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Latest Market Research Articles

Simple Asset Class ETF Momentum Strategy Universe Enhancers?

Would adding a systematically chosen exchange-traded fund (ETF) or note (ETN) asset class proxy to the base set used in the “Simple Asset Class ETF Momentum Strategy” improve performance? To investigate, we consider adding each of the following 22 ETFs/ETNs (suggested over time by subscribers) one at a time to the strategy:

iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)
VelocityShares Daily Inverse VIX Short-Term (XIV)
ProShares UltraShort S&P 500 (SDS)
Guggenheim Frontier Markets (FRN)
iPath DJ-UBS Copper Total Return Sub-Index (JJC)
United States Oil (USO)
JPMorgan Alerian MLP Index (AMJ)
iShares 7-10 Year Treasury Bond (IEF)
iShares TIPS Bond (TIP)
Vanguard Total Bond Market (BND)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares Core US Credit Bond (CRED)
SPDR Barclays International Treasury Bond (BWX)
PowerShares DB G10 Currency Harvest (DBV)
SPDR Dow Jones International Real Estate (RWX)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
PowerShares Closed-End Fund Income Composite  (PCEF)
AlphaClone Alternative Alpha (ALFA)
IQ Hedge Multi-Strategy Tracker (QAI)
PowerShares Global Listed Private Equity  (PSP)
First Trust US IPO Index (FPX)

The base set consists of:

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)
SPDR Dow Jones REIT (RWR)
iShares Barclays 20+ Year Treasury Bond (TLT)
3-month Treasury bills (Cash)

We evaluate adding an asset to the base set via its effect on monthly net return-risk ratio (average monthly net return divided by standard deviation of monthly returns, a rough Sharpe ratio). Since the added assets have different sample periods, we rationalize by focusing on the difference in return-risk ratio (the ratio of the base set with the asset minus the ratio of the base set only) over the period the added asset is available. We then relate the resulting 22 differences in return-risk ratio to four characteristics of the respective added assets: (1) average monthly return; (2) standard deviation of monthly returns; (3) average (pairwise) cross-correlation of monthly returns with the base set assets; and, (4) serial correlation of monthly returns. The objective is to determine whether any of these four characteristics explain asset contribution to the momentum strategy. Using dividend/split-adjusted monthly prices for the above 31 asset class proxies as available during July 2002 through November 2014 (a maximum of 149 months), we find that:

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Weekly Summary of Research Findings: 12/15/14 – 12/19/14

Below is a weekly summary of our research findings for 12/15/14 through 12/19/14. 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.

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

When Consensus Earnings Forecast and Stock Return Diverge

Do changes in consensus analyst earnings forecasts that disagree with contemporaneous stock returns signal exploitable mispricings? In their November 2014 paper entitled “To Follow or Not to Follow – An Analysis of the Profitability of Portfolio Strategies Based on Analyst Consensus EPS Forecasts”, Rainer Baule and Hannes Wilke investigate the power of a variable that relates consensus earnings forecast momentum to stock price momentum to predict stock returns. Specifically, the variable is the ratio of (1+change in consensus earnings forecast) to (1+stock return) over the last six months. Their consensus earnings forecast metric is a rolling average of consensus estimates for the current and next years weighted according to proximity of the current-year forecast to the end of the firm’s fiscal year (for example, three months before the end of the fiscal year, the rolling 12-month metric is 3/12 of the forecast for the current year plus 9/12 of the forecast for next year). They measure predictive power via a portfolio that is each month long (short) the fifth of stocks with the highest (lowest) last-month variable values. They evaluate both raw excess portfolio performance (relative to the risk-free rate) and four-factor portfolio alpha (adjusting for market, size, book-to-market and momentum factors). They limit the stock universe to the widely covered and very liquid components of the S&P 100 Index. Using monthly analyst consensus earnings forecasts and total returns for S&P 100 stocks during February 1978 through December 2013 (a total of 278 stocks listed for at least one month),  they find that: Keep Reading

Stock Returns Around Christmas

Does the Christmas holiday, a time of putative good will toward all, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the five trading days before and the five trading days after the holiday. Using daily closing levels of the S&P 500 index for 1950-2013 (64 events), we find that: Keep Reading

Interplay of the Dollar, Gold and Oil

What is the interplay among investable proxies for the U.S. dollar, gold and crude oil? Do changes in the value of the dollar lead those in hard assets? To investigate, we relate the return series of three exchange-traded funds: (1) the futures-based PowerShares DB US Dollar Index Bullish (UUP); (2) the spot-based SPDR Gold Shares (GLD); and, (3) the spot-based United States Oil (USO). Using monthly, weekly and daily prices for these funds during March 2007 (limited by inception of UUP) through November 2014 (93 months), we find that: Keep Reading

Inflation Forecast Update

The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for November 2014. The actual total (core) inflation rate for November is lower than (lower than) forecasted.

The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

Equal Weighting vs. All Feasible Long-only Mean-variance Optimals

Is equal weighting (1/n) of portfolio components a good choice? In their November 2014 paper entitled “Is 1/n Really Better Than Optimal Mean-Variance Portfolio?”, Woo Chang Kim, Yongjae Lee and William Ziemba assess 1/n weighting by comparing its performance to the performances of all feasible mean-variance optimal portfolios for different asset universes. By “all feasible,” they mean many long-only mean-variance optimal portfolios generated by randomly picking the estimated future return-to-variance ratios for assets within a universe. They use Sharpe ratio to measure portfolio performance. They consider  10 asset universes: 10 U.S. equity sectors; 10 U.S. equity industries; eight country equity indexes; three U.S. equity factor portfolios; six U.S. equity styles; 25 U.S. equity styles; 100 U.S. equity styles; 250 large-capitalization U.S. stocks; 250 medium-capitalization U.S. stocks; and, 250 small-capitalization U.S. stocks.They apply mostly annual rebalancing but also consider semiannual and quarterly rebalancing for the three stock universes. They also test 1/n versus capitalization weighting for seven of the 10 universes. Using returns for specified assets at the tested rebalancing frequencies with sample start dates as early as July 1963 and end dates as late as June 2014, they find that: Keep Reading

Usefulness of P/E10 as Stock Market Return Predictor

Is P/E10 (or Cyclically Adjusted Price-Earnings ratio, CAPE) a useful indicator of U.S. stock market valuation? P/E10, as calculated in Robert Shiller’s data set, is the ratio of the inflation-adjusted S&P Composite Index level to the average monthly inflation-adjusted 12-month trailing earnings of index companies over the previous ten years. To investigate its usefulness, we consider in-sample regression and ranking and cumulative performance tests. Using Robert Shiller’s monthly estimates of the nominal and real S&P Composite Index (calculated as average of daily closes during the month), associated dividends, 12-month trailing real earnings and long-term interest rate as available during January 1871 through November 2014, we find that: Keep Reading

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Editor Archive Picks

Expected Volatility of Stock Market Volatility as a Predictor

S&P 500 Index options data imply expected S&P 500 Index volatility (VIX) over the next month. In turn, VIX futures options data imply expected volatility of VIX (VVIX) over the next month. Does VVIX predict stock index option and VIX option returns? In their September 2014 paper entitled “Volatility-of-Volatility Risk”, Darien Huang and Ivan Shaliastovich investigate whether VVIX represents a time-varying risk affecting: (1)…

Exploiting the Predictability of Volatility

There is a stream of research finding that asset price volatility is much more predictable than returns. Is there a way to extract economically meaningful gains from the predictability of volatility. In his March 2010 paper entitled “Alpha Generation and Risk Smoothing using Volatility of Volatility” (the National Association of Active Investment Managers’ 2010 Wagner Award winner), Tony Cooper investigates dynamic leverage as…

Unexpected Market Volatility as a Market Return Predictor

Do upside (downside) market volatility surprises scare investors out of (draw investors into) the stock market? In the November 2013 version of his paper entitled “Dynamic Asset Allocation Strategies Based on Unexpected Volatility”, Valeriy Zakamulin investigates the ability of unexpected stock market volatility to predict future market returns. He calculates stock market index volatility for a month using daily returns. He then regr…

Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection

…An Heuristic Approach”, Wouter Keller and Hugo van Putten investigate the effects of combining momentum, volatility and correlation selection criteria to form an equally weighted portfolio of the three best funds from a set of mutual fund proxies for seven asset classes, as follows: To follow trend, rank funds from highest to lowest lagged total return (relative momentum). To suppress volatility, rank funds from lowest to highest volatili…

Global Low-volatility Stock Portfolio Performance Predictors

Are there times when investors should avoid low-volatility stocks? In their August 2014 paper entitled “Tactical Timing of Low Volatility Equity Strategies”, Sanne De Boer and James Norman investigate which factors predict the performance of low-volatility stocks relative to a capitalization-weighted index globally since 1980. They focus on two concerns: (1) will low-volatility stocks perform poorly when they are relatively expensive…

Popular Articles

    Models, Trading Calendar and Momentum Strategy Updates

    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. More

    Inflation Forecast Update

    The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for November 2014. The actual total (core) inflation rate for November is lower than (lower than) forecasted. The new actual and forecasted inflation rates will flow into Real Earnings Yield Model projections at the end of the month.

    Preliminary Momentum Strategy Update

    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 More

    A Few Notes on Dual Momentum Investing

    In the preface to his 2015 book entitled Dual Momentum Investing: An Innovative Strategy for Higher Returns with Lower Risk, author Gary Antonacci states: “We need a way to earn long-term above-market returns while limiting our downside exposure. This book shows how momentum investing can make that desirable outcome a reality. …the academic community now accepts momentum as the More

    Martin Zweig’s Four Percent Model

    A reader inquired about the validity of Martin Zweig’s Four Percent Model, which states (from pages 93-94 of the 1994 version of Martin Zweig’s Winning on Wall Street): “The Four Percent Model for the stock market works as follows. First, It uses the Value Line Composite Index…an unweighted price index of approximately seventeen hundred stocks… More

    Stock Returns Around Christmas

    Does the Christmas holiday, a time of putative good will toward all, give U.S. stock investors a sense of optimism that translates into stock returns? To investigate, we analyze the historical behavior of the S&P 500 Index during the five trading days before and the five trading days after the holiday. Using daily closing levels More

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Current Momentum Winners

ETF Momentum Signal
for December 2014 (Final)

Momentum ETF Winner

Second Place ETF

Third Place ETF

Gross Momentum Portfolio Gains
(Since August 2006)
Top 1 ETF Top 2 ETFs
212% 229%
Top 3 ETFs SPY
219% 88%
Strategy Overview
Stock Market Projection

Projected change in S&P 500 Index as of market close on 12/19/14…

12-19-14

For elaboration, go to Market Models or the detailed descriptions of the Real Earnings Yield (REY) Model and the Reversion-to-Value (RTV) Model.

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