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Latest Market Research Articles

Sources of Active Equity Mutual Fund Risk

Are the sources of active mutual fund risk mostly common (systematic) or unique (idiosyncratic)? In his July 2014 paper entitled “Components of Portfolio Variance: R2, SelectionShare and TimingShare”, Anders Ekholm decomposes mutual fund return variance (risk) into three sources: (1) passive systematic factor exposure (R-squared); (2) active security selection or stock picking (SelectionShare); and, (3) active systematic factor timing (TimingShare). He demonstrates estimation of these three components based on mutual fund returns (reflecting daily manager actions) rather than holdings (known only via quarterly snapshots). He employs the widely used four-factor (market, size, book-to-market, momentum) model of stock returns to define systematic risk. Using daily returns for a broad sample of actively managed U.S. equity mutual funds and for the four factors during 2000 through 2013, he finds that: Keep Reading

Composite Stock Market Valuation Model

Is there some better predictor of long-term stock market return than the widely cited cyclically adjusted price-earnings ratio (P/E10 or CAPE)? In the July 2014 version of his paper entitled “Forecasting Equity Returns: An Analysis of Macro vs. Micro Earnings and an Introduction of a Composite Valuation Model”, Stephen Jones compares how well several fundamental and economic factors predict real long-term (10-year) equity market total return, with focus on Market Value/Gross Domestic Product (MV/GDP). He compares the predictive power of MV/GDP to those of P/E10 and Tobin’s q. He then constructs a multi-variable forecasting model that includes MV/GDP, a demographic metric and personal income-related variables. Using U.S. data since 1954 for different input variables, he finds that: Keep Reading

Inflation Forecast Update

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

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

Cyclical Behaviors of Size, Value and Momentum in UK

Do the behaviors of the most widely accepted stock market factors (size, book-to-market or value, and momentum) vary with the economic trend? In the June 2014 version of their paper entitled “Macroeconomic Determinants of Cyclical Variations in Value, Size and Momentum premium in the UK”, Golam Sarwar, Cesario Mateus and Natasa Todorovic examine differences in the sensitivities of UK equity market size, value and momentum factor returns (premiums) to changes in broad and specific economic variables. They define the broad economic state each month as upturn (downturn) when the OECD Composite Leading Indicator for the UK increases (decreases) that month. They also consider contributions of six specific variables to economic trend: GDP growth; unexpected inflation (change in CPI); interest rate (3-month UK Treasury bill yield); term spread (10-year UK Treasury bond yield minus 3-month UK Treasury bill yield); credit spread (Moody’s U.S. BBA yield minus 10-year UK government bond yield); and, money supply growth. They lag economic variables by one or two months to align their releases with stock market premium measurements. Using monthly UK size, value and momentum factors and economic data during July 1982 through December 2012, they find that: Keep Reading

Value-Momentum Switching Based on Value Premium Persistence

Can investors exploit monthly persistence in the value premium for U.S. stocks? In his February 2014 paper entitled “Exploiting Factor Autocorrelation to Improve Risk Adjusted Returns”, Kevin Oversby investigates whether investors can exploit the fact that the Fama-French model high-minus-low (HML) value factor exhibits positive monthly autocorrelation (persistence). The HML factor derives from the difference in performance between portfolios of stocks with high and low book-to-market ratios. Prior published research indicates that the value premium concentrates in small firms, so he focuses on stocks with market capitalizations below the NYSE median. His test strategies each month invest in capitalization-weighted small value (small growth or small momentum) Fama-French portfolios when the prior-month sign of the HML factor is positive (negative). The strategies additionally retreat to a risk-free asset (such as U.S. Treasury bills) if the prior-month return for the test strategy is negative. Using HML factor values and monthly portfolio returns for small value, small growth and small momentum Fama-French portfolios, he finds that: Keep Reading

Weekly Summary of Research Findings: 7/14/14 – 7/18/14

Below is a weekly summary of our research findings for 7/14/14 through 7/18/14. 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

Ultimate Stock-Pickers vs. Luck

Are Morningstar’s Ultimate Stock-Pickers good stock pickers? In his June 2014 paper entitled “Using Random Portfolios to Evaluate the Performance of the Ultimate Stock-Pickers Index”, Stefaan Pauwels compares the quarterly volatility-adjusted performances of the Morningstar Ultimate Stock-Pickers (USP) top buys, top holdings and top sells to those of many randomly generated (zero-skill) portfolios. Morningstar specifies USP members as fund managers across a range of equity styles with: (1) tenure longer than average within style category; and, (2) 1-year, 3-year, 5-year and 10-year returns exceeding that of the broad equity market. Each quarter, Morningstar generates lists of top ten USP buys, holdings and sells. The study compares the volatility-adjusted returns of these equally weighted lists to those of 1,000 equally weighted portfolios of ten stocks randomly selected each quarter from the S&P 500 Index. He performs volatility adjustment by dividing quarterly return by the standard deviation of daily returns during the quarter. Using quarterly USP lists from the end of November 2010 through early September 2013 and contemporaneous quarterly total returns and daily returns for associated stocks and the stocks in the S&P 500 Index, he finds that:

Keep Reading

Exploitation of Technical Analysis by Hedge Funds?

Do hedge fund managers who use technical analysis beat those who do not? In their May 2014 paper entitled “Sentiment and the Effectiveness of Technical Analysis: Evidence from the Hedge Fund Industry”, David Smith, Na Wang, Ying Wang and Edward Zychowicz examine the relative performance of users and non-users of technical analysis among hedge fund managers in different sentiment environments. They hypothesize that short-selling constraints prevent market correction of mispricings when sentiment is high (overly optimistic), but not when sentiment is low (overly pessimistic). Discovery of mispricings via technical analysis may therefore be more effective when sentiment is high. To test their hypothesis, they compare the performance of hedge funds that report using technical analysis to that of hedge funds that do not, with focus on the state of market sentiment. They define the market sentiment state as high or low depending on whether the monthly Baker-Wurgler market sentiment measure is above or below its full-sample median. Using end-of-period status on use/non-use of technical analysis and monthly returns for 3,290 live and 1,845 dead funds from the Lipper TASS hedge fund database and monthly market sentiment data during January 1994 through December 2010, they find that: Keep Reading

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Editor Archive Picks

Stocks versus Bonds as Investment Horizon Lengthens

Should investors believe in the superiority of stocks for the long run and bonds for the short run? In his December 2011 paper entitled “Stocks, Bonds, Risk, and the Holding Period: An International Perspective”, Javier Estrada examines how the absolute and relative risks of stocks and bonds evolve as investment horizon grows (time diversification). Considering both annual and cumulative returns and various measures of variability/ri…

Future Stock Market Returns and P/E10

Is price-to-earnings ratio cyclically adjusted via a 10-year average (CAPE, or P/E10) a good predictor of future stock market performance? In his October 2012 paper entitled “The Enhanced Risk Premium Factor Model & Expected Returns”, Javier Estrada examines three simple models that generate 10-year annualized stock market expected return (ER) based on P/E10 and the risk-free rate (Rf). Specifically, the three models hypothesize…

Lifecycle Funds Guard Against Upside Volatility?

Are target‐date (glidepath) funds that periodically decrease (increase) allocation to stocks (bonds and cash) as the investor ages competitive with alternative strategies? In his February 2013 paper entitled “The Glidepath Illusion: An International Perspective”, Javier Estrada evaluates three alternative types of strategies, all based on a working life of 40 years with annual retirement fund contributions of $1,000 (inflation‐adjust…

Long-term Investors: Focus on Terminal Wealth?

Should long-term investors focus on terminal wealth and ignore interim volatility? In his August 2013 paper entitled “Rethinking Risk”, Javier Estrada compares distributions of terminal wealths for $100 initial investments in stocks or bonds over investment horizons of 10, 20 or 30 years. He utilizes mean, median, tail (extreme 1%, 5% and 10%) and risk-adjusted performance metrics. He employs real returns for 19 country markets adjus…

Applying Beta to Portfolios of ETFs

Is beta an effective tool for selecting exchange-traded funds (ETF)? In their October 2010 paper entitled “Black Swans, Beta, Risk, and Return”, Javier Estrada and Mari­a Vargas investigate the usefulness of beta as a metric for constructing portfolios of country and industry ETFs. They use the MSCI world market index (consisting of developed markets only before 1988 and both developed and emerging markets thereafter) as the beta ref…

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 June 2014. Updated Market Models backtest charts and the market valuation metrics map based on data available through June 2014. We have updated the Trading Calendar to incorporate data for June 2014. We have updated More

    Inflation Forecast Update

    The Inflation Forecast now incorporates actual total and core Consumer Price Index (CPI) data for June 2014. The actual total (core) inflation rate for June is a little higher than (slightly 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 July 2014. The differences in past returns between the third and fourth places is small enough that they could change order by the close. The first and second places are unlikely to change.

    A Few Notes on Global Value

    In the introduction to his 2014 book entitled Global Value: How to Spot Bubbles, Avoid Market Crashes, and Earn Big Returns in the Stock Market, author Mebane Faber, ponders: “Can we or can’t we predict when a bubble is occurring? Below we [try] to find an objective way to identify bubbles, avoid their popping, and invest in their aftermath.” More

    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 More

    DJIA-Gold Ratio as a Stock Market Indicator

    A reader requested a test of the following hypothesis [presented by Simon Maierhofer, co-founder of ETFguide] from the article “Gold’s Bluff – Is a 30 Percent Drop Next?”: “Ironically, gold is more than just a hedge against market turmoil. Gold is actually one of the most accurate indicators of the stock market’s long-term direction. The Dow More

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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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