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.

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

Testing a SPY-EEMV-VT-TLT-PBBBX Allocation Strategy

In reaction to “Testing the EFA-SPY-TLT-PBBBX EW Strategy”, a subscriber asked about the performance of a strategy that each year rebalances to 25% SPDR S&P 500 (SPY), 10% iShares MSCI Emerging Markets Min Vol Factor (EEMV),  15% Vanguard Total World Stock Index Fund (VT), 25% iShares Barclays 20+ Year Treasury Bond (TLT) and 25% PIA BBB Bond Fund (PBBBX). Annual rebalancing avoids short-term capital gains for taxable accounts. We again compare performance of this alternative 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 the test at the end of 2011, limited by EEMV data and the annual rebalancing rule. We ignore rebalancing frictions for both strategies. Using monthly dividend-adjusted prices for SPY, EEMV, VT, TLT and PBBBX starting December 2011 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 starting January 2012, all through June 2021, we find that:

Keep Reading

Asset Class 12-month Reversion?

A subscriber, hypothesizing that asset classes with weak past returns should revert, requested testing of a strategy that each month holds the equal-weighted three of the Simple Asset Class ETF Momentum Strategy (SACEMS) universe with the lowest cumulative returns over the past 12 months (12-month EW Bottom 3). For comparison, we use the SACEMS EW Top 3 portfolio as specified. We begin the test at the end of February 2007, limited by SACEMS inputs with a 12-month lookback interval. We ignore monthly rebalancing frictions for both strategies. Using monthly dividend-adjusted prices of the nine SACEMS asset class proxies during February 2006 through June 2021, we find that: Keep Reading

Testing the EFA-SPY-TLT-PBBBX EW Strategy

A subscriber asked about the performance of a strategy that each month rebalances to 25% international equities, 25% U.S. equities, 25% U.S. Treasuries and 25% BBB bonds, and how this performance compares 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. To investigate, we use:

We begin the test at the end of June 2006, limited by SACEMS inputs. We ignore monthly rebalancing frictions for both strategies. Using monthly dividend-adjusted prices for EFA, SPY, TLT and PBBBX starting June 2006 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 starting July 2006, all through June 2021, we find that: Keep Reading

Fixing Institutional Investing?

Why have U.S. public pension, endowment and other non-profit funds (institutional investors) consistently underperformed simple, investible passive benchmarks since 2008? How should they remedy that underperformance? In his April 2021 paper entitled “How to Improve Institutional Fund Performance”, Richard Ennis summarizes prior papers quantifying post-2008 institutional investor returns and recommends how institutions can improve this performance. Extending performance estimates from prior analyses through June 2020, he finds that: Keep Reading

Assessment of the Dragon Portfolio

A subscriber provided promotional materials for, and requested assessment of, the Artemis Capital Management Dragon portfolio. General allocations for this portfolio are:

  • 24% to secular growth such as U.S. and international stocks.
  • 21% to “long volatility and convex hedging” such as the Artemis Vega Fund and tail risk hedges (probably options and/or futures).
  • 19% to commodity trend following.
  • 18% to interest rate-sensitive assets such as U.S. Treasury bonds, Treasury Inflation-Protected Securities (TIPS) and investment grade bonds.
  • 18% to inflation protection such as gold and potentially crypto-assets.

Apparently, the fund has not yet launched and all performance data are backtested (hypothetical). Lacking detail to replicate the Dragon portfolio, we look at its hypothetical monthly returns per promotional materials. We use a 60% SPDR S&P 500 Trust (SPY) – 40% iShares 20+ Year Treasury Bond (TLT) portfolio, rebalanced monthly, as a simple hybrid benchmark. For reference, we also compare results for SPY, the Simple Asset Class ETF Value Strategy (SACEVS) Best Value portfolio and the Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 portfolio. Using gross monthly total returns for the Dragon portfolio, SPY, TLT, SACEVS Best Value and SACEMS EW Top 2 during January 2012 through April 2021, we find that: Keep Reading

SACEMS with Overnight Return Capture

In view of research indicating that overnight (close-to-open) returns are on average significantly higher than open-to-close returns, a subscriber proposed an enhancement to the Simple Asset Class ETF Momentum Strategy (SACEMS), as follows:

  • Instead of ranking SACEMS assets at the market close on the last trading day of each month, rank them at the open.
  • Sell any assets leaving SACEMS portfolios at the open.
  • Buy any assets entering SACEMS portfolios at the close.

Due to complexity of precisely programming a backtest of this setup, we instead run the following tests:

  1. Compare average daily open-to-close and close-to-open returns for each SACEMS non-cash asset over available sample periods since July 2002.
  2. Compare SACEMS portfolio performances during July 2006 through May 2021 for: (a) ranking assets at the open on the last trading day of each month and executing all trades at the open; and, (b) ranking assets at the close on the last trading day of each month and executing all trades at the close (baseline SACEMS).
  3. Calculate SACEMS portfolio performances during July 2006 through May 2021 for a variation that ranks assets at the open on the last trading day of each month, liquidates SACEMS portfolios at the open and reforms them at the close. This variation is more aggressive in exploiting an overnight return effect than the proposed approach, but is easier to program.

We consider Top 1, equal-weighted (EW) Top 2 and EW Top 3 SACEMS portfolios. We focus on full-sample gross compound annual growth rate, gross annual Sharpe ratio and maximum drawdown based on monthly data for portfolio comparisons. Using dividend-adjusted opening and closing prices for all SACEMS assets during July 2002 through May 2021, we find that: Keep Reading

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

Dynamic Retirement Portfolio Sustainable Withdrawal Rate

How can retirees estimate whether their investment strategy will sustain all the withdrawals they expect to make in retirement? In his February 2021 paper entitled “The Sustainability of (Global) Withdrawal Strategies”, Javier Estrada  presents two tools to monitor retirement plans for early signs of trouble:

  1. One evaluates sustainability of an existing withdrawal strategy.
  2. The other calculates the sustainable level of inflation-adjusted withdrawals at any given point during retirement.

Both change over time according to (1) deviations of actual returns from those used in modeling the retirement portfolio and (2) the number of years left in retirement. He also illustrates these tools for a sample of 21 countries and the world over a 120-year period assuming a 30-year retirement with 30 annual withdrawals at the beginning of each year. The terminal value one year after the final withdrawal is the bequest. All returns and portfolio values are in real (inflation-adjusted) terms. Using annual real total returns for stocks and government bonds for 21 countries in local currencies/inflation rates and the world in U.S. dollars/inflation rate from the Dimson-Marsh-Staunton database during 1900 through 2019, he finds 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

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