Objective research to aid investing decisions
Menu
Value Allocations for October 2019 (Final)
Cash TLT LQD SPY
Momentum Allocations for October 2019 (Final)
1st ETF 2nd ETF 3rd ETF

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.

Sector Breadth as Market Return Indicator

Does breadth of equity sector performance predict overall stock market return? To investigate, we relate next-month stock market return to sector breadth (number of sectors with positive past returns) over lookback intervals ranging from 1 to 12 months. We consider the following nine sector exchange-traded funds (ETF) offered as 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)

We use SPDR S&P 500 (SPY) to represent the overall stock market. Using monthly dividend-adjusted returns for SPY and the sector ETFs during December 1998 through August 2019, we find that: Keep Reading

SACEMS, SACEVS and Trading Calendar Updates

We have updated monthly allocations and performance data for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS). We have also updated performance data for the Combined Value-Momentum Strategy.

We have updated the Trading Calendar to incorporate data for September 2019.

Preliminary SACEMS and SACEVS Allocation Updates

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for October 2019. For SACEMS, past returns for ranks two through four are very close, so the second and third places could change by the close. For SACEVS, allocations are unlikely to change.

Long-only Stock Momentum with Volatility Timing

What is the best way to avoid stock momentum portfolio crashes? In her July 2019 paper entitled “Momentum with Volatility Timing”, Yulia Malitskaia tests a long-only volatility-timed stock momentum strategy that exits holdings when strategy volatility over a past interval exceeds a specified threshold. She focuses on a recent U.S. sample that includes the 2008-2009 market crash and its aftermath. She considers the following momentum portfolios:

  • WML10 – each month long (short) the tenth, or decile, of stocks with the highest (lowest) returns from 12 months ago to one month ago.
  • W10 and L10 – WML10 winner and loser sides separately.
  • WML10-Scaled – adjusts WML10 exposure according to the ratio of a volatility target to actual WML10 annualized daily volatility over the past six months. This approach seeks to mitigate poor returns when WML10 volatility is unusually high.
  • W10-Timed – holds W10 (cash, with zero return) when W10 volatility over the past six months is below (at or above) a specified threshold. This approach seeks to avoid poor post-crash, loser-driven WML10 performance and poor W10  performance during crashes.

She performs robustness tests on  MSCI developed and emerging markets risk-adjusted momentum indexes. Using daily and monthly returns for W10 and L10 portfolios since 1980 and for MSCI momentum indexes since 2000, all through 2018, she finds that:

Keep Reading

Stock Momentum Strategy Risk Management Horse Race

What is the best risk management approach for a conventional stock momentum strategy? In their August 2019 paper entitled “Enhanced Momentum Strategies”, Matthias Hanauer and Steffen Windmueller compare performances of several stock momentum strategy risk management approaches proposed in prior research. They use the momentum factor, returns to a monthly reformed long-short portfolio that integrates average returns from 12 months ago to two months ago with market capitalization, as their base momentum strategy (MOM). They consider five risk management approaches:

  1. Constant volatility scaling with 6-month lookback (cvol6M) – scales the base momentum portfolio to a constant target volatility (full sample volatility of the base strategy) using volatility forecasts from daily momentum returns over the previous six months (126 trading days).
  2. Constant volatility scaling with 1-month lookback (cvol1M) – same as cvol6M, but with volatility forecasts from daily momentum returns over the previous month (21 trading days).
  3. Dynamic volatility scaling estimated in-sample (dynIS) – enhances constant volatility scaling by also forecasting momentum portfolio returns based on market return over the past two years using the full sample (with look-ahead bias).
  4. Dynamic volatility scaling estimated out-of-sample (dyn) – same as dynIS, but with momentum portfolio return forecasts from the inception-to-date market subsample.
  5. Idiosyncratic momentum (iMOM) – sorts stocks based on their residuals from monthly regressions versus market, size and value factors from 12 months ago to one month ago (rather than their raw returns) and scales residuals by monthly volatility of residuals over this same lookback interval. 

They evaluate momentum risk management strategies based on: widely used return and risk metrics; competition within a mean-variance optimization framework; and, breakeven portfolio reformation frictions. Using monthly and daily returns in U.S. dollars for U.S. common stocks since July 1926 and for common stocks from 48 international markets since July 1987 (July 1994 for emerging markets), all through December 2017, they find that: Keep Reading

SACEMS-SACEVS Diversification with Mutual Funds

“SACEMS-SACEVS for Value-Momentum Diversification” finds that the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) are mutually diversifying. Do longer samples available from “SACEVS Applied to Mutual Funds” and “SACEMS Applied to Mutual Funds” confirm this finding? To check, we look at the following three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

  1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
  2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
  3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

Using monthly gross returns for SACEVS and SACEMS mutual fund portfolios during September 1997 through July 2019, we find that:

Keep Reading

SACEVS-SACEMS for Value-Momentum Diversification

Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, we look at three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

  1. SACEVS Best Value paired with SACEMS Top 1 (aggressive value and aggressive momentum).
  2. SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 3 (aggressive value and diversified momentum).
  3. SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS and SACEMS portfolios since January 2003 for the first strategy and since June 2006 for the latter two, all through July 2019, we find that: Keep Reading

Equity Factor Time Series Momentum

In their July 2019 paper entitled “Momentum-Managed Equity Factors”, Volker Flögel, Christian Schlag and Claudia Zunft test exploitation of positive first-order autocorrelation (time series, absolute or intrinsic momentum) in monthly excess returns of seven equity factor portfolios:

  1. Market (MKT).
  2. Size – small minus big market capitalizations (SMB).
  3. Value – high minus low book-to-market ratios (HML).
  4. Momentum – winners minus losers (WML)
  5. Investment – conservative minus aggressive (CMA).
  6. Operating profitability – robust minus weak (RMW).
  7. Volatility – stable minus volatile (SMV).

For factors 2-7, monthly returns derive from portfolios that are long (short) the value-weighted fifth of stocks with the highest (lowest) expected returns. In general, factor momentum timing means each month scaling investment in a factor from 0 to 1 according its how high its last-month excess return is relative to an inception-to-date window of past levels. They consider also two variations that smooth the simple timing signal to suppress the incremental trading that it drives. In assessing costs of this incremental trading, they assume (based on other papers) that realistic one-way trading frictions are in the range 0.1% to 0.5%. Using monthly data for a broad sample of U.S. common stocks during July 1963 through November 2014, they find that: Keep Reading

Simple Debt Class Mutual Fund Momentum Strategy

A subscriber requested confirmation of the performance of a simple momentum strategy that each month selects the best performing debt mutual fund based on total return over the past three months. To investigate, we test a simple strategy on the following 12 mutual funds (those with the longest histories from a proposed list of 14 funds):

T. Rowe Price New Income (PRCIX)
Thrivent Income A (LUBIX)
Vanguard GNMA Securities (VFIIX)
T. Rowe Price High-Yield Bonds (PRHYX)
T. Rowe Price Tax-Free High Yield Bonds (PRFHX)
Vanguard Long-Term Treasury Bonds (VUSTX)
T. Rowe Price International Bonds (RPIBX)
Fidelity Convertible Securities (FCVSX)
PIMCO Short-Term A (PSHAX)
Fidelity New Markets Income (FNMIX)
Eaton Vance Government Obligations C (ECGOX)
Vanguard Long-Term Bond Index (VBLTX)

We consider a strategy that allocates funds at the end of each month based on total returns over a specified ranking (lookback) interval to the Top 1, equally weighted (EW) Top 2, EW Top 3, EW Top 4 or EW Top 5 funds. We determine the first winners in November 1988 so that at least nine funds are available for lookback interval sensitivity testing. As a benchmark, we use the equally weighted and monthly rebalanced combination of all available funds (EW All). Using monthly dividend-adjusted closing prices for the 12 mutual funds from inceptions through June 2019, we find that: Keep Reading

SACEMS vs. Luck

How lucky would a asset class picker with no skill have to be to match the performance of the Simple Asset Class Momentum Strategy (SACEMS), which each month picks winners from a set of eight exchange-traded funds (ETF) plus cash based on total returns over a specified lookback interval. To investigate, we run 1,000 trials of a “strategy” that each month allocates funds to one, the equally weighted two or the equally weighted three of these nine assets picked at random. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics. Using monthly total (dividend-adjusted) returns and for the specified assets during February 2006 (limited by DBC) through June 2019, we find that:

Keep Reading

Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts