Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for July 2024 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for July 2024 (Final)
1st ETF 2nd ETF 3rd ETF

Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

Expanded/Modified SACEMS Asset Universe?

A subscriber suggested expanding and modifying the asset universe for the Simple Asset Class ETF Momentum Strategy (SACEMS) to consist of the following exchange-traded funds (ETF):

  • SPDR Portfolio S&P 500 Growth (SPYG)
  • SPDR Portfolio S&P 500 Value (SPYV)
  • iShares Russell 2000 Growth (IWO)
  • iShares Russell 2000 Value (IWN)
  • Invesco QQQ Trust (QQQ)
  • iShares MSCI EAFE Index (EFA)
  • iShares MSCI Emerging Markets Index (EEM)
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares Core U.S. Aggregate Bond (AGG)
  • iShares U.S. Real Estate ETF (IYR)
  • SPDR Gold Shares (GLD)
  • Invesco DB Commodity Index Tracking (DBC)
  • 3-month Treasury bills (Cash)

To investigate attractiveness of this alternative, we first look at compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for the expanded universe across SACEMS momentum measurement (lookback) intervals ranging from 1 to 12 months to identify effective lookback intervals. We then compare annual performance statistics of the Top 1, equal-weighted (EW) Top 2, EW Top 3 and EW Top 4 portfolios for the expanded and baseline asset universes with the SACEMS baseline lookback interval. Using monthly dividend-adjusted returns for the expanded asset universe during February 2006 (limited by DBC) through December 2021 and monthly returns for baseline SACEMS over the same period, we find that: Keep Reading

Substitute VIG for SPY in SACEVS and SACEMS?

A subscriber asked whether substituting the less volatile Vanguard Dividend Appreciation Index Fund (VIG) for SPDR S&P 500 (SPY) in the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS) would improve outcomes. To investigate, we substitute monthly VIG dividend-adjusted returns for SPY dividend-adjusted returns in the two model strategies. Because VIG is not available for the entire sample periods used in the tracked models, we splice VIG returns into the SPY position starting with inception of the former in May 2006. We then compare the spliced performance with the original baseline performance, including: gross compound annual growth rates (CAGR), gross annual returns, average gross annual returns, standard deviations of gross annual returns, gross annual Sharpe ratios and maximum drawdowns (MaxDD). In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using the specified methodology and data to generate SACEVS monthly returns starting August 2002 and SACEMS monthly returns starting July 2006, all through December 2021, we find that:

Keep Reading

Interest Rate Changes Exploitable for Sector Rotation?

A subscriber asked about a strategy that rotates among equity sectors according to changes in interests rate as set by Federal Reserve Bank monetary policy. To investigate, we consider the following nine sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF):

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 monthly effective federal funds rate (EFFR) as the interest rate. We consider two EFFR-based variables: (1) monthly change in EFFR; and, (2) 3-month slope of EFFR for signal smoothing. For each variable and each sector ETF, we consider two tests: (1) correlation of the variable with ETF return each of the next three months; and, (2) average next-month ETF returns across ranked fifths (quintiles) of the EFFR variable. The first test looks for linear relationships, and the second test looks for non-linear relationships. Measurements are at month ends, with a 1-day delay for ETF return calculations to ensure availability of EFFR data. Using monthly levels of EFFR since September 1998 and dividend-adjusted monthly levels of the above sector ETFs and of SPDR S&P 500 (SPY) since December 1998 (limited by sector ETFs), all through November 2021, we find that: Keep Reading

Leading Economic Index Exploitable for Sector Rotation?

A subscriber asked about a strategy that rotates among equity sectors according to the Leading Economic Index (LEI), published monthly by the Conference Board (see “Leading Economic Index and the Stock Market”). To assess LEI usefulness for sector rotation, we consider the following nine sector Standard & Poor’s Depository Receipts (SPDR) exchange-traded funds (ETF):

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 consider two LEI-based variables: (1) monthly change in LEI; and, (2) 3-month average change in LEI (average of current value, revised value for prior month and twice-revised value for two months ago) for signal smoothing. For each variable and each sector ETF, we consider two tests: (1) correlation of the variable with ETF return each of the next three months; and, (2) average next-month ETF returns across ranked fifths (quintiles) of the LEI variable. The first test looks for linear relationships, and the second test looks for non-linear relationships. Monthly measurements employ closes on LEI release dates, generally after the market open about three weeks after ends of calendar months reported. Using monthly changes in LEI from archived Conference Board press releases and contemporaneous dividend-adjusted daily levels of the above sector ETFs and of SPDR S&P 500 (SPY) from mid-July 2002 (limited by LEI press releases) through mid-November 2021 (233 monthly LEI observations), we find that: Keep Reading

SACEVS with Quarterly Allocation Updates

Do quarterly allocation updates for the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) work as well as monthly updates? These strategies allocate funds to the following asset class exchange-traded funds (ETF) according to valuations of term, credit and equity risk premiums, or to cash if no premiums are undervalued:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR S&P 500 (SPY)

Changing from monthly to quarterly allocation updates does not sacrifice information about lagged quarterly S&P 500 Index earnings, but it does sacrifice currency of term and credit premiums. To assess alternatives, we compare cumulative performances and the following key metrics for quarterly and monthly allocation updates: gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD), annual gross returns and volatilities and annual gross Sharpe ratios. Using monthly dividend-adjusted closes for the above ETFs during September 2002 (earliest alignment of months and quarters) through September 2021, we find that:

Keep Reading

SACEMS Hedge Portfolios

A subscriber asked about performance of Simple Asset Class ETF Momentum Strategy (SACEMS) hedge portfolios, which each month buy the asset class exchange-traded funds (ETF) in the SACEMS universe with the highest past returns and sell (short) those with the lowest. To investigate, we look at three hedge portfolios:

  • Top 1 – Bottom 1: long the ETF with the highest past return and short the ETF with the lowest.
  • EW Top 2 – EW Bottom 2: long the equal-weighted (EW) two ETFs with the highest past returns and short the two with the lowest.
  • EW Top 3 – EW Bottom 3: long the equal-weighted three ETFs with the highest past returns and short the three with the lowest. 

For each portfolio, monthly rebalancing sets the long and short sides to equal dollar amounts. We consider monthly gross portfolio  performance statistics (ignoring any rebalancing and shorting frictions), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio. To calculate annual excess returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. SACEMS Top 1, EW Top 2 and EW Top 3 SACEMS long-only portfolios serve as benchmarks. Using monthly gross returns for SACEMS ETFs (and cash) by rank during July 2006 through October 2021, we find that:

Keep Reading

Add Position Stop-gain to SACEMS?

Does adding a position take-profit (stop-gain) rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by harvesting some upside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-gains, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return rises above a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month maximum returns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

Add Position Stop-loss to SACEMS?

Does adding a position stop-loss rule improve the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) by avoiding some downside volatility? SACEMS each months picks winners from among the a set of eight asset class exchange-traded fund (ETF) proxies plus cash based on past returns over a specified interval. To investigate the value of stop-losses, we augment SACEMS with a simple rule that: (1) exits to Cash from any current winner ETF when its intra-month return falls below a specified threshold; and, (2) re-sets positions per winners at the end of the month. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using monthly total (dividend-adjusted) returns and intra-month drawdowns for the specified assets during February 2006 through September 2021, we find that: Keep Reading

Testing a 2-12 Asset Class Absolute Momentum Strategy

A subscriber asked about the performance of a strategy that each month has five equal-weighted positions:

We designate this strategy 2-12 Absolute. As requested, we also consider two variations that substitute Invesco DB Commodity Index Tracking Fund (DBC) for either TLT or LQD, and we compare 2-12 Absolute performance to that of a portfolio that each month allocates 50% to Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% to Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2. We begin tests at the end of March 2009, limited by inception of ACWI. We exclude monthly rebalancing/switching frictions for all strategies. Using monthly dividend-adjusted prices for GLD, ACWI, VNQ, TLT, LQD, DBC and SHY starting March 2009 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 starting April 2009, all through August 2021, we find that: Keep Reading

Performance of Statewide Pension Funds

When a public pension fund reports beating its benchmark, does that signify a job well done? In his July 2021 paper entitled “Cost, Performance, and Benchmark Bias of Public Pension Funds in the United States: An Unflattering Portrait”, Richard Ennis analyzes net returns of statewide pension funds in the U.S. He calculates both (1) net Sharpe ratio and (2) return versus a matched benchmark constructed via regression as the best-fit combination of the Russell 3000 stock index, the MSCI ACWI ex-U.S. stock index and the Bloomberg Barclays US Aggregate bond index. He compares performance relative to this benchmark and estimated costs for each fund. He then assesses performance of the funds versus the benchmarks they themselves construct and report against. Using self-reported data for a sample of 24 such funds self-reported via the Public Plans Data website during July 2010 through June 2020, he finds that: Keep Reading

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