Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for May 2022 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for May 2022 (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.

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 24 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)
VanEck Vectors BDC Income (BIZD)
Vanguard Total Bond Market (BND)
SPDR Barclays International Treasury Bond (BWX)
iShares MSCI Emerging Markets (EEM)
iShares MSCI Frontier 100 (FM)
First Trust US IPO Index (FPX)
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)
Invesco Closed-End Fund Income Composite (PCEF)
Invesco Global Listed Private Equity (PSP)
IQ Hedge Multi-Strategy Tracker (QAI)
Invesco QQQ Trust (QQQ)
SPDR Dow Jones International Real Estate (RWX)
ProShares UltraShort S&P 500 (SDS)
iShares Short Treasury Bond (SHV)
iShares TIPS Bond (TIP)
United States Oil (USO)
Invesco DB US Dollar Index Bullish Fund (UUP)
ProShares VIX Short-Term Futures (VIXY)
ProShares VIX Mid-Term Futures (VIXM)

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, dividend-adjusted returns for all assets as available during February 2006 through May 2021, 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 nine SACEMS 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. 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, dividend-adjusted returns for SACEMS assets during February 2006 through May 2021, we find that: Keep Reading

Factor Crowding in Commodity Futures

Can investors detect when commodity futures momentum, value and carry (basis) strategies are crowded and therefore likely to generate relatively weak returns? In the March 2021 version of their paper entitled “Crowding and Factor Returns”, Wenjin Kang, Geert Rouwenhorst and Ke Tang examine how crowding by commodity futures traders affects expected returns for momentum, value and basis strategies. They define commodity-level crowding based on excess speculative pressure, measured for each commodity as the deviation of non-commercial trader net position (long minus short) from its 3-year average, scaled by open interest. They calculate crowding for a long-short strategy portfolio as the average of commodity-level crowding metrics of long positions minus the average of commodity-level crowding metrics for short positions, divided by two. They specify strategy portfolios as follows:

  • Momentum – each week long (short) the equally weighted 13 commodities with the highest (lowest) past 1-year returns as of the prior week.
  • Value – each week long (short) the equally weighted 13 commodities with the highest (lowest) ratios of last-week nearest futures price to nearest futures price three years ago.
  • Basis – each week long (short) the equally weighted 13 commodities with the highest (lowest) basis, measured as percentage price difference between nearest and next maturity contracts as of the prior week.

For each strategy, they measure effects of crowding by measuring returns separately when strategy crowding is above or below its rolling 3-year average. Using weekly (Tuesday close) investor position data published by the Commodity Futures Trading Commission (CFTC) for 26 commodities traded on North American exchanges during January 1993 through December 2019, they find that:

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SACEMS Applied to Mutual Funds

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”):

  1. Vanguard Total Stock Market Index Investor Shares (VTSMX)
  2. Vanguard Small Capitalization Index Investor Shares  (NAESX)
  3. Fidelity Diversified International (FDIVX)
  4. Vanguard Long-Term Treasury Investor Shares (VUSTX)
  5. Fidelity New Markets Income Fund (FNMIX)
  6. Vanguard REIT Index Investor Shares (VGSIX)
  7. First Eagle Gold A (SGGDX)
  8. Oppenheimer Commodity Strategy Total Return A (QRAAX) until in October 2011, and BlackRock Commodity Strategies Portfolio Institutional Shares (BICSX) thereafter
  9. 3-month U.S. 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 April 2021, we find that: Keep Reading

SPY-TLT Allocation Momentum?

A subscriber suggested review of the “SPY-TLT Universal Investment Strategy”, which each day allocates 100% of funds to SPDR S&P 500 (SPY) and/or iShares 20+ Year Treasury Bond (TLT) with SPY-TLT allocations equal to that with the best risk-adjusted daily performance over the past few months. There are 11 SPY-TLT allocation percentage choices: 100-0, 90-10, 80-20, 70-30, 60-40, 50-50, 40-60, 30-70, 20-80, 10-90 and 0-100. We test a simplified version of the strategy as follows:

  1. Each trading day, calculate dividend-adjusted close-to-close SPY and TLT returns.
  2. As soon as enough days are available, calculate the ratio of average daily return to standard deviation of daily returns over the past 63 trading days (about three months) for each of the 11 allocation choices. This lookback interval is common for such analyses and is within the lookback interval range of 50-80 days suggested by the author.
  3. For each day thereafter, maintain a portfolio with SPY-TLT allocations equal to those of the winning allocation choice over the specified lookback interval. We consider both same-close (requiring slight anticipation of the winning allocation choice) and next-open rebalancing executions (because such anticipation appears problematic).

We ignore small rebalancing frictions incurred daily when the allocation does not change. We initially ignore rebalancing frictions when the allocation does change, but then perform a frictions sensitivity test. Using daily dividend-adjusted opening and closing prices for SPY and TLT during July 30, 2002 (limited by TLT) through April 20, 2021, we find that: Keep Reading

Simple Term Structure ETF/Mutual Fund Momentum Strategy

Does a simple relative momentum strategy applied to tradable U.S. Treasury term structure proxies produce attractive results by picking the best duration for exploiting current interest rate trend? To investigate, we run short-term and long-term tests. The short-term test employs four exchange-traded funds (ETF) to represent the term structure:

SPDR Barclays 1-3 Month T-Bill (BIL)
iShares 1-3 Year Treasury Bond (SHY)
iShares Barclays 7-10 Year Treasury Bond (IEF)
iShares Barclays 20+ Year Treasury Bond (TLT)

The second test employs three Vanguard mutual funds to represent the term structure:

Vanguard Short-Term Treasury Fund (VFISX)
Vanguard Intermediate-Term Treasury Fund (VFITX)
Vanguard Long-Term Treasury Fund (VUSTX)

For each test, we allocate all funds at the end of each month to the fund with the highest total return over a specified ranking (lookback) interval, ranging from one month to 12 months. To accommodate the longest lookback interval, portfolio formation commences 12 months after the start of the sample. We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics. Using monthly dividend-adjusted closing prices for BIL since May 2007, for SHY, IEF and TLT since July 2002 and for VFISX, VFITX and VUSTX since October 1991, all through March 2021, we find that: Keep Reading

Stock Factor/Anomaly Momentum

Do published stock factors exhibit performance streaks exploitable via intrinsic (absolute, or time series) and relative (cross-sectional) momentum? In the March 2021 revision of their paper entitled “Factor Momentum and the Momentum Factor”, Sina Ehsani and Juhani Linnainmaa investigate stock factor portfolio monthly time series and cross-sectional momentum. They consider 15 factors for U.S. stocks (size, value, profitability, investment, momentum, accruals, betting against beta, cash flow-to-price, earnings-to-price, liquidity, long-term reversals, net share issuance, quality minus junk, residual variance and short-term reversals) and seven of these factors for global stocks. Each factor portfolio is long (short) stocks with higher (lower) expected returns based on that factor. They each month measure factor momentum as factor portfolio return from 12 months ago to one month ago. They consider six factor momentum strategies and one benchmark strategy that all exclude the stock momentum factor and are all rebalanced monthly and equal-weighted, as follows:

  • Time Series Winners –  long factor portfolios with positive momentum.
  • Time Series Losers – long factor portfolios with negative momentum.
  • Time Series Hedge– long Time Series Winners and short Time Series Losers.
  • Cross-sectional Winners –  long factor portfolios with above-median momentum.
  • Cross-sectional Losers – long factor portfolios with below-median momentum.
  • Cross-sectional Hedge – long Cross-sectional Winners and short Cross-sectional Losers.
  • Benchmark – long all factor portfolios.

Using monthly returns as available for the 15 U.S. stock anomalies since July 1963 and seven of these anomalies applied to global stocks since July 1990, all through December 2019 (mostly Kenneth French data), they find that: Keep Reading

Longer Test of Simplest Asset Class ETF Momentum Strategy

A subscriber asked for an extended test of a very simple momentum strategy that each month holds Vanguard 500 Index Fund Investor Shares (VFINX) or Vanguard Long-Term Treasury Fund Investor Shares VUSTX according to which of these funds has the highest total return over the last three months. To investigate, based on the way mutual funds report prices, we calculate past 3-month total returns using dividend-adjusted prices for month-ends and strategy returns using dividend adjusted prices for first days of the following month. We assume zero fund switching costs and no restrictions on monthly fund switching. We use buying and holding VFINX as a benchmark. Using the specified fund price series and monthly 3-month U.S. Treasury bill (T-bill) yield from the end of May 1986 (limited by VUSTX) through the beginning of March 2021, we find that: Keep Reading

SACEMS with Margin

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? To investigate effects of margin, we augment SACEMS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratiocompound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly gross total returns for SACEMS and monthly T-bill yields during July 2006 through February 2021, we find that:

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Recent Weaknesses of Factor Investing

How have value, quality, low-volatility and momentum equity factors, and combinations of these factors, performed in recent years. In their October 2020 paper entitled “Equity Factor Investing: Historical Perspective of Recent Performance”, Benoit Bellone, Thomas Heckel, François Soupé and Raul Leote de Carvalho review and put into context recent performances of these these factors/combinations as applied to medium-capitalization and large-capitalization World, U.S. and European stock universes. They consider both long-short and long-only factor portfolios and further investigate effects of (1) neutralizing beta and sector dependencies, (2) using multiple metrics for each factor and (3) including small stocks. Using firm accounting data and stock returns to support factor portfolio construction during 1995 through early 2020, they find that:

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