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Mutual Fund Hot Hand Performance

A subscriber inquired about a “hot hand” strategy that each year picks the top performer from a family of diversified equity mutual funds (not including sector funds) and holds that winner the next year. To evaluate this strategy, we consider Vanguard diversified equity mutual funds with inceptions no later than September 2011. The test period is the lifetime of SPDR S&P 500 (SPY), which serves as a benchmark. We assume no costs or holding period constraints/delays for switching from one fund to another. We also simplify calculations by assuming that end-of-year “hot hand” fund identification and fund switches occur simultaneously (in other words, we can accurately rank mutual funds one day before the end of the year). Using monthly total returns for SPY and for Vanguard diversified equity mutual funds as available during December 1992  through December 2018, we find that:

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Book-to-Market Volatility as Stock Return Predictor

Do investors systematically undervalue stocks that have relatively large book-to-market fluctuations? In their December 2018 paper entitled “The Value Uncertainty Premium”, Turan Bali, Luca Del Viva, Menna El Hefnawy and Lenos Trigeorgis test whether book-to-market volatility relates positively to future returns. They specify book-to-market volatility as standard deviation of daily estimated book-to-market ratios divided by their average over the past 12 months. They estimate book value using the most recent quarterly balance sheet plus analyst forecasts of net income minus expected dividends since that quarter. They lag all accounting data three months and analyst forecasts one month to avoid look-ahead bias. They then each month starting January 1986 rank stocks into tenths (deciles) by book-to-market volatility and reform a hedge portfolio that is long (short) the highest (lowest) decile. Using monthly and daily returns and firm accounting data for a broad sample of non-financial U.S. stocks and data for a large set of control variables during January 1985 through December 2016, they find that:

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Stock Market and the Super Bowl

Investor mood may affect financial markets. Sports may affect investor mood. The biggest mood-mover among sporting events in the U.S. is likely the National Football League’s Super Bowl. Is the week before the Super Bowl especially distracting and anxiety-producing? Is the week after the Super Bowl focusing and anxiety-relieving? Presumably, post-game elation and depression cancel between respective fan bases. Using past Super Bowl dates since inception and daily/weekly S&P 500 Index levels for 1967 through 2018 (52 events), we find that: Keep Reading

Weekly Summary of Research Findings: 1/22/19 – 1/25/19

Below is a weekly summary of our research findings for 1/22/19 through 1/25/19. 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

Stopping Tests after Lucky Streaks?

Might purveyors of trading strategies be presenting performance results biased by stopping them when falsely successful? In other words, might they be choosing lucky closing conditions for reported positions? In the December 2018 revision of their paper entitled “p-Hacking and False Discovery in A/B Testing”, Ron Berman, Leonid Pekelis, Aisling Scott and Christophe Van den Bulte investigate whether online A/B experimenters bias results by stopping monitored commercial (marketing) experiments based on latest p-value. They hypothesize that such a practice may exist due to: (1) poor training in statistics; (2) self-deception motivated by desire for success; or, (3) deliberate deception for selling purposes. They employ regression discontinuity analysis to estimate whether reaching a particular p-value causes experimenters to end their tests. Using data from 2,101 online A/B experiments with daily tracking of results during 2014, they find that:

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Coverage Ratio and Asymmetric Utility for Retirement Portfolio Evaluation

Failure rate, the conventional metric for evaluating retirement portfolios, does not distinguish between: (1) failures early versus late in retirement; or, (2) small and large surpluses (bequests). Is there a better way to evaluate retirement portfolios? In their December 2018 paper entitled “Toward Determining the Optimal Investment Strategy for Retirement”, Javier Estrada and Mark Kritzman propose coverage ratio, plus an asymmetric utility function that penalizes shortfalls more than it rewards surpluses, to evaluate retirement portfolios. They test this approach in 21 countries and the world overall. Coverage ratio is number of years of withdrawals supported by a portfolio during and after retirement, divided by retirement period. The utility function increases at decreasing rate (essentially logarithmic) as coverage ratio rises above one and decreases sharply (linearly with slope 10) as it falls below one. They focus on a 30-year retirement with 4% initial withdrawal rate and annual inflation-adjusted future withdrawals. The portfolio rebalances annually to target stocks and bonds allocations. They consider 11 target stocks-bonds allocations ranging from 100%-0% to 0%-100% in increments of 10%. When analyzing historical returns, the first (last) 30-year period is 1900-1929 (1985-2014), for a total of 86 (overlapping) periods. When using simulations, they draw 25,000 annual real returns for stocks and bonds from two uncorrelated normal distributions. For bonds, all simulation runs assume 2% average real annual return with 3% standard deviation. For stocks, simulation runs vary average real annual return and standard deviation for sensitivity analysis. Using historical annual real returns for stocks and bonds for 21 countries and the world overall during 1900 through 2014 from the Dimson-Marsh-Staunton database, they find that: Keep Reading

Should the “Anxious Index” Make Investors Anxious?

Since 1990, the Federal Reserve Bank of Philadelphia has conducted a quarterly Survey of Professional Forecasters. The American Statistical Association and the National Bureau of Economic Research conducted the survey from 1968-1989. Among other things, the survey solicits from economic experts probabilities of U.S. economic recession (negative GDP growth) during each of the next four quarters. The survey report release schedule is mid-quarter. For example, the release date of the fourth quarter 2018 report is November 13, 2018, with forecasts for the four quarters of 2019. The “Anxious Index” is the probability of recession during the next quarter. Are these forecasts meaningful for future U.S. stock market returns? Rather than relate the probability of recession to stock market returns, we instead relate one minus the probability of recession (the probability of good times). If forecasts are accurate, a relatively high (low) forecasted probability of good times should indicate a relatively strong (weak) stock market. Using survey results and quarterly S&P 500 Index levels (on survey release dates as available, and mid-quarter before availability of release dates) from the fourth quarter of 1968 through the fourth quarter of 2018 (201 surveys), we find that:

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Back Doors in Betting Against Beta?

Do unconventional portfolio construction techniques obscure how, and how well, betting against beta (BAB) works? In their November 2018 paper entitled “Betting Against Betting Against Beta”, Robert Novy-Marx and Mihail Velikov revisit the BAB factor, focusing on interpretation of three unconventional BAB construction techniques:

  1. Rank weighting of stocks – BAB employs rank weighting rather than equal or value weighting, with each stock in high and low estimated beta portfolios weighted proportionally to the difference between its estimated beta rank and the median rank.
  2. Hedging by leveraging – BAB seeks market neutrality by deleveraging (leveraging) the high (low) beta portfolio based on estimated betas rather than borrowing to buy the market portfolio to offset BAB’s short market tilt.
  3. Novel beta estimation – BAB measures stock betas by combining market correlations based on five years of overlapping 3-day returns with volatilities based on one year of daily returns, rather than using slope coefficients of daily stock returns versus daily market returns.

Based on mathematical analysis and empirical results using returns for a broad sample of U.S. stocks during January 1968 through December 2017, they find that: Keep Reading

Weekly Summary of Research Findings: 1/14/19 – 1/18/19

Below is a weekly summary of our research findings for 1/14/19 through 1/18/19. 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

Adjust the SACEMS Lookback Interval?

The Simple Asset Class ETF Momentum Strategy (SACEMS) each month picks winners based on total return over a specified ranking (lookback) interval from the following eight asset class exchange-traded funds (ETF), plus cash:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Emerging Markets Index (EEM)
iShares MSCI EAFE Index (EFA)
SPDR Gold Shares (GLD)
iShares Russell 2000 Index (IWM)
SPDR S&P 500 (SPY)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

This set of ETFs offers: (1) opportunities to capture momentum across global developed and emerging equity markets, large and small U.S. equities, bonds and commodities; (2) gold and cash as safe havens; (3) histories long enough for backtesting across multiple market environments; and, (4) simplicity of computation and recognition of the trade-off between number of ETFs and trading frictions. As historical data accumulate, we can estimate an increasingly robust optimal lookback interval. Should we change the baseline lookback interval at this point? To investigate, we revisit relevant analyses and conduct further robustness tests, with focus on the equal-weighted (EW) Top 3 SACEMS portfolio. Using monthly dividend-adjusted closing prices for asset class proxies and the yield for Cash during February 2006 (when all ETFs are first available) through December 2018, we find that: Keep Reading

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