Strategic Allocation

Is there a better way than the Fed model to measure relative attractiveness of equities and bonds. In his October 2020 paper entitled “Towards a Better Fed Model”, Raymond Micaletti examines seven Fed Model alternatives, each comparing a 10-year forward annualized estimate of equity returns to the yield of 10-year constant maturity U.S. Treasury notes (T-note). The seven estimates of future equity returns are based on autocorrelation-corrected quarterly regressions using 10 years of past quarterly data for one of: (1) Aggregate Investor Allocation to Equities (AIAE); (2) Cyclically-Adjusted Price-to-Earnings Ratio (CAPE); (3) Tobin’s Q (QRATIO); (4) Market Capitalization-to-Nominal GDP (MC/GDP); (5) Market Capitalization-to-Adjusted Gross Value Added (MC/AGVANF); (6) Market Capitalization-to-Household and Non-Profit Total Assets (MC/HHNPTA); and, (7) Household and Non-Profit Equity Allocation-to-Nominal GDP (HHNPEQ/GDP). He calculates AIAE as total market value of equities divided by the sum of total market value of equities and total par value of bonds, approximated by adding the liabilities of five categories of borrowers. He then tests for each alternative a tactical asset allocation (TAA) strategy that each month weights equities and bonds based on a modified z-score of the forecasted 10-year equity risk premium (equity return minus T-note yield) computed by subtracting the median and dividing by the standard deviation of actual monthly premiums over the past 10 years. If modified z-score is greater than 1 (less than -1), the strategy is 100% in equities (0% in equities). In between those thresholds, weights are based on linear interpolation. Using quarterly data from the Archival Federal Reserve Economic Database (ALFRED) and Robert Shiller’s data library and daily U.S. equity market returns and U.S. Treasury bond/note roll-adjusted futures returns as available from the end of the fourth quarter of 1951 through the end of the third quarter of 2020, he finds that: Keep Reading

Target-date funds (TDFs) are popular fund-of-funds retirement investments that offer asset class diversification and periodic rebalancing aimed at a specific retirement year. TDFs typically charge layers of fees (fund-of-funds fee plus fees of underlying funds). Can investors do better themselves? In their October 2020 paper entitled “Off Target: On the Underperformance of Target-Date Funds”, David Brown and Shaun Davies assess feasibility and costs of emulating TDFs with low-cost exchange-traded funds (ETF) based on publicly disclosed TDF initial portfolio allocations and dynamic adjustments (glide paths). They identify TDFs as those mutual funds with one of 2005, 2010, 2015, 2020, 2025, 2030, 2035, 2040, 2045, 2050, 2055 or 2060 in their names. They replicate TDFs by matching each of their holdings to the one of 50 Vanguard ETFs (as available when they reach $50 million in assets) with the highest full-sample monthly correlation of returns. Using monthly returns, holdings and expense ratios for TDFs and the funds they hold and monthly returns for the 50 Vanguard ETFs during January 2006 through December 2017, they find that:

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Are aggregate periodic stocks-bonds rebalancing actions of Target Date Funds (TDF), which trade against momentum, increasingly affecting U.S. stock market dynamics? In their October 2020 paper entitled “Retail Financial Innovation and Stock Market Dynamics: The Case of Target Date Funds”, flagged by a subscriber, Jonathan Parker, Antoinette Schoar and Yang Sun examine market impacts of Target Date Funds (TDFs), assets of which have grown from less than $8 billion in 2000 to more than $2.3 trillion (of roughly $21 trillion in U.S. mutual funds) in 2019. Using quarterly data on TDF holdings, monthly U.S. stock market and Vanguard Total Bond Market Index Fund (bond market) returns and monthly data for stocks held by and similar to those held by TDFs during the third quarter of 2008 through the fourth quarter of 2018 (excluding three quarters with suspect data), they find that:

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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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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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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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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

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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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

A subscriber asked whether forcing the Simple Asset Class ETF Momentum Strategy (SACEMS) to agree with the Simple Asset Class ETF Value Strategy (SACEVS) when the latter assigns zero weight to stocks or government bonds improves the performance of the former. Specifically, the suggested change would force to Cash in SACEMS any allocation to SPDR S&P 500 ETF Trust (SPY) or iShares 20+ Year Treasury Bond ETF (TLT) occurring when SACEVS allocates 0% to SPY or TLT, respectively. To investigate, we impose this additional condition on SACEMS and compare detailed monthly and annual performance statistics for this new version of SACEMS (New) to the original version (Base). Using monthly SACEVS allocations and monthly dividend-adjusted prices of the SACEMS universe ETFs during February 2006 through July 2020, we find that: Keep Reading