly Summary of Research Findings: 6/29/15 – 7/2/15

Below is a weekly summary of our research findings for 6/29/15 through 7/2/15. 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

Sector vs. Factor U.S. Stock Diversification?

Which is better, sector-based or factor-based stock investing? In their June 2015 paper entitled “Factor-Based v. Industry-Based Asset Allocation: The Contest”, Marie Briere and Ariane Szafarz compare the attractiveness of sector-based and factor-based U.S. stock allocations. From Kenneth French’s data library, they extract return series for 10 sectors and five factors (size, value, profitability, investment and momentum). They expand the factor set to 10 by using long and short portfolios for each factor. They consider three trials:

  1. Which group, sectors or factors, yields the dominantly more attractive efficient frontier?
  2. Which group offers the clearly superior gross Jensen’s alphas across single-sector/factor portfolios and portfolios diversified across sectors or factors based on maximizing estimated Sharpe ratio, minimizing estimated volatility or equal weighting?
  3. Do portfolios diversified across sectors or factors (based on maximizing estimated Sharpe ratio, minimizing estimated volatility or equal weighting) offer the best gross Sharpe ratios?

For each trial, they test long-only and long-short factor portfolios. Also for each trial, they test the overall sample, economic recession and expansion subsamples (per the National Bureau of Economic Research) and bull and bear market subsamples (per Forbes magazine). Using monthly U.S. stock market factor and sector returns from Kenneth French’s library spanning July 1963 through November 2014, they find that: Keep Reading

Real-world Equity Fund Performance Benchmarks

Do equity style mutual funds look more attractive when benchmarked to matched style stock indexes than to more theoretical factor models of stock returns? In their April 2015 paper entitled “On Luck versus Skill When Performance Benchmarks are Style-Consistent”, Andrew Mason, Sam Agyei-Ampomah, Andrew Clare and Steve Thomas compare alphas for U.S. equity style mutual funds as calculated with conventional factor models and as calculated with matched Russell style indexes. The factor models they consider are the 1-factor capital asset pricing model (CAPM), the Fama-French 3-factor model (market, size, book-to-market) and the Carhart 4-factor model (adding momentum). They consider both value (net asset value)-weighted and equal-weighted portfolios of mutual funds. They also perform simulations to control for differences in the precision of alpha estimates due to differences in fund sample sizes. Using monthly gross and net returns and equity styles for 2,384 surviving and dead U.S. diversified equity funds, and returns for Russell equity style indexes and market/size/value/momentum factors, during January 1990 through December 2011, they find that: Keep Reading

Momentum Strategy, Value Strategy and Trading Calendar Updates

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

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

Preliminary Momentum and Value Strategy Updates

The home page and “Momentum Strategy” now show preliminary asset class ETF momentum strategy positions for July 2015. The differences in past returns among the top four places are fairly large, and the past returns for the top three positions are sufficiently above the Cash return, that selections are unlikely to change by the close. However, markets are volatile.

The home page and “Value Strategy” now show preliminary ETF allocations related to term, credit and equity premiums for the third quarter of 2015. These allocations could shift slightly by the close.

More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by the Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets then becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

We compare original (SACEMS) and modified (SACEMS Granular) winner portfolios, allocating all funds at the end of each month to the asset class ETF or cash with the highest total return over the past five months. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 through May 2015 (156 months), we find that: Keep Reading

Update SACEVS with End-of-quarter Instead of Quarterly Average Yields?

“Simple Asset Class ETF Value Strategy” (SACEVS) tests a simple relative value strategy that each quarter allocates funds to one or more of the following three asset class exchange-traded funds (ETF), plus cash, based on degree of undervaluation of measures of the term risk, credit risk and equity risk premiums:

3-month Treasury bills (Cash)
iShares 7-10 Year Treasury Bond (IEF)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

One version of SACEVS (Best Value) picks the most undervalued premium. Another (Weighted) weights all undervalued premiums according to degree of undervaluation. Premium calculations and SACEVS portfolio allocations derive from quarterly average yields for 3-month Constant Maturity U.S. Treasury bills (T-bills), 10-year Constant Maturity U.S. Treasury notes (T-notes) and Moody’s Seasoned Baa Corporate Bonds (Baa). A subscriber asked whether fresh end-of-quarter yields might work better than quarterly average yields. Using monthly S&P 500 Index levelsquarterly S&P 500 earnings and daily T-note, T-bill and Baa yields during March 1989 through March 2015 (limited by availability of earnings data), and quarterly dividend-adjusted closing prices for the above three asset class ETFs during September 2002 through March 2015 (154 months, limited by availability of IEF and LQD), we find that: Keep Reading

Weekly Summary of Research Findings: 6/22/15 – 6/26/15

Below is a weekly summary of our research findings for 6/22/15 through 6/26/15. 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

The Decision Moose Asset Allocation Framework

A reader suggested a review of the Decision Moose asset allocation framework of William Dirlam. “Decision Moose is an automated framework for making intermediate-term investment decisions.” Decision Moose focuses on asset class momentum, as augmented by monetary policy, exchange rate and interest rate indicators. Its signals tell followers when to switch from one index fund to another among nine encompassing a broad range of asset classes, including equity indexes for several regions of the globe. The trading system is a long-only approach that allocates 100% of funds to the index “having the highest probability of price appreciation.” The site includes a history of switch recommendations since the end of August 1996, with gross performance. To evaluate Decision Moose, we assume that switches and associated trading returns are as described (out of sample, not backtested) and compare the returns to those for the dividend-adjusted S&P 500 Depository Receipts (SPY) over the same intervals. Using Decision Moose signals and performance data during 8/30/96 through 6/5/15 (nearly 19 years), we find that: Keep Reading

Index Investing Makes Stock Picking Harder?

How does growth in capitalization-weighted equity index investing affect the stock market? In the December 2014 update of their paper entitled “Indexing and Stock Price Efficiency”, Nan Qin and Vijay Singal examine the relationship between equity index investing (driven passively by liquidity trading and index changes, not actively by information) and stock price efficiency. They estimate equity index (passive) investing from holdings of 591 equity index mutual funds, enhanced index mutual funds, exchange-traded funds and closet indexers. They measure each stock’s passive (non-passive) ownership as the percentage of shares held by these funds (other funds) at the end of each quarter, with the lower bound of passive (non-passive) trading volume the absolute quarterly change in holdings of these (other) funds. They measure stock price efficiency by: (1) magnitude of post-earnings announcement drift (response to new information); and, (2) intraday and daily deviations of price from a random walk. Each quarter, they relate these measures of price inefficiency to level of index ownership across stocks. Using intraday and daily return, earnings announcement and quarterly fund holdings data for S&P 500 Index stocks and size/turnover-matched stocks during 2002 (post-decimalization) through 2013, they find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for July 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
13.8% 14.1%
Top 3 ETFs SPY
14.0% 7.5%
Strategy Overview
Current Value Allocations

ETF Value Signal
for 3rd Quarter 2015 (Final)

Cash

IEF

LQD

SPY

The asset with the highest allocation is the holding of the Best Value strategy.
Gross Compound Annual Growth Rates
(Since September 2002)
Best Value Weighted 60-40
13.4% 9.4% 8.4%
Strategy Overview
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