Investing Research Articles

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Explaining Stock Return Anomalies with a Five-factor Model

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

Combining and Exploiting Stock Market Forecasting Variables

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:

  1. Kitchen sink – employing regression coefficients for all 20 forecasting variables (but with four of the variables compressed into a composite).
  2. 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.
  3. 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

U.S. Stock Market Death Crosses and Golden Crosses

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:

  1. Behavior of the index during the 126 trading days (six months) after death and golden crosses.
  2. 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

SACEMS with Three Copies of Cash

Subscribers have expressed concern about selecting assets with negative past returns for versions of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) that hold the equally weighted (EW) Top 2 or EW Top 3 exchange-traded funds (ETF). To test this concern, we expand the universe of ETFs/Cash by adding two copies of Cash, as follows:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)
3-month Treasury bills (Cash)
3-month Treasury bills (Cash)

For this universe, if all assets other than Cash have negative total returns over the past five months, the EW Top 2 and EW Top 3 portfolios select only Cash. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through July 2015 (114 months), we find that: Keep Reading

Weekly Summary of Research Findings: 8/17/15 – 8/21/15

Below is a weekly summary of our research findings for 8/17/15 through 8/21/15. 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

Inflation-based Projection of the Price of Gold

Where is the price of gold headed? In their August 2015 paper entitled “The Golden Constant”, Claude Erb and Campbell Harvey apply a “golden constant” hypothesis (inflation is the principal driver of the price of gold) to project the future price of gold. Specifically, they explore implications of mean reversion of the real price of gold. Using the monthly relationship between gold price in U.S. dollars and U.S. inflation during 1975 through the first half of 2015, they find that: Keep Reading

Stock Market Valuation Ratio Trends

To determine whether the stock market is expensive or cheap, some experts use aggregate valuation ratios, either trailing or forward-looking, such as earnings-price ratio (E/P) and dividend yield. Operating under a belief that such ratios are mean-reverting, most imminently due to movement of stock prices, these experts expect high (low) future stock market returns when these ratios are high (low). Where are the ratios now? Using the most recent actual and forecasted earnings and dividend data from Standard & Poor’s, we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for July 2015. The actual total (core) inflation rate for July is about the same as (the same as) forecasted.

Carry Trade Excluding Unfavorable Conditions

Is there an easy way to avoid unfavorable positions within a currency carry trade strategy (long currencies with high interest rates and short those with low)? In their July 2015 paper entitled “Conditioning Carry Trades: Less Risk, More Return!”, Arjen Mulder and Ben Tims examine a carry trade strategy that avoids currencies for which exchange rate return is likely to offset interest rate return (the carry trade is unlikely to work). Based on prior research, they hypothesize that carry-trade-won’t-work conditions are: (1) very high absolute interest rate differences; plus, (2) high exchange rate volatility. They specify an interest rate difference as extreme if it is among the 10% highest monthly absolute differences across all currencies relative to the U.S. dollar over the last 60 months. They specify exchange rate volatility as extreme if the five-year exponential moving average of squared differences between conventional carry trade returns and the average carry trade return over the last 60 months is among the top 25% of values. Using monthly spot exchange rates versus the U.S. dollar and interest rates for 25 currencies as available during January 1975 through May 2015 (with the first ten years used to define interest rate difference and exchange rate volatility conditions as of January 1985), they find that: Keep Reading

Sociology of Financial Markets Research?

What does a large online repository of research on financial markets say about community interactions? In the August 2015 version of his article entitled “Recent Trends in Empirical Finance”, Marcos Lopez de Prado measures trends in level of research activity, topical emphasis, level of interest as measured by downloads and level of collaboration. Based on data for 128,897 research papers by 72,070 authors posted on SSRN’s Financial Economics Network (as of June 4, 2015), he finds that: Keep Reading

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Current Momentum Winners

ETF Momentum Signal
for August 2015 (Final)

Winner ETF

Second Place ETF

Third Place ETF

Gross Compound Annual Growth Rates
(Since August 2006)
Top 1 ETF Top 2 ETFs
13.5% 14.0%
Top 3 ETFs SPY
14.0% 7.7%
Strategy Overview
Current Value Allocations

ETF Value Signal
for August 2015 (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
13.0% 10.0% 8.1%
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