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Value Investing Strategy (Strategy Overview)

Allocations for August 2021 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for August 2021 (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.

More International Equity Market Granularity for SACEMS?

A subscriber asked whether more granularity in international equity choices for the “Simple Asset Class ETF Momentum Strategy” (SACEMS), such as considered by Decision Moose, would improve performance. To investigate, we augment/replace international developed and emerging equity market exchange-traded funds (ETF) such that the universe of assets becomes:

  • SPDR S&P 500 (SPY)
  • iShares Russell 2000 Index (IWM)
  • iShares Europe (IEV)
  • iShares MSCI Japan (EWJ)
  • iShares MSCI Pacific ex Japan (EPP)
  • iShares MSCI Emerging Markets Index (EEM)
  • iShares JPMorgan Emerging Markets Bond Fund (EMB)
  • iShares Latin America 40 (ILF)
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • Vanguard REIT ETF (VNQ)
  • SPDR Gold Shares (GLD)
  • PowerShares DB Commodity Index Tracking (DBC)
  • 3-month Treasury bills (Cash)

We compare original (SACEMS Base) and modified (SACEMS Granular), each month picking winners from their respective sets of ETFs based on total returns over a fixed lookback interval. We focus on gross compound annual growth rate (CAGR), gross maximum drawdown (MaxDD) and gross annual Sharpe ratio (average annual excess return divided by standard deviation of annual excess returns, using average monthly T-bill yield during a year to calculate excess returns) as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. Using daily and monthly total (dividend-adjusted) returns for the specified assets during February 2006 through September 2020, we find that: Keep Reading

Asset Class Momentum Faster During Bear Markets?

A subscriber asked whether the optimal momentum ranking (lookback) interval for the “Simple Asset Class ETF Momentum Strategy” (SACEMS) shrinks during bear markets for U.S. stocks. To investigate, we compare SACEMS monthly performance statistics when the S&P 500 Index at the previous monthly close is above (bull market) or below (bear market) its 10-month simple moving average. We consider Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners for the baseline SACEMS lookback interval. We focus on monthly reward/risk (average monthly return divided by standard deviation of monthly returns) as a key performance metric. In a robustness test for the EW Top 3 portfolio, we consider lookback intervals ranging from one to 12 months. Using monthly total (dividend-adjusted) returns for SACEMS assets since February 2006 and monthly S&P 500 Index level since September 2005, all through September 2020, we find that:

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Alternative Simplest Asset Class Momentum Strategies

In response to “Tech Premium Boost for Simplest Asset Class Momentum Strategy?”, a subscriber asked about testing the combination of Vanguard Growth Index Fund (VUG) and Vanguard Total Bond Market Index Fund (BND) in the “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPDR S&P 500 (SPY) or iShares Barclays 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months. To investigate, we run a horse race between the strategy executed with SPY and TLT (SPY-TLT), the strategy executed with Invesco QQQ Trust (QQQ) and TLT (QQQ-TLT) and the requested alternative (VUG-BND). We focus on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) as performance metrics and assess robustness across lookback intervals of one to 12 months. Using monthly dividend-adjusted prices for SPY, QQQ, VUG, TLT and BND during April 2007 (limited by BND) through September 2020, we find that:

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Breaking Asset Ranking Systems into Pairs

Is there a better way to identify attractive and unattractive assets than simply ranking them? In the August 2020 version of their paper entitled “Decoding Systematic Relative Investing: A Pairs Approach”, Christian Goulding, Campbell Harvey and Alex Pickard examine a long-short strategy that periodically reforms a portfolio by evaluating all possible pairs within an asset universe based on:

  1. High positive signal-future return correlation for each asset on its own in a pair.
  2. Low (or negative) signal correlation between assets in the pair.
  3. Low (or negative) signal-future return correlations between one asset and the other in the pair.

They use these three inputs to calculate a (somewhat complex) composite score for each pair. Among pairs with the highest composite scores, the member with the higher (lower) signal goes to the long (short) side of the portfolio. They assess usefulness of the three conditions and the composite score using a momentum signal calculated as average past monthly return over a specified lookback interval minus its inception-to-date mean and divided by its inception-to-date standard deviation. They split their sample roughly in half and use the first half for detection of profitable pair strategies and the second half to measure out-of-sample performance. They further test an explicit tactical allocation strategy using a 12-month momentum lookback interval, a rolling 10-year monthly composite score and a scheme that weights the top four asset pairs according to respective composite scores. As a benchmark, they use a comparable conventional relative momentum strategy that simply ranks assets on momentum signal. Using monthly returns for 13 broad asset-class indexes encompassing equities, bonds, real estate investment trusts (REIT) and commodities (78 possible pairs) as available through May 2020, they find that:

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Portfolio Reformation Schedule and Equity Factor Returns

Does equity factor portfolio reformation (rebalancing) schedule materially affect portfolio performance? In their February 2020 paper entitled “Rebalance Timing Luck: The (Dumb) Luck of Smart Beta”, Corey Hoffstein, Nathan Faber and Steven Braun measure rebalance timing luck (RTL) in returns for long-only portfolios of S&P 500 stocks selected based on:

  • Value – trailing 12-month earnings yield.
  • Quality – average of rankings for return on equity, accruals ratio (reverse ranking) and leverage ratio (reverse ranking).
  • Momentum – return from 12 months ago to one month ago.
  • Low Volatility – 12-month realized volatility.

They quantify RTL as dispersion in portfolio performance (best minus worst) across different reformation schedules. They also vary number of stocks (50 to 400) and portfolio reformation frequency (annual, semi-annual or quarterly) to assess RTL sensitivity to these parameters. For corroboration, they measure RTL for replications of existing S&P Dow Jones Enhanced Value, Quality, Momentum and Low Volatility indexes. Using data for S&P 500 stocks starting July 2000 and for factor-based indexes starting January 2001, all through September 2019, they find that:

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How Canadian Pension Funds Outperform

Which institutional investors do best and why? In the September 2020 update of their paper entitled “The Canadian Pension Fund Model: A Quantitative Portrait”, Alexander Beath, Sebastien Betermier, Chris Flynn and Quentin Spehner compare performances of Canadian pension funds and those of other countries, focusing on Sharpe ratio of the fund assets, Sharpe ratio of the fund net portfolio (long assets and short liabilities) and correlation between fund assets and liabilities. They look at both large (over $10 billion U.S. dollars in assets as of 2018) and small funds. They consider two test periods, five years (2014-2018) and 15 years (2004-2018), excluding funds with missing annual data. The 5-five year sample has 250 funds from 11 countries. The 15-year sample has 105 funds. After comparing performance, they look for reasons why Canadian performance differs. Using performance data, asset allocation strategies and cost structures for the selected 250 pension funds, they find that:

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SACEMS with Three Copies of Cash

Subscribers have questioned selecting assets with negative past returns within the “Simple Asset Class ETF Momentum Strategy” (SACEMS). Inclusion of Cash as one of the assets in the SACEMS universe of exchange-traded funds (ETF) prevents the SACEMS Top 1 portfolio from holding an asset with negative past returns. To test full dual momentum versions of SACEMS equally weighted (EW) Top 2 and EW Top 3 SACEMS portfolios, we add two more copies of Cash to the universe, thereby preventing both of them from holding assets with negative past returns. We focus on the effects of adding two copies of Cash on compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) of SACEMS EW Top 2 and EW Top 3 portfolios. Using monthly dividend adjusted closing prices for the asset class proxies and the yield for Cash during February 2006 through July 2020, we find that:

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Allocations and Returns of Endowments

How do U.S. non-profit endowment funds allocate and perform? In their November 2019 paper entitled “The Risk, Reward, and Asset Allocation of Nonprofit Endowment Funds”, Andrew Lo, Egor Matveyev and Stefan Zeume examine recent asset allocations and investment returns of U.S. public non-profit endowment funds. Due to the unstructured nature of asset reporting, they manually assign each asset in each fund to one of nine categories: (1) public equity; (2) fixed income; (3) private equity; (4) cash instruments; (5) hedge funds; (6) real estate; (7) real assets and real return; (8) trusts; and, (9) cooperative investments. Using tax return data encompassing 34,170 endowment funds during 2009 through 2018, they find that: Keep Reading

Effectiveness of Various Risk Controls during the COVID-19 Crash

How well did previously identified portfolio risk management strategies work during the COVID-19 market crash? In their July 2020 paper entitled “Strategic Risk Management: Out-of-Sample Evidence from the COVID-19 Equity Selloff”, Campbell Harvey, Edward Hoyle, Sandy Rattray and Otto Van Hemert extended analyses of risk management strategies they identified in a 2016-2019 series of papers with an out-of-sample test of the February-March 2020 stock market sell-off. These strategies include:

  • Long put options, short credit risk, long bonds or long gold.
  • Trend following based on time series/intrinsic momentum (past return divided by volatility of returns over a specified lookback interval) or on moving average crossovers.
  • Holding defensive stocks (based on profitability, payout, growth, safety or quality).
  • Volatility targeting (increasing/decreasing exposure when past volatility is relative low/high).
  • Rebalancing a stocks-bonds portfolio only half way and only when recent (1, 3 or 12 months) portfolio return is above its historical average.

Extending analyses from their prior papers through March 2020 to capture the COVID-19 crash, they find that:

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SACEVS Best Value + SACEMS EW Top 2?

A subscriber asked for a comparison of two 50%-50% monthly rebalanced combinations of Simple Asset Class ETF Value Strategy (SACEVS) and Simple Asset Class ETF Momentum Strategy (SACEMS) portfolios, as follows:

  1. 50-50 Best Value + EW Top 2: SACEVS Best Value plus SACEMS EW Top 2, employing a somewhat more aggressive momentum portfolio.
  2. 50-50 Best Value + EW Top 3: Best Value plus SACEMS equal-weighted (EW) Top 3, as tracked at “Combined Value-Momentum Strategy (SACEVS-SACEMS)”.

To investigate, we run the two combinations and compare cumulative performances and annual performance statistics. Using monthly SACEVS Best Value and SACEMS EW Top 2 and EW Top 3 portfolio returns commencing July 2006 (limited by SACEMS), we find that: Keep Reading

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