Momentum Investing

How should investors think about surprises in economic data? In their March 2022 paper entitled “Caught by Surprise: How Markets Respond to Macroeconomic News”, Guido Baltussen and Amar Soebhag devise and investigate a real-time aggregate measure of surprises in economic (not financial) variables around the world. Each measurement for each variable consists of release date/time, initial as-released value, associated consensus (median) forecast, number and standard deviation of individual forecasts and any revision to the previous as-released value across U.S., UK, the Eurozone and Japan markets from the Bloomberg Economic Calendar. They classify variables as either growth-related or inflation-related. They apply recursive principal component analysis to aggregate individual variable surprises separately into daily nowcasts of initial growth-related and inflation-related announcement surprises and associated revision surprises. They investigate the time series behaviors of these nowcasts and then examine their interactions with returns for four asset classes:

1. Stocks via prices of front-month futures contracts rolled the day before expiration for S&P 500, FTSE 100, Nikkei 225 and Eurostoxx 50 indexes.
2. Government bonds via prices of front-month futures contracts rolled the day before first notice on U.S., UK, Europe and Japan 10-year bonds.
3. Credit via returns on 5-year credit default swaps for U.S. and Europe investment grade and high yield corporate bond indexes.
4. Commodities via excess returns for the Bloomberg Commodity Index.

Specifically, they test an investment strategy that takes a position equal to the 1-day lagged value of the growth surprise nowcast or the inflation surprise nowcast on the last trading day of each month. They pool regions within an asset class by equally weighting regional markets. Using daily as-released data for 191 economic variables across global regions and the specified monthly asset class price inputs during March 1997 through December 2019, they find that: Keep Reading

A subscriber suggested comparing intrinsic momentum (IM), also called absolute momentum and time series momentum, to simple moving average (SMA) as alternative signals for equity market entry and exit. To investigate across a wide variety of economic and market conditions, we measure the long run performances of entry and exit signals from IMs over past intervals of one to 12 months (IM1 through IM12) and SMAs ranging from 2 to 12 months (SMA2 through SMA12). We consider two cases for IM signals and one case for SMA signals, as applied to the S&P 500 Index as a proxy for the stock market and the 3-month U.S. Treasury bill (T-bill) as a proxy for cash (the risk-free rate). The three rule types are therefore:

1. IMs Case 1 – in stocks (cash) when past index return is positive (negative).
2. IMs Case 2 – in stocks (cash) when average monthly past index return is above (below) average monthly T-bill yield over the same interval.
3. SMAs – in stocks (cash) when the index is above (below) the SMA.

We estimate S&P 500 Index monthly total returns using quarterly dividend yield calculated from Shiller data for March, June, September and December. This estimation does not affect index timing signals. We focus on net compound annual growth rate (CAGR), maximum drawdown (MaxDD) and annual Sharpe ratio as key performance metrics, with baseline stocks-cash switching frictions 0.2%. We use buying and holding the S&P 500 Index (B&H) as a benchmark. Using monthly closes of the S&P 500 Index during December 1927 through February 2022 (94 years), and contemporaneous monthly index dividend and T-bill yields, we find that:

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Which of the explanations offered in past research best explains, and potentially justifies belief in persistence of, stock return momentum? In the February 2022 revision of their paper entitled “What Explains Momentum? A Perspective From International Data”, Amit Goyal, Narasimhan Jegadeesh and Avanidhar Subrahmanyam test alternative explanations for momentum as hypothesized in earlier studies on a recent international dataset. They each month calculate momentum for each stock as its return over the last 12 months excluding the most recent month, standardized by subtracting the average momentum for all stocks of the same country. They then rank stocks into tenths (deciles) based on country-standardized momentum to form a value-weighted hedge portfolio that is long the winner decile and short the loser decile. They employ hedge portfolio returns to test explanatory powers of measurable proxies for the following rationales:

• Overconfidence that escalates with returns.
• Generally slow diffusion of news among investors.
• Anchoring bias, specified as ratio of current price to 52-week high.
• Frog-in-the-pan (investors underreact to small bits of news that arrive gradually due to limited attention, but react appropriately to shocking news).
• Stock risk that varies with past returns (high for winners and low for losers).

They also examine whether hedge portfolio profitability varies across broad market past return and volatility states. Using data groomed to exclude obvious errors for both listed and delisted stocks across 22 non-U.S. developed markets and 27 emerging markets during 1993 through 2020, they find that: Keep Reading

“SACEMS with Momentum Breadth Crash Protection” evaluates in depth the potential of a simple momentum breadth rule to improve performance of the Simple Asset Class ETF Momentum Strategy (SACEMS). This rule forces the model to all cash when fewer than some threshold of the non-cash SACEMS assets have positive returns over a specified lookback interval. Do major findings of that evaluation still hold? To update, we repeat some of the analyses with the minor changes since made to SACEMS plus recent data. We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the Top 1, equal-weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We look at all possible momentum breadth thresholds for the baseline SACEMS lookback interval. We then consider lookback intervals ranging from one to 12 months for a specific momentum breadth threshold. Using monthly dividend-adjusted closing prices for SACEMS assets and the T-bill yield during February 2006 through February 2022, we find that:

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A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equal-weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. We focus on monthly return, monthly volatility and compound annual growth rate (CAGR) as key performance metrics. In a robustness test for the EW Top 2 and EW Top 3 portfolios, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for SACEMS assets since February 2006 and monthly S&P 500 Index level since September 2005, all through January 2022, we find that:

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In response to “Combining Defensive-in-May and Sector Reversion”, a subscriber requested testing of a strategy combining seasonal effects (cyclical sectors during November through April and defensive sectors during May through October) and sector momentum. Cyclical and defensive choices are:

• Cyclical:
  • Materials Select Sector SPDR (XLB)
  • Industrial Select Sector SPDR (XLI)
  • Technology Select Sector SPDR (XLK)
  • Consumer Discretionary Select SPDR (XLY)
  • Vanguard Small-Cap Value Index Fund (VBR)
• Defensive:
  • Consumer Staples Select Sector SPDR (XLP)
  • Utilities Select Sector SPDR (XLU)
  • Health Care Select Sector SPDR (XLV)
  • Vanguard Dividend Appreciation Index Fund (VIG)
  • iShares 7-10 Year Treasury Bond (IEF)

At the end of each October, the strategy buys the one cyclical fund with the highest return over some past interval (betting on momentum). At the end of each April, the strategy sells the cyclic fund and buys the one defensive fund with the highest return over the past interval (again, betting on momentum). For convenience, we use a 6-month lookback interval to rank funds. We use buy-and-hold SPDR S&P 500 (SPY) as a benchmark. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during October 2006 (limited by availability of VIG) through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

Inspired by “The iM Seasonal ETF Switching Strategy”, a subscriber requested testing of a strategy combining seasonal effects (cyclical sectors during November through April and defensive sectors during May through October) and sector reversion. Cyclical and defensive choices are:

• Cyclical:
  • Materials Select Sector SPDR (XLB)
  • Industrial Select Sector SPDR (XLI)
  • Technology Select Sector SPDR (XLK)
  • Consumer Discretionary Select SPDR (XLY)
  • Vanguard Small-Cap Value Index Fund (VBR)
• Defensive:
  • Consumer Staples Select Sector SPDR (XLP)
  • Utilities Select Sector SPDR (XLU)
  • Health Care Select Sector SPDR (XLV)
  • Vanguard Dividend Appreciation Index Fund (VIG)
  • iShares 7-10 Year Treasury Bond (IEF)

At the end of each October, the strategy buys the one cyclical fund with the lowest return over some past interval (betting on reversion). At the end of each April, the strategy sells the cyclic fund and buys the one defensive fund with the lowest return over the past interval (again, betting on reversion). For convenience, we use a 6-month lookback interval to rank funds. We use buy-and-hold SPDR S&P 500 (SPY) as a benchmark. We focus on semiannual return statistics, along with compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using semiannual dividend-adjusted prices for the selected funds during October 2006 (limited by availability of VIG) through October 2021 (defining the first and last available semiannual intervals), we find that: Keep Reading

Do widely accepted equity factor premiums exist in data older than generally employed in academic studies? In their November 2021 paper entitled “The Cross-Section of Stock Returns before 1926 (And Beyond)”, Guido Baltussen, Bart van Vliet and Pim van Vliet look for some of the most widely accepted factor premiums in a newly assembled sample of U.S. stocks spanning January 1866 through December 1926 (61 years of additional and independent data). Specifically, they look at: size as measured by market capitalization; value as measured by dividend yield (strongly associated with earnings during the sample period); stock price momentum from 12 months ago to one month ago; short-term (1-month) return reversal; and, risk as measured by market beta. They use only those stocks which trade frequently and apply liquidity/data quality filters. To measure factor premiums, they each month for each factor:

• Regress next-month stock return versus stock factor value and compute slopes of the relationship.
• Reform a value-weighted hedge portfolio that is long (short) stocks with high (low) expected returns based on factor values to measure: (1) average factor portfolio gross return; and, (2) gross factor (CAPM) alphas and betas based on regression of factor portfolio excess return versus market excess return.

They further investigate economic explanations of factor premiums and test machine learning methods found successful with recent data. Using monthly prices, dividends and market capitalizations for 1,488 stocks in the new database, they find that:

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Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through September 2021, we find that: Keep Reading