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Momentum Investing

Do financial market prices reliably exhibit momentum? If so, why, and how can traders best exploit it? These blog entries relate to momentum investing/trading.

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Optimal Intrinsic Momentum and SMA Intervals Across Asset Classes

What are the optimal intrinsic/absolute/time series momentum (IM) and simple moving average (SMA) measurement intervals for different asset class proxies? To investigate, we use data from the Simple Asset Class ETF Momentum Strategy for 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)

For IM tests, we invest in each ETF (Cash) when its return over the past one to 12 months is positive (negative). For SMA tests, we invest in each ETF (Cash) when its price is above (below) its average monthly price over the past two to 12 months. Since SMA rules use price levels and IM rules use returns, IM measurement interval N corresponds to SMA measurement interval N+1. For example, a 6-month IM measurement uses the same start and stop points as a 7-month SMA measurement. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key metrics for comparing different IM and SMA measurement intervals since earliest ETF data availabilities based on the longest IM measurement interval. Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash over the period July 2002 (or inception if not available by then) through January 2018, we find that:

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Momentum Strategy, Value Strategy and Trading Calendar Updates

We have updated monthly Simple Asset Class ETF Momentum Strategy (SACEMS) winners and associated performance data at “Momentum Strategy”. We have updated monthly Simple Asset Class ETF Value Strategy (SACEVS) allocations and associated performance data at “Value Strategy”. We have also updated performance data for the “Combined Value-Momentum Strategy”. We have incorporated data changes as described in this morning’s preliminary update.

We have updated the “Trading Calendar” to incorporate data for January 2018.

Preliminary Momentum Strategy and Value Strategy Updates

The home page“Momentum Strategy” and “Value Strategy” now show preliminary Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) positions for February 2018. For SACEMS, past returns for the first and second positions and for the third and fourth positions are close, such that rankings could change by the close. For SACEVS, allocations are unlikely to change by the close.

An anomaly in the source data surfaced this month. Returns for December 2017 for dividend-paying ETFs changed between the end of December 2017 and the end of January 2018. It appears that data available as of the December market close did not account for dividend ex-dates during December. This anomaly has two implications:

  1. December 2017 returns previously reported for SACEMS and SACEVS (and alternatives using dividend paying ETFs) were too low. We are correcting these returns.
  2. More seriously, incorporation of December 2017 dividends causes a change in the SACEMS top three winners for December 2017, which we determine based on total returns. Since the historical SACEMS performance we present is based on fully updated backtests, the data anomaly introduces a disconnect between backtest and live portfolio performances. In this case, the backtest performs better than a live portfolio. If this issue recurs, we will consider other data management approaches.

Recall the prior data instability reported in “Simple Asset Class ETF Momentum Strategy Data Changes”. Over the long run, data instability issues may cancel with respect to live portfolio performance.

Momentum Investing in a Nutshell?

How, in a nutshell, do momentum investing strategies work? In his December 2017 paper entitled “Keep Up the Momentum”, Thierry Roncalli summarizes the nature of the momentum premium in a less mathematical way than in the previously available “Understanding the Momentum Risk Premium: An In-Depth Journey Through Trend-Following Strategies”. He distinguishes between:

  • Time-series or trend-following or intrinsic or absolute momentum (long assets with a positive past trend and short assets with a negative past trend).
  • Cross-sectional or relative or winners-minus-losers momentum (long assets that have outperformed and short assets that have underperformed relative to each other).

Based on mathematical derivations and prior research, he concludes that: Keep Reading

More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), as considered by Decision Moose, would improve performance. To investigate, we replace the iShares MSCI Emerging Markets Index (EEM) and the iShares MSCI EAFE Index (EFA) with four regional international equity exchange-traded funds (ETF). The universe of assets becomes:

PowerShares DB Commodity Index Tracking (DBC)
iShares MSCI Pacific ex Japan (EPP)
iShares MSCI Japan (EWJ)
SPDR Gold Shares (GLD)
iShares Europe (IEV)
iShares Latin America 40 (ILF)
iShares Russell 1000 Index (IWB)
iShares Russell 2000 Index (IWM)
iShares Barclays 20+ Year Treasury Bond (TLT)
Vanguard REIT ETF (VNQ)
3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from the above set of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and rough gross annual Sharpe ratio (average annual return divided by standard deviation of annual returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through December 2017, we find that: Keep Reading

Sticky SACEMS

Subscribers have suggested an alternative approach for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) designed to suppress trading by holding past winners until they fall further in the rankings than in the baseline specification. SACEMS each month picks winners from the following set of exchange-traded funds (ETF) based on total returns over a specified 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)

There are three versions of SACEMS: (1) top one of the nine ETFs (Top 1); (2) equally weighted top two (EW Top 2); and, (3) equally weighted top three (EW Top 3). To test the suggestion, we specify three “sticky” versions of SACEMS as follows:

  1. Top 1 Sticky – retains the past winner until it drops out of the top 2.
  2. EW Top 2 Sticky – retains past winners until they drop out of the top 3.
  3. EW Top 3 Sticky – retains past winners until they drop out of the top 4.

We compare sticky and baseline strategies using the tabular performance statistics used for the baseline. Using monthly total (dividend-adjusted) returns for the specified assets during February 2006 (limited by DBC) through December 2017, we find that:

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Simple Sector ETF Momentum Strategy Update/Extension

“Simple Sector ETF Momentum Strategy” investigates performances of simple momentum trading strategies for the following nine sector exchange-traded funds (ETF) executed with Standard & Poor’s Depository Receipts (SPDR):

Materials Select Sector SPDR (XLB)
Energy Select Sector SPDR (XLE)
Financial Select Sector SPDR (XLF)
Industrial Select Sector SPDR (XLI)
Technology Select Sector SPDR (XLK)
Consumer Staples Select Sector SPDR (XLP)
Utilities Select Sector SPDR (XLU)
Health Care Select Sector SPDR (XLV)
Consumer Discretionary Select SPDR (XLY)

Here, we update the principal strategy and extend it by adding equally weighted combinations of the top two and top three sector ETFs, along with corresponding robustness tests. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly dividend-adjusted closing prices for the sector ETFs and SPDR S&P 500 (SPY) and 3-month U.S. Treasury bill (T-bill) yields since December 1998, and S&P 500 Index levels since September 1998, all through December 2017, we find that: Keep Reading

Categorization of Risk Premiums

What is the best way to think about reliabilities and risks of various anomaly premiums commonly that investors believe to be available for exploitation? In their December 2017 paper entitled “A Framework for Risk Premia Investing”, Kari Vatanen and Antti Suhonen present a framework for categorizing widely accepted anomaly premiums to facilitate construction of balanced investment strategies. They first categorize each premium as fundamental, behavioral or structural based on its robustness as indicated by clarity, economic rationale and capacity. They then designate each premium in each category as either defensive or offensive depending on whether it is feasible as long-only or requires short-selling and leverage, and on its return skewness and tail risk. Based on expected robustness and riskiness of selected premiums as described in the body of research, they conclude that: Keep Reading

SACEVS-SACEMS Leverage Sensitivity Tests

“SACEVS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Value Strategy” (SACEVS). “SACEMS with Margin” investigates the use of target 2X leverage via margin to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS). In response, a subscriber requested a sensitivity test of 1.25X, 1.50X and 1.75X leverage targets. To investigate effects of these leverage targets, we separately augment SACEVS Best Value, SACEMS EW Top 3 and the equally weighted combination of these two strategies by: (1) initially applying target leverage via margin; (2) for each month with a positive portfolio return, adding margin at the end of the month to restore target leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore target leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate. Using monthly total (dividend-adjusted) returns for the specified assets since July 2002 for SACEVS and since July 2006 for SACEMS, both through December 2017, we find that:

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Volatility Scaling for Momentum Strategies?

What is the best way to implement futures momentum and manage its risk? In their November 2017 paper entitled “Risk Adjusted Momentum Strategies: A Comparison between Constant and Dynamic Volatility Scaling Approaches”, Minyou Fan, Youwei Li and Jiadong Liu compare performances of five futures momentum strategies and two benchmarks:

  1. Cross-sectional, or relative, momentum (XSMOM) – each month long (short) the equally weighted tenth of futures contract series with the highest (lowest) returns over the past six months.
  2. XSMOM with constant volatility scaling (CVS) – each month scales the XSMOM portfolio by the ratio of a 12% target volatility to annualized realized standard deviation of daily XSMOM portfolio returns over the past six months.
  3. XSMOM with dynamic volatility scaling (DVS) – each month scales the XSMOM portfolio by the the ratio of next-month expected market return (a function of realized portfolio volatility and whether MSCI return over the last 24 months is positive or negative) to realized variance of XSMOM portfolio daily returns over the past six months.
  4. Time-series, or intrinsic, momentum (TSMOM) – each month long (short) the equally weighted futures contract series with positive (negative) returns over the past six months.
  5. TSMOM with time-varying volatility scaling (TSMOM Scaled) – each month scales the TSMOM portfolio by the ratio of 22.6% (the volatility of an equally weighted portfolio of all future series) to annualized exponentially weighted variance of TSMOM returns over the past six months.
  6. Equally weighted, monthly rebalanced portfolio of all futures contract series (Buy-and-Hold).
  7. Buy-and-Hold with time-varying volatility scaling (Buy-and-Hold Scaled) – each month scales the Buy-and-Hold portfolio as for TSMOM Scaled.

They test these strategies on a multi-class universe of 55 global liquid futures contract series, starting when at least 45 series are available in November 1991. They focus on average annualized gross return, annualized volatility, annualized gross Sharpe ratio, cumulative return and maximum (peak-to-trough) drawdown (MaxDD) as comparison metrics. Using monthly prices for the 55 futures contract series (24 commodities, 13 government bonds, 9 currencies and 9 equity indexes) during June 1986 through May 2017, they find that:

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