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Weekly Summary of Research Findings: 6/20/16 – 6/24/16

Below is a weekly summary of our research findings for 6/20/16 through 6/24/16. 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.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Effect of Tracking Products on Short-term Equity Index Trending/Reversal

Does availability of liquid tracking products change short-term trending/reversal tendencies of equity indexes? In their May 2016 paper entitled “Indexing and Stock Market Serial Dependence Around the World”, Guido Baltussen, Sjoerd van Bekkum and Zhi Da investigate how introduction of index futures, exchange-traded funds (ETF) and mutual funds affects measures of index serial dependence. They hypothesize that technical interplay in index products among investors, market makers and arbitrageurs stimulates short-term reversal. They measure serial dependence with daily lags and one-week lag in two ways: (1) simple autocorrelations; and, (2) returns to a “MAC(5)” trading strategy based on a weighted average of autocorrelations for lags 1 to 4, with positive (negative) returns indicating trending (reversal). Using daily data for 21 major global equity indexes and associated index futures and ETFs and for mutual funds tracking the S&P 500 Index as available through mid-May 2013, they find that: Keep Reading

Stock Market Behavior Around Mid-year and 4th of July

The middle of the year might be a time for funds to dress their windows and investors to review and revise portfolios. The 4th of July celebration might engender optimism among U.S. investors. Are there any reliable patterns to daily U.S. stock market returns around mid-year and the 4th of July? To check, we analyze the historical behavior of the S&P 500 Index from five trading days before through trading days after both the end of June and the 4th of July. Using daily closing levels of the index for 1950-2015 (66 years), we find that: Keep Reading

Predicting Stock Index Reversals with Amareos Sentiment Indicators

Do data-intensive, high-frequency investor sentiment measurements usefully predict stock index performance? In his May 2016 paper entitled “Can Sentiment Indicators Signal Market Reversals?”, Arnaud Lagarde applies a random forest machine learning algorithm to test the power of Amareos sentiment indications to predict stock index reversals. Algorithm training data relates sentiment to known stock index return for the next 182 days (six months). If this return is -20% or lower (+10% or higher), he designates the condition at the time of forecast as a market top (bottom). Otherwise, he designates the condition as neutral. He starts with 20 global equity indexes. He holds out four indexes (CAC40, CSI300, Nikkei and S&P500) for out-of-sample testing. He then randomly selects 80% of daily observations on the other 16 indexes for algorithm training, with the remaining 20% reserved for additional out-of-sample testing. Out-of-sample testing includes tabulation of raw top/bottom identification accuracy and a simple trading strategy that is long (in cash) after a bottom (top) indication and does not react to a neutral indications. He focuses trading strategy testing on: (1) the four hold-out indexes over the entire sample period; and, (2) the last six weeks of data for all indexes, which cannot be used for training. Using daily Amareos market sentiment readings and returns for the 20 equity indexes during January 2005 through mid-April 2016, he finds that: Keep Reading

Finding Close Economic Substitutes for Stock Pairs Trading

When does a cointegration test, which looks for a connection between two apparently wandering price paths, work for pairs trading? In their May 2016 paper entitled “Cointegration and Relative Value Arbitrage”, Binh Do and Robert Faff investigate the conditions under which cointegration successfully identifies stocks for pairs trading. Their basic pairs trading strategy is to each month:

  1. Identify cointegrated pairs based on daily total returns over the last 12 months.
  2. Over the next six months, buy (sell) the relatively undervalued (overvalued) stock when cointegrated pair spread exceeds its selection interval mean by two standard deviations.
  3. Close positions when the spread reverts to its historical mean or the trading period ends, whichever occurs first.
  4. Closed trades may be reopened as signaled, if there is more than a month left in the trading interval.

They then refine the strategy by constraining selected pairs to those that are close economic substitutes, corresponding to a low cointegration coefficient. Pairs passing (failing) this constraint move together in the long run without any price scaling (only with scaling of prices for one member of the pair). While they focus on pairs of individual stocks, they also consider trading of pairs of small groups (baskets) of stocks. Their benchmark is a conventional pairs trading strategy that identifies pairs with the smallest sums of squared differences in normalized daily prices over the past 12 months, and then trades as specified above over the next six months. Using daily data for a broad sample of U.S. common stocks during July 1962 through December 2013, they find that: Keep Reading

Pervasive 12-Month (and 5-Day) Relative Strength Cycles?

Do asset returns exhibit cyclic relative strength? In the December 2015 revision of their paper entitled “Return Seasonalities”, Matti Keloharju, Juhani Linnainmaa and Peter Nyberg examine 12-month relative strength cycles via a strategy that is each month long (short) assets with the highest (lowest) returns during the same calendar month over the past 20 years. They apply this strategy to individual U.S. stocks, factor and anomaly portfolios of U.S. stocks, industry portfolios of U.S. stocks, developed country stock indexes and commodity futures contract series. They also test a 5-day relative strength cycle across individual U.S. stocks. They perform ancillary tests to investigate sources and interactions of relative strength cycles. Using monthly and daily data for a broad sample of U.S. common stocks, industry portfolios and factor/anomaly portfolios mostly since July 1963 and monthly data for 24 commodity futures series and 15 country stock indexes since January 1970, all through December 2011, they find that: Keep Reading

Weekly Summary of Research Findings: 6/13/16 – 6/17/16

Below is a weekly summary of our research findings for 6/13/16 through 6/17/16. 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.

Subscribers: To receive these weekly digests via email, click here to sign up for our mailing list. Keep Reading

Rough Net Worth Growth Benchmarks

How fast should individuals plan to grow net worth as they age? To investigate, we examine median levels of household (1) total net worth and (2) net worth excluding home equity from several vintages of U.S. Census Bureau data. We make the following head-of-household age cohort assumptions:

  • “Less than 35 years” means about age 30.
  • “35 to 44 years” means about age 39.
  • “45 to 54 years” means about age 49.
  • “55 to 64 years” means about age 59.
  • “65 to 69 years” means about age 67.
  • “70 to 74 years” means about age 72.
  • “75 and over” means about age 78.

We also assume that wealth growth between these ages is constant via compound annual growth rate (CAGR) calculations. Using median levels of total net worth and net worth excluding home equity from 2000. 2005, 2010 and 2011 Census Bureau summary tables, we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for May 2016. The actual total (core) inflation rate for May is slightly higher than (slightly higher than) forecasted.

Effective Federal Funds Rate and the Stock Market

Do changes in the Effective Federal Funds Rate (EFFR), the actual cost of short-term liquidity derived from a combination of market demand and Federal Reserve open market operations designed to maintain the Federal Funds Rate (FFR) target, predictably influence the U.S. stock market over the intermediate term? To investigate, we relate smoothed (volume-weighted median) monthly levels of EFFR to monthly U.S. stock market returns (S&P 500 Index or Russell 2000 Index) over available sample periods. Using monthly data as specified since July 1954 for EFFR and the S&P 500 Index (limited by EFFR) and since September 1987 for the Russell 2000 Index, all through April 2016, we find that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for June 2016 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
10.5% 11.1%
Top 3 ETFs SPY
12.2% 7.3%
Strategy Overview
Current Value Allocations

ETF Value Signal
for June 2016 (Final)





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
12.7% 9.8% 7.8%
Strategy Overview
Editor Archive Picks

Quality-enhanced Size Effect

…idence about the import of the size effect, is there a way investors can extract a reliable premium from small stocks? In their January 2015 draft paper entitled “Size Matters, If You Control Your Junk”, Clifford Asness, Andrea Frazzini, Ronen Israel, Tobas Moskowitz and Lasse Pedersen examine whether controlling for firm quality mitigates the following seven unfavorable empirical findings that the size effect: Is weak overall in the…

Summarizing Value (and Momentum) Investing

When does value investing work and how does it work best? In the April 2015 initial draft of their paper entitled “Fact, Fiction, and Value Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz address areas of confusion about value investing. They describe value as the tendency of cheap securities to outperform expensive ones based on some valuation method. They broadly specify the value premium as the…

Factor Investing Wisdom?

How should investors think about stock factor investing? In his April 2016 paper entitled “The Siren Song of Factor Timing”, Clifford Asness summarizes his current beliefs on exploiting stock factor premiums. He defines factors as ways to select individual stocks based on such firm/stock variables as market capitalization, value (in many flavors), momentum, carry (yield) and quality. He equates factor, smart beta and style…

Mocking Momentum Myths

What about all those criticisms of momentum investing (such as high turnover/trading frictions and crash-proneness)? In the May 2014 draft of their paper entitled “Fact, Fiction and Momentum Investing”, Clifford Asness, Andrea Frazzini, Ronen Israel and Tobias Moskowitz summarize research on the momentum anomaly and rebut ten criticisms (myths) of momentum investing. Specifically, they address claims that momentum profitability is…

Stock Quality and Future Returns

Are high-quality stocks worth the price? In their August 2013 paper entitled “Quality Minus Junk”, Clifford Asness, Andrea Frazzini and Lasse Pedersen investigate whether high-quality stocks outperform low-quality stocks. They define high-quality stocks as those that are profitable, growing, safe and well-managed. Specifically, they compute a single quality score for each stock by averaging scores for four components calculated as…

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