Momentum Investing

Quantitative investing involves disciplined rule-based approaches to help investors structure optimal portfolios that balance return and risk. How has such investing evolved? In their June 2018 paper entitled “The Current State of Quantitative Equity Investing”, Ying Becker and Marc Reinganum summarize key developments in the history of quantitative equity investing. Based on the body of research, they conclude that: Keep Reading

Are U.S. stock and sector momentum strategies, as implemented by exchange-traded funds (ETF), attractive? To investigate, we consider five momentum-oriented U.S. equity ETFs with assets over $100 million, all currently available (in order of decreasing assets):

• iShares Edge MSCI USA Momentum Factor (MTUM) – holds U.S. large-capitalization and mid-capitalization stocks with relatively high momentum.
• First Trust Dorsey Wright Focus 5 (FV) – holds five equally weighted sector and industry ETFs selected via a proprietary relative strength methodology, reformed twice a month.
• PowerShares DWA Momentum Portfolio (PDP) – invests at least 90% of assets in approximately 100 U.S. common stocks per a proprietary methodology designed to identify powerful relative strength characteristics, reformed quarterly.
• SPDR Russell 1000 Momentum Focus (ONEO) – tracks the Russell 1000 Momentum Focused Factor Index, picking U.S. stocks that have recently outperformed.
• First Trust Dorsey Wright Dynamic Focus 5 ETF (FVC) – similar to FV but with added risk management via an increasing allocation to cash equivalents when relative strengths of more than one-third of the universe diminish relative to a cash index, reformed twice a month.

Because some sample periods are very short, we focus on daily return statistics, but also consider cumulative returns and maximum drawdowns (MaxDD). We use two benchmark ETFs, iShares Russell 1000 (IWB) and iShares Russell 3000 (IWV), according to momentum fund descriptions. Using daily returns for the five momentum funds and the two benchmarks as available through mid-September 2018, we find that: Keep Reading

Brian Livingston introduces his 2018 book, Muscular Portfolios: The Investing Revolution for Superior Returns with Lower Risk, as follows: “What we laughingly call the financial ‘services’ industry is a cesspool filled with sharks intent on siphoning your money away and making it their own. The good news is that it is absolutely possible to grow your savings with no fear of financial sharks or stock market crashes. In the past few years, we’ve seen an explosion of low-cost index funds, along with serious mathematical breakthroughs in how to combine these funds into low-risk portfolios. …This book shows you how.  …You can start with just a little money and make it grow.” Based on research from multiple sources and extensions of that research, he concludes that: Keep Reading

Do exchange-traded funds (ETF) that track major currencies support a relative momentum strategy? To investigate, we consider the following four ETFs:

Invesco DB US Dollar Bullish (UUP)
Invesco CurrencyShares Euro Currency (FXE)
Invesco CurrencyShares Japanese Yen (FXY)
WisdomTree Chinese Yuan Strategy (CYB)

We each month rank these ETFs based on past return over lookback intervals ranging from one to 12 months. We consider portfolios of past winners reformed monthly based on Top 1 and on equally weighted (EW) Top 2 and Top 3 ETFs. The benchmark portfolio is the equally weighted combination of all four ETFs. We present findings in formats similar to those used for the Simple Asset Class ETF Momentum Strategy and the Simple Asset Class ETF Value Strategy. Using monthly adjusted closing prices for the currency ETFs during March 2007 (when three become available) through August 2018, we find that: Keep Reading

Do alternative “Cash” (deemed risk-free) instruments materially affect performance of the“Simple Asset Class ETF Momentum Strategy” (SACEMS)? Changing the proxy for Cash can affect how often the model selects Cash, as well as the return on Cash when selected. To investigate, we test separately each of the following yield and exchange-traded funds (ETF) as the risk-free asset:

3-month Treasury bills (Cash), a proxy for the money market as in base SACEMS
SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
iShares 1-3 Year Treasury Bond (SHY)
iShares 7-10 Year Treasury Bond (IEF)
iShares TIPS Bond (TIP)

In other words, we add one of the five risk-free assets to the following base set of eight ETFs:

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)

We focus on the equally weighted (EW) EW Top 3 SACEMS portfolio and consider all performance metrics used for base SACEMS. Using end-of-month total (dividend-adjusted) returns for the specified assets during February 2006 (except May 2007 for BIL) through July 2018, we find that:

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A subscriber inquired whether a longer test of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) is feasible using mutual funds rather than exchange-traded funds (ETF) as asset class proxies. To investigate, we consider the following set of mutual funds (partly adapted from the paper summarized in “Asset Allocation Combining Momentum, Volatility, Correlation and Crash Protection”):

Oppenheimer Commodity Strategy Total Return A (QRAAX) until its discontinuation in mid-2016, and PIMCO CommoditiesPLUS Strategy (PCPSX) thereafter.
Vanguard Emerging Markets Stock Index Investor Shares (VEIEX)
Fidelity Diversified International (FDIVX)
First Eagle Gold A (SGGDX)
Vanguard Total Stock Market Index Investor Shares (VTSMX)
Vanguard Small Capitalization Index Investor Shares  (NAESX)
Vanguard REIT Index Investor Shares (VGSIX)
Vanguard Long-Term Treasury Investor Shares (VUSTX)
3-month Treasury bills (Cash)

We rank mutual funds based on total (dividend-adjusted) returns over past (lookback) intervals of one to 12 months. We consider portfolios of past mutual fund 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 mutual funds, rebalanced monthly (EW All); buying and holding VTSMX; and, holding VTSMX when the S&P 500 Index is above its 10-month simple moving average (SMA10) and Cash when the index is below its SMA10 (VTSMX:SMA10). Using monthly dividend-adjusted closing prices for the above mutual funds and the yield for Cash during March 1997 through July 2018 (269 months), we find that: Keep Reading

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

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

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

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:

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