Objective research to aid investing decisions
Menu
Value Allocations for June 2019 (Final)
Cash TLT LQD SPY
Momentum Allocations for June 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.

“Current High” Boost for SACEMS?

A subscriber asked whether applying a filter that restricts monthly asset selections of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to those currently at an intermediate-term high improves performance. This strategy each month reforms a portfolio of winners from the following universe based on total return 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 focus on the equally weighted (EW) Top 3 SACEMS portfolio and replace any selection not at an intermediate-term high with Cash. We define intermediate-term high based on monthly closes over a specified past interval ranging from one month to six months. We consider all gross performance metrics used for base SACEMS. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through July 2018 (150 months), we find that: Keep Reading

Bringing Order to the Factor Zoo?

From a purely statistical perspective, how many factors are optimal for explaining both time series and cross-sectional variations in stock anomaly/stock returns, and how do these statistical factors relate to stock/firm characteristics? In their July 2018 paper entitled “Factors That Fit the Time Series and Cross-Section of Stock Returns”, Martin Lettau and Markus Pelger search for the optimal set of equity factors via a generalized Principal Component Analysis (PCA) that includes a penalty on return prediction errors returns. They apply this approach to three datasets:

  1. Monthly returns during July 1963 through December 2017 for two sets of 25 portfolios formed by double sorting into fifths (quintiles) first on size and then on either accruals or short-term reversal.
  2. Monthly returns during July 1963 through December 2017 for 370 portfolios formed by sorting into tenths (deciles) for each of 37 stock/firm characteristics.
  3. Monthly excess returns for 270 individual stocks that are at some time components of the S&P 500 Index during January 1972 through December 2014.

They compare performance of their generalized PCA to that of conventional PCA. Using the specified datasets, they find that: Keep Reading

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 the 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 June 2018, we find that: Keep Reading

Gold Timing Strategies

Are there any gold trading strategies that reliably beat buy-and-hold? In their April 2018 paper entitled “Investing in the Gold Market: Market Timing or Buy-and-Hold?”, Viktoria-Sophie Bartsch, Dirk Baur, Hubert Dichtl and Wolfgang Drobetz test 4,095 seasonal, 18 technical, and 15 fundamental timing strategies for spot gold and gold futures. These strategies switch at the end of each month as signaled between spot gold or gold futures and U.S. Treasury bills (T-bill) as the risk-free asset. They assume trading frictions of 0.2% of value traded. To control for data snooping bias, they apply the superior predictive ability multiple testing framework with step-wise extensions. Using monthly spot gold and gold futures prices and T-bill yield during December 1979 through December 2015, with out-of-sample tests commencing January 1990, they find that:

Keep Reading

Excluding Bad Stock Factor Exposures

The many factor-based indexes and exchange-traded funds (ETFs) that track them now available enable investors to construct multi-factor portfolios piecemeal. Is such piecemeal construction suboptimal? In their July 2018 paper entitled “The Characteristics of Factor Investing”, David Blitz and Milan Vidojevic apply a multi-factor expected return linear regression model to explore behaviors of long-only factor portfolios. They consider six factors: value-weighted market, size, book-to-market ratio, momentum, operating profitability and investment(change in assets). Their model generates expected returns for each stock each month, and further aggregates individual stock expectations into factor-portfolio expectations holding all other factors constant. They use the model to assess performance differences between a group of long-only single-factor portfolios and an integrated multi-factor portfolio of stocks based on combined rankings across factors. The focus on gross monthly excess (relative to the 10-year U.S. Treasury note yield) returns as a performance metric. Using data for a broad sample of U.S. common stocks among the top 80% of NYSE market capitalizations and priced at least $1 during June 1963 through December 2017, 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 2018, we find that: Keep Reading

Betting Against Beta, Plus Market Momentum

betting against beta (BAB) portfolio is long low-beta assets and short high-beta assets, with each side leveraged to a beta of one. Do strong past stock market returns (when investors tend to overweight high-beta stocks) predict an increase in BAB returns? In his June 2018 paper entitled “Time-Varying Leverage Demand and Predictability of Betting-Against-Beta”, Esben Hedegaard tests the prediction that BAB performs better in times and in countries after high past stock market returns in three ways: (1) regression of BAB returns versus past market returns; (2) sorts of BAB returns into fifths (quintiles) based on past market returns; and, (3) a timing strategy that is long BAB half the time and short BAB half the time based on detrended inception-to-date past market returns, scaled to 10% annualized volatility for comparability. Using daily and monthly data, including monthly BAB returns, for U.S. common stocks and the U.S. stock market since 1931 and for 23 other countries from as early as 1988, all through January 2018, he finds that: Keep Reading

Alternative Momentum Metrics for SACEMS?

A subscriber asked whether some different momentum metric might improve performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS), which each month reforms a portfolio of winners from the following universe based on total return 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 performances of the following alternative monthly momentum metrics to that of the original baseline metric:

  • Average monthly total returns over the lookback interval.
  • Slope of the dividend-adjusted price series over the lookback interval.
  • Sharpe ratio of the monthly total return series over the lookback interval (using Cash return as the risk-free rate, and setting the Sharpe ratio of Cash at zero).

We focus on the equally weighted (EW) Top 3 SACEMS portfolio. We consider all the performance metrics used for the baseline, with emphasis on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD). Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash over the period February 2006 (the earliest all ETFs are available) through May 2018 (148 months), we find that: Keep Reading

Currency Exchange Style Factors for Incremental Diversification

Do currency exchange factor strategies usefully diversify a set of conventional asset classes? In their May 2018 paper entitled “Currency Management with Style”, Harald Lohre and Martin Kolrep investigate the systematic harvesting of currency exchange carry, value and momentum strategies, specified as follows and applied to the G10 currencies:

  • Carry – buy (sell) the three equally weighted currency forwards with the highest (lowest) short-term interest rates, reformed monthly.
  • Momentum – buy (sell) the three equally weighted currency forwards with the greatest (least) appreciation over the past three months, reformed monthly.
  • Value (long-term reversion) – buy (sell) the three equally weighted currency forwards with the lowest (highest) change in their real exchange rates, based on purchasing power parity, over the past 60 months, reformed monthly.

They examine in-sample (full-sample) mean-variance relationships for these strategies to assess their value as diversifiers of five conventional asset classes (U.S. stocks, commodities, U.S. Treasury bonds, U.S. corporate investment-grade bonds and U.S. corporate high-yield bonds). They also look at potential out-of-sample benefits of these strategies based on information available at the time of each monthly rebalancing as additions to a risk parity portfolio of the five conventional assets from the perspective. For this out-of-sample test, they consider both minimum variance (tail risk hedging) and mean-variance optimization (return seeking) for aggregating the three currency strategies. Using monthly data for the selected assets from the end of January 1999 through December 2016, they find that: Keep Reading

Doing Momentum with Style (ETFs)

“Beat the Market with Hot-Anomaly Switching?” concludes that “a trader who periodically switches to the hottest known anomaly based on a rolling window of past performance may be able to beat the market. Anomalies appear to have their own kind of momentum.” Does momentum therefore work for style-based exchange-traded funds (ETF)? To investigate, we apply a simple momentum strategy to the following six ETFs that cut across market capitalization (large, medium and small) and value versus growth:

iShares Russell 1000 Value Index (IWD) – large capitalization value stocks.
iShares Russell 1000 Growth Index (IWF) – large capitalization growth stocks.
iShares Russell Midcap Value Index (IWS) – mid-capitalization value stocks.
iShares Russell Midcap Growth Index (IWP) – mid-capitalization growth stocks.
iShares Russell 2000 Value Index (IWN) – small capitalization value stocks.
iShares Russell 2000 Growth Index (IWO) – small capitalization growth stocks.

We test a simple Top 1 strategy that allocates all funds each month to the one style ETF with the highest total return over a set momentum measurement (ranking or lookback) interval. We focus on the baseline ranking interval from “Simple Asset Class ETF Momentum Strategy”, but test sensitivity of findings to ranking intervals ranging from one to 12 months. As benchmarks, we consider an equally weighted and monthly rebalanced combination of all six style ETFs (EW All), buying and holding S&P Depository Receipts (SPY), and holding SPY when the S&P 500 Index is above its 10-month simple moving average and U.S. Treasury bills (T-bills) when the index is below its 10-month simple moving average (SPY:SMA10). We consider the performance metrics used in “Momentum Strategy (SACEMS)”. Using monthly dividend-adjusted closing prices for the style ETFs and SPY, monthly levels of the S&P 500 index and monthly yields for 3-month T-bills during August 2001 (limited by IWS and IWP) through May 2018 (202 months, ), we find that:

Keep Reading

Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts