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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.

Optimal Retirement Glidepath with Trend Following

What are optimal allocations during retirement years for a portfolio of stocks and bonds, without and with a trend following overlay? In their March 2019 paper entitled “Absolute Momentum, Sustainable Withdrawal Rates and Glidepath Investing in US Retirement Portfolios from 1925”, Andrew Clare, James Seaton, Peter Smith and Steve Thomas compare outcomes across two sets of U.S. retirement portfolios since 1925:

  1. Standard – allocations to the S&P 500 Index and a bond index ranging from all stocks to all bonds in increments of 10%, rebalanced at the end of each month.
  2. Trend following – the same portfolios with a trend following overlay that shifts stock index and bond index allocations to U.S. Treasury bills (T-bills) when below respective 10-month simple moving averages at the end of the preceding month.

They consider investment horizons of 2 to 30 years to assess glidepath effects. They consider both U.S. Treasury bonds and U.S. corporate bonds to assess credit effects. For comparison of portfolio outcomes, they use real (inflation-adjusted) returns and focus on Perfect Withdrawal Rate (PWR), the maximum annual withdrawal rate
that results in zero terminal value (requiring perfect foresight). Using monthly data for the S&P 500 Index, U.S. government and corporate bond indexes and U.S. inflation during 1926 through 2016, they find that: Keep Reading

Sophisticated Simulation of Intrinsic (Time Series) Momentum

How can investors confidently assess risk of strategy crashes (tail events) when there are so few crashes even in long samples? In their March 2019 paper entitled “Time-Series Momentum: A Monte-Carlo Approach”, Clemens Struck and Enoch Cheng present a Monte-Carlo simulation procedure for strategy backtesting that both preserves time series and cross-sectional return characteristics while diversifying time series simulation inputs. They use this procedure to test intrinsic (absolute or time series) momentum on S&P 500 Index futures and on an equal-weighted multi-class portfolio of 27 futures series. They consider long-short and long-only (long-cash) versions of time series momentum (TSM), with or without volatility adjustment. For testing actual histories, they consider lookback intervals of 1, 3, 6, 9 and 12 months to measure momentum. For simulations, they focus on optimal lookbacks from actual histories and consider multiple time series models. Their in-sample subperiods are 1985-2009 for the S&P 500 Index and February 1989-2009 for the multi-class portfolio. Their out-of-sample subperiod is 2010-2018. They roll each futures series at the end of each month into the next front contract, using spot indexes prior to the availability of some futures. They use buy-and-hold portfolios (with rolling) as benchmarks. Using monthly prices for nine equity indexes, four government bonds, eight commodities and six currencies futures/spot series in U.S. dollars over the specified sample period, they find that:

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Asset Class Momentum Faster During Bear Markets?

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. This strategy 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)

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, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for the specified assets since February 2006 (limited by DBC) and the monthly level of the S&P 500 Index since September 2005, all through February 2019, we find that:

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Asset Class Short-term Momentum Over the Long Run

Do assets other than individual stocks exhibit a short-term (1-month) reversal effect? In their February 2019 paper entitled “Short-Term Momentum (Almost) Everywhere”, Adam Zaremba, Andreas Karathanasopoulos and Huaigang Long investigate short-term return predictability within long run global samples spanning five asset classes: equity indexes, government bonds, treasury bills, commodity futures and currencies. Each month they sort assets by class or overall into fifths (quintiles) on prior-month return. For classes with at least 10 assets available, they then construct long-short hedge portfolios that are long (short) the equal-weighted quintile of assets with the highest (lowest) prior-month returns. Using monthly returns for 45 equity indexes, 54 government bonds, 52 government bills, 48 commodity futures and 62 currency exchange rates in U.S. dollars as available during 1800 through 2018, they find that: Keep Reading

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 updated the “Trading Calendar” to incorporate data for March 2019.

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 April 2019. For SACEMS, the top two positions are unlikely to change by the close, but third place could change. For SACEVS, allocations are unlikely to change.

Simple Momentum Strategy Applied to TSP Funds

A subscriber asked about applying the “Simple Asset Class ETF Momentum Strategy” to the funds available to U.S. federal government employees via the Thrift Savings Plan (TSP). To investigate, we test the strategy on the following five funds:

G Fund: Government Securities Investment Fund (G)
F Fund: Fixed Income Index Investment Fund (F)
C Fund: Common Stock Index Investment Fund (C)
S Fund: Small Cap Stock Index Investment Fund (S)
I Fund: International Stock Index Investment Fund (I)

We each month rank these funds based on returns over past (lookback) intervals of one to 12 months. We test Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly fund winners. We employ as a benchmark a naively diversified EW portfolio of all five funds, rebalanced monthly (EW All). Using monthly returns for the five funds from initial availability of all five (January 2001) through February 2019, we find that:

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SACEMS with Three Copies of Cash

Subscribers have questioned selecting assets with negative past returns within the “Simple Asset Class ETF Momentum Strategy” (SACEMS). Inclusion of Cash as one of the assets in the SACEMS universe of exchange-traded funds (ETF) prevents the SACEMS Top 1 portfolio from holding an asset with negative past returns. To test full dual momentum versions of SACEMS equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios, we add two more copies of Cash to the universe, thereby preventing both of them from holding assets with negative past returns. The SACEMS universe thus becomes:

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)
3-month Treasury bills (Cash)
3-month Treasury bills (Cash)

We focus on the effects of adding two copies of Cash on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS EW Top 2 and EW Top 3 portfolios. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 (the earliest all ETFs are available) through February 2019, we find that: Keep Reading

Optimal Monthly Cycle for SACEMS?

Is there a best time of the month for measuring momentum within the Simple Asset Class ETF Momentum Strategy (SACEMS)? This strategy 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)

To investigate, we compare 21 variations of the strategy based on shifting the monthly return calculation cycle relative to trading days from the end of the month (EOM). For example, an EOM+5 cycle ranks assets based on closing prices five trading days after EOM each 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 for the specified assets during mid-February 2006 (limited by DBC) through mid-February 2019, we find that: Keep Reading

Inflated Expectations of Factor Investing

How should investors feel about factor/multi-factor investing? In their February 2019 paper entitled “Alice’s Adventures in Factorland: Three Blunders That Plague Factor Investing”, Robert Arnott, Campbell Harvey, Vitali Kalesnik and Juhani Linnainmaa explore three critical failures of U.S. equity factor investing:

  1. Returns are far short of expectations due to overfitting and/or trade crowding.
  2. Drawdowns far exceed expectations.
  3. Diversification of factors occasionally disappears when correlations soar.

They focus on 15 factors most closely followed by investors: the market factor; a set of six factors from widely used academic multi-factor models (size, value, operating profitability, investment, momentum and low beta); and, a set of eight other popular factors (idiosyncratic volatility, short-term reversal, illiquidity, accruals, cash flow-to-price, earnings-to-price, long-term reversal and net share issuance). For some analyses they employ a broader set of 46 factors. They consider both long-term (July 1963-June 2018) and short-term (July 2003-June 2018) factor performances. Using returns for the specified factors during July 1963 through June 2018, they conclude that:

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