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

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.

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

SACEMS Portfolio-Asset Addition Testing

Does adding an exchange-traded fund (ETF) or note (ETN) to the Simple Asset Class ETF Momentum Strategy (SACEMS) boost performance via consideration of more trending/diversifying options? To investigate, we add the following 23 ETF/ETN asset class proxies one at a time to the base set and measure effects on the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

AlphaClone Alternative Alpha (ALFA)
JPMorgan Alerian MLP Index (AMJ)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
UBS ETRACS Wells Fargo Business Development Companies (BDCS)
iShares Core US Credit Bond (CRED)
First Trust US IPO Index (FPX)
PowerShares DB G10 Currency Harvest (DBV)
iShares JPMorgan Emerging Market Bond Fund (EMB)
Guggenheim Frontier Markets (FRN)
iShares iBoxx High-Yield Corporate Bond (HYG)
iShares 7-10 Year Treasury Bond (IEF)
iShares Latin America 40 (ILF)
iShares National Muni Bond ETF (MUB)
PowerShares Closed-End Fund Income Composite (PCEF)
PowerShares Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares TIPS Bond (TIP)
United States Oil (USO)
iPath S&P 500 VIX Short-Term Futures (VXX)
iPath S&P 500 VIX Medium-Term Futures (VXZ)

The base set consists of:

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)

Each month, we rank the base set plus one of the additional ETFs/ETNs based on past return and reform the SACEMS portfolios. The sample starts with the first month all base set ETFs are available (February 2006), but inceptions for most of the additional ETFs/ETNs are after this month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly reformation costs. Using end-of-month total (dividend-adjusted) returns for the specified 32 assets as available during February 2006 through June 2018, we find that: Keep Reading

SACEMS Portfolio-Asset Exclusion Testing

Are all of the potentially trending/diversifying asset class proxies used in the Simple Asset Class ETF Momentum Strategy (SACEMS) necessary? Might one or more of them actually be harmful to performance? To investigate, we each month rank the following assets based on past return with one excluded (nine separate test series) and reform the Top 1, equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios:

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)

The test starts with the first month all ETFs are available (February 2006). We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics, ignoring monthly portfolio reformation costs. Using end-of-month total (dividend-adjusted) returns for the specified nine assets during February 2006 through June 2018, we 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

Simple Asset Class ETF Momentum Strategy (SACEMS) Update

Does a simple relative momentum strategy applied to tradable asset class proxies produce attractive results? To investigate, we test a simple strategy on 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)

This set of ETFs offers: (1) opportunities to capture momentum across global developed and emerging equity markets, large and small U.S. equities and bonds and commodities; (2) gold and cash as safe havens; (3) histories long enough for backtesting across multiple market environments; and, (4) simplicity of computation and recognition of the trade-off between number of ETFs and trading frictions. We rank ETFs based on total (dividend-adjusted) returns over past (lookback) intervals of one to 12 months. We consider portfolios of past ETF winners based on Top 1 and on equally weighted (EW) Top 2 through Top 5. We consider as benchmarks: an equally weighted portfolio of all ETFs, rebalanced monthly (EW All); buying and holding SPY (SPY); and, holding SPY when the S&P 500 Index is above its 10-month simple moving average (SMA10) and Cash when the index is below its SMA10 (SPY:SMA10). Using monthly dividend-adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 (when all ETFs are first available) through May 2018 (148 months), we find that: Keep Reading

Daily Email Updates
Login
Research Categories
Recent Research
Popular Posts
Popular Subscriber-Only Posts