Momentum Investing

How well do mutual funds exploit theoretical (academic) equity factor premiums, and how well do investors exploit such exploitation? In their January 2019 paper entitled “Factor Investing from Concept to Implementation”, Eduard Van Gelderen, Joop Huij and Georgi Kyosev examine: (1) how performances of mutual funds that target equity factor premiums (low beta, size, value, momentum, profitability, investment) compare to that of funds that do not; and, (2) flow-adjusted performances, indicating how much of any outperformance accrues to fund investors. They classify funds empirically based on factor exposures. Using monthly returns and total assets and quarterly turnover and expense ratios for 3,109 actively managed long-only U.S. equity mutual funds with assets over $5 million (1,334 dead and 1,775 live) since January 1990 and for 4,859 (2,000 dead and 2,859 live) similarly specified global mutual funds since January 1991, all through December 2015, along with contemporaneous monthly equity factor returns, they find that: Keep Reading

In response to “Robustness of SACEMS Based on Sharpe Ratio”, a subscriber asked whether Martin ratio might work better than raw returns and Sharpe ratio for ranking assets within the Simple Asset Class ETF Momentum Strategy (SACEMS), which each month selects the best performers over a specified lookback interval from among 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)

To investigate, we focus on the SACEMS equally weighted (EW) Top 3 portfolio and compare outcomes across lookback intervals ranging from one to 12 months for the following three asset ranking metrics:

1. Raw return – cumulative total return over the lookback interval.
2. Sharpe ratio (SR) – average daily excess return (asset return minus T-bill return) divided by standard deviation of daily excess returns over the lookback interval, with months approximated as 21 trading days. We set SR for Cash at zero (though it is actually zero divided by zero).
3. Martin ratio (MR) – average daily excess return divided by the Ulcer Index calculated from daily returns over the lookback interval, with months again approximated as 21 trading days.

We employ gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) to compare ranking metrics. 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

In response to “SACEMS with SMA Filter”, a subscriber suggested instead crash protection via momentum breadth (proportion of assets with positive momentum) by:

1. Switching to 100% cash when fewer than four of eight Simple Asset Class ETF Momentum Strategy (SACEMS) non-cash assets have positive past returns.
2. Scaling from cash into winners when four to eight risk assets have positive past returns (no cash for eight).
3. Replacing U.S. Treasury bills (T-bills), a proxy for broker money market rates, with iShares Barclays 7-10 Year Treasury Bond (IEF) as “Cash.”

To investigate, we each month rank assets from the following SACEMS universe based on total returns over a specified lookback interval. We also each month measure momentum breadth for the eight non-cash assets using the same lookback interval.

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)

While emphasizing the suggested momentum breadth crash protection threshold, we look at all possible thresholds. While emphasizing a baseline lookback interval, we consider lookback intervals ranging from one to 12 months for the suggested momentum breadth threshold. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the equal-weighted (EW) Top 3 SACEMS portfolio, but also look at Top 1 and EW Top 2. We also look at EW Top 3 portfolio turnover. Using monthly dividend-adjusted closing prices for SACEMS assets and IEF and the T-bill yield during February 2006 (the earliest all ETFs are available) through December 2018, we find that: Keep Reading

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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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

Are moving averages or intrinsic (time series) momentum theoretically better for following trends in asset prices? In their November 2018 paper entitled “Trend Following with Momentum Versus Moving Average: A Tale of Differences”, Valeriy Zakamulin and Javier Giner compare from a theoretical perspective effectiveness of four popular trend following rules:

1. Intrinsic Momentum – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the closing price at the beginning of the lookback interval.
2. Simple Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the equally weighted average closing price during the lookback interval.
3. Linear Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the linearly weighted (weights linearly increasing to the most recent) average closing price during the lookback interval.
4. Exponential Moving Average – buy (sell) when the closing price at the end of a specified lookback interval is greater (less) than the exponentially weighted (weights exponentially increasing to the most recent) average closing price during the lookback interval.

They transform these price rules into return-based versions and create a trend model as an autoregressive return process. They then explore interactions of the trading rules with the trend model. Based on this theoretical approach, they conclude that: Keep Reading

Do “bubble” stocks (those with high shorting demand and small borrowing supply) exhibit unconventional momentum behaviors? In their December 2018 paper entitled “Overconfidence, Information Diffusion, and Mispricing Persistence”, Kent Daniel, Alexander Klos and Simon Rottke examine how momentum effects for bubble stocks differ from conventional momentum effects. They each month sort stocks into groups independently as follows:

1. Momentum winners (losers) are the 30% of stocks with the highest (lowest) returns from one year ago to one month ago, incorporating a skip-month.
2. Stocks with high (low) shorting demand are those with the top (bottom) 30% of short interest ratios.
3. Stocks with small (large) borrowing supply are those with the top (bottom) 30% of institutional ownerships.

They then use intersections of these groups to reform 27 value-weighted portfolios. Bubble (constrained) stocks are those in the intersection of high shorting demand and low institutional ownership, including both momentum winners and losers. For purity, they further split bubble losers into those that were or were not also bubble winners within the past five years. Using monthly and daily returns, market capitalizations and trading volumes for a broad sample of U.S. common stocks, monthly short interest ratios and quarterly institutional ownership data from SEC Form 13F filings during July 1988 through June 2018, they find that: Keep Reading

Subscribers have asked whether risk-adjusted returns might work better than raw returns for ranking Simple Asset Class ETF Momentum Strategy (SACEMS) assets. In fact, “Alternative Momentum Metrics for SACEMS?” supports belief that Sharpe ratio beats raw returns. Is this finding strong enough to justify changing the strategy, which each month selects the best performers over a specified lookback interval from among 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)

To investigate, we update the basic comparison and conduct three robustness tests:

1. Does Sharpe ratio beat raw returns consistently across Top 1, equally weighted (EW) Top 2, EW Top 3 and EW Top 4 portfolios, and the 50%-50% SACEMS EW Top 3-Simple Asset Class ETF Value Strategy (SACEVS) Best Value portfolio?
2. Does Sharpe ratio beat raw returns consistently across different lookback intervals?
3. For multi-asset portfolios, does weighting by Sharp ratio rank beat equal weighting? In other words, do future returns behave systematically across ranks?

To calculate Sharpe ratios, we each month for each asset subtract the risk-free rate (Cash yield) from raw monthly total returns to generate monthly total excess returns over a specified lookback interval. We then calculate Sharpe ratio as average monthly excess return divided by standard deviation of monthly excess returns over the lookback interval. We set Sharpe ratio for Cash at zero (though it is actually zero divided by zero). 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

Does active stock factor exposure management boost overall portfolio performance? In their November 2018 paper entitled “Optimal Timing and Tilting of Equity Factors”, Hubert Dichtl, Wolfgang Drobetz, Harald Lohre, Carsten Rother and Patrick Vosskamp explore benefits for global stock portfolios of two types of active factor allocation:

1. Factor timing – exploit factor premium time series predictability based on economic indicators and factor-specific technical indicators.
2. Factor tilting – exploit cross-sectional (relative) attractiveness of factor premiums.

They consider 20 factors spanning value, momentum, quality and size. For each factor each month, they reform a hedge portfolio that is long (short) the equal-weighted fifth, or quintile, of stocks with the highest (lowest) expected returns for that factor. For implementation of factor timing, they consider: 14 economic indicators standardized by subtracting respective past averages and dividing by standard deviations; and, 16 technical indicators related to time series momentum, moving averages and volatilities. They suppress redundancy and noise in these indicators via principal component analysis separately for economic and technical groups, focusing on the first principal component of each group. They translate any predictive power embedded in principal components into optimal factor portfolio weights using augmented mean-variance optimization. For implementation of factor tilting, they overweight (underweight) factors that are relatively attractive (unattractive) based on valuations of factor top and bottom quintile stocks, top-bottom quintile factor variable spreads, prior-month factor returns (momentum) and volatilities of past monthly factor returns. Their benchmark portfolio is the equal-weighted combination of all factor hedge portfolios. For all portfolios, they assume: monthly portfolio reformation costs of 0.75% (1.15%) of turnover value for the long (short) side; and, annual 0.96% cost for an equity swap to ensure a balanced portfolio of factor portfolios. For monthly factor timing and tilting portfolios only, they assume an additional cost of 0.20% of associated turnover. Using monthly data for a broad sample of global stocks from major equity indexes and for specified economic indicators during January 1997 through December 2016 (4,500 stocks at the beginning and 5,000 stocks at the end), they find that: Keep Reading

Is stock price momentum an imperfect proxy for sensitivity of individual stocks to past dispersion of returns across stocks (zeta risk, or return dispersion)? In their November 2018 paper entitled “Market Risk and the Momentum Mystery”, James Kolari and Wei Liu investigate relationships between momentum and return dispersion as predictors of individual U.S. stock returns. They employ both portfolio comparisons and regression tests. For the former, their momentum portfolio is long (short) the equally weighted top (bottom) tenth, or decile, of stocks ranked on past 12-month minus one skip-week returns, reformed monthly. Their main return dispersion portfolio is long (short) the equally weighted decile of stocks with the most positive (negative) sensitivities to the dispersion of all individual daily stock returns over the past 12 months minus one skip-week, reformed monthly. Using daily and monthly returns for a broad sample of U.S. stocks priced over $5 since January 1964, and contemporaneous 1-month U.S. Treasury bill yields and monthly returns of selected stock return model factors since January 1965, all through December 2017, they find that:

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