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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.

Stock Return Autocorrelations and Option Returns

Does return persistence of individual stocks predict associated option returns? In their March 2019 paper entitled “Stock Return Autocorrelations and the Cross Section of Option Returns”, Yoontae Jeon, Raymond Kan and Gang Li investigate relationships between equity option returns and return autocorrelations of underlying stocks. They consider call options, put options and straddles (long both a call and a put with the same strike price). Each month on standard option expiration date, they:

  • Measure one-step monthly stock return autocorrelations using a 36-month rolling window of monthly returns for U.S. stocks with over 20 monthly observations.
  • Rank stocks (and respective options) by autocorrelation into fifths (quintiles).
  • Construct a hedge portfolio that is long (short) the equal-weighted or market capitalization-weighted stocks in the top (bottom) quintile of autocorrelations, to calculate stock portfolio return as a control variable.
  • Construct corresponding hedge portfolios of call options, put options or straddles, limiting choices to reasonably liquid options with moneyness closest to 1.0 and time to expiration closest to 30 days. 
  • Hold these portfolios until the next standard option expiration date.

They further explore out-of-sample use of results via modified mean-variance optimization of a portfolio consisting of the S&P 500 Index, the risk-free asset and equity options with bid-ask spreads no greater than 10% of price. They size individual option positions as a function of underlying stock volatility, variance risk premium and stock return autocorrelation. They assume investor utility derives from constant relative risk aversion level 3. For the frictionless case, they base option returns on the bid-ask midpoint. For the case with frictions, they assume buys (sells) occur at the ask (bid). Using specified stock and options data during January 1996 through December 2017, they find that: Keep Reading

Effects of Factor Crowding

Does crowding of factor investing strategies reliably predict returns for those strategies? In his March 2019 paper entitled “The Impact of Crowding in Alternative Risk Premia Investing”, Nick Baltas explores mechanics of alternative risk (factor) premium crowding and implications of crowding for future performance. He classifies factor premiums as: divergent (such as momentum), inherently destabilizing due to positive feedback loops and lack of fundamental anchors; or, convergent (such as value), having self-correcting negative feedback loops and fundamental anchors. To test crowding effects, he considers the following premiums: equity value (book-to-market), size (market capitalization), momentum (from regression of return from 12 months ago to one month ago versus volatility), quality (return on assets) and low beta (versus the MSCI World Index); commodities momentum (12-month return); and, currencies value (purchasing power parity) and momentum (12-month return). Each premium consists of returns from a hedge portfolio that is each week long (short) the equal-weighted assets with the highest (lowest) expected returns. For equities, he uses top and bottom tenths. For commodities and currencies, he uses top and bottom thirds. His crowding metric (CoMetric) is average pairwise correlation of factor-adjusted returns of assets within the long or short sides of premium portfolios over the last 52 weeks (except 260 weeks for value). He defines the 20% of weeks with the highest (lowest) CoMetrics as most (least) crowded. Using the specified factor and return data for liquid developed market stocks since September 2004, 24 constituents of the S&P GSCI Commodity Index since January 1999, and 26 developed and emerging markets currency pairs versus the U.S. dollar since January 2000, all through May 2018, he finds that:

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Ubiquitous Equity Factor Momentum?

Do returns for equity factors (long stocks with high expected returns and short stocks with low expected returns based on some firm/stock trading characteristic) broadly and reliably exhibit momentum? In other words, do factors with strong (weak) returns in recent months have strong (weak) returns next month? In the February 2019 revision of their paper entitled “Factor Momentum Everywhere”, Tarun Gupta and Bryan Kelly test return momentum among 65 widely studied long-short equity factors for the U.S. and 62 factors globally that have underlying data available since the mid-1960s, including: valuation ratios (such as earnings-to-price and book-to-market); size, investment and profitability metrics (such as market capitalization, sales growth and return on equity); idiosyncratic risk metrics (such as betting against beta, stock volatility and skewness); and, liquidity metrics (such as Amihud illiquidity, share volume and bid-ask spread). For each factor, they each month:

  • Exclude as outliers the top and bottom 1% of stocks with the most extreme factor characteristic values.
  • Split residual stocks into big and small size segments based on median NYSE market capitalization for U.S. stocks and 80th percentile of market capitalizations for international stocks.
  • Within size segments, sort stocks into low/medium/high characteristic bins based on 30/40/30 percentile splits and form value-weighted sub-portfolios that are long (short) high (low) bins.
  • Form an overall factor portfolio with long side 0.5 * (Large High + Small High) and short side 0.5 * (Large Low + Small Low).

They consider both time series factor momentum (TSFM, intrinsic or absolute momentum) and cross-sectional factor momentum (CSFM, relative momentum). As benchmarks, they consider the equal-weighted average return for all factors and a conventional stock momentum factor based on returns from 12 months to one month ago. Using monthly U.S. and global data required to construct the factor portfolios and their returns from 1965 through 2017, they find that: Keep Reading

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

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

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