Strategic Allocation

Success of dynamic asset class allocations assumes that expected asset class returns, return riskiness and investor risk aversion change at least somewhat predictably over time. Are individual investors truly better off with dynamic (rather than static) allocations? In their October 2024 paper entitled “Victor Meets the Bogleheads: Comparing Static versus Dynamic Asset Allocation”, Victor Haghani and James White focus on the modest differences between static and logic-driven dynamic allocation strategies. Based on their experiences as research analysts and investment managers, they conclude that: Keep Reading

How can the low-volatility effect, whereby stocks with low past volatility tend to outperform the market on a risk-adjusted basis (but lag during long bull markets), help achieve common investment goals? In their October 2024 paper entitled “Leveraging the Low-Volatility Effect”, Lodewijk van der Linden, Amar Soebhag and Pim van Vliet test ways to use the low-volatility effect to support five distinct investment goals. Their low-volatility benchmark strategy each month holds the 100 of the 1,000 largest U.S. stocks with the lowest 36-month volatilities. They consider ways to exploit the effect in five ways:

1. To safely boost return, they integrate value (net payout yield) and momentum (return from 12 months ago to one month ago) with low-volatility by each month: (1) selecting the 500 of the 1,000 largest U.S. stocks with the lowest 36-month volatilities; and, (2) picking the 100 of these stocks with the highest combined net payout yield and momentum. 
2. To beat a conventional 60-40 stocks-bonds portfolio, they consider: (1) replacing 10% of stocks and 5% of bonds with a 15% allocation to Strategy 1; (2) assigning equal weights to stocks, bonds and Strategy 1; or, (3) allocating 70% to Strategy 1 and 30% to bonds.
3. To beat the stock market, they target a market beta of 1.00 via a 140% long position in Strategy 1, financed either by: (1) borrowing 40%, with credit spread plus the T-bill rate as the borrowing cost; or, (2) using equity market index futures, with annual return slippage and implicit costs 0.2%.
4. For absolute returns, they consider a 100% position in Strategy 1, offset by: (1) 48% short positions in speculative stocks (high volatility, low net payout yield and low momentum), assuming 2% annual shorting costs; or, (2) a 72% position in short equity market index futures, with 0.2% annual costs.
5. For crash protection compared to 5% out-of-the-money 1-month put options, they target a market beta of -0.50 by combining: (1) a 30% long position in the low-volatility benchmark with a 50% short position in speculative stocks, with credit spread over the T-bill rate as the borrowing cost; or, (2) a 70% long position in the low-volatility benchmark with a 100% short position in equity market index futures, with 0.2% annual costs.

In general, portfolio rebalancing is monthly. Using monthly data for the largest 1,000 U.S. stocks and for the other asset types specified above during 1990 through 2023, they find that: Keep Reading

Do target retirement date funds, offering glidepaths that shift asset allocations away from equities and toward bonds as target dates approach, safely generate attractive returns? To investigate, we consider seven such mutual funds offered by Vanguard, as follows:

• • Vanguard Target Retirement 2020 Fund (VTWNX)
  • Vanguard Target Retirement 2025 Fund (VTTVX)
  • Vanguard Target Retirement 2030 Fund (VTHRX)
  • Vanguard Target Retirement 2035 Fund (VTTHX)
  • Vanguard Target Retirement 2040 Fund (VFORX)
  • Vanguard Target Retirement 2045 Fund (VTIVX)
  • Vanguard Target Retirement 2050 Fund (VFIFX)

We consider as benchmarks SPDR S&P 500 ETF Trust (SPY), iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and both 80-20 and 60-40 monthly rebalanced SPY-LQD combinations. We look at monthly and annual return statistics, including compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Using monthly total returns for SPY, LQD, three target retirement date funds since October 2003 and four target retirement date funds since June 2006 (limited by Vanguard inception dates), all through September 2024, we find that:

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The Simple Asset Class ETF Value Strategy (SACEVS) includes an allocation to  iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) when the credit premium, measured monthly based on the difference between the  Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield, is undervalued. Arguably, iShares iBoxx $ High Yield Corporate Bond ETF (HYG) is a more aggressive way than LQD to exploit an undervalued credit premium. To test the effect of this aggressiveness, we substitute HYG for LQD in the SACEVS Best Value and SACEVS Weighted strategies by dovetailing HYG returns to those of LQD as soon as HYG becomes available in April 2007. We then compare performances of SACEVS with and without HYG. Using monthly dividend-adjusted returns for HYG since April 2007 and data for baseline SACEVS since July 2002, all through September 2024, we find that: Keep Reading

Can investors use leveraged exchange-traded funds (ETF) to construct attractive versions of simple 60%/40% (60/40) and 40%/60% (40/60) stocks-bonds portfolios? In their March 2020 presentation package entitled “Robust Leveraged ETF Portfolios Extending Classic 40/60 Portfolios and Portfolio Insurance”, flagged by a subscriber, Mikhail Smirnov and Alexander Smirnov consider several variations of classic stocks/bonds portfolios implemented with leveraged ETFs. They ultimately focus on a monthly rebalanced partially 3X-leveraged portfolio consisting of:

• 40% ProShares UltraPro QQQ (TQQQ)
• 20% Direxion Daily 20+ Year Treasury Bull 3X Shares (TMF)
• 40% iShares 20+ Year Treasury Bond ETF (TLT)

To validate findings, we consider this portfolio and several 60/40 and 40/60 stocks/bonds portfolios. We look at net monthly performance statistics, along with compound annual growth rate (CAGR), maximum drawdown (MaxDD) based on monthly data and annual Sharpe ratio. To estimate monthly rebalancing frictions, we use 0.5% of amount traded each month. We use average monthly 3-month U.S. Treasury bill yield during a year as the risk-free rate in Sharpe ratio calculations for that year. Using monthly adjusted prices for TQQQ, TMF, TLT and for SPDR S&P 500 ETF Trust (SPY) and Invesco QQQ Trust (QQQ) to construct benchmarks during February 2010 (limited by TQQQ inception) through September 2024, we find that: Keep Reading

Homes typically represent a substantial fraction of investor wealth. Are there reliable relationships between U.S. home prices and the U.S. stock market? For example, does a rising stock market stimulate home prices? Do falling home prices point to offsetting liquidation of equity positions. Do homes effectively diversify equity holdings? Measurements are:

• Monthly levels of the non-seasonally adjusted S&P/Case-Shiller U.S. National Home Price Index (Home Price Index) during January 1987 through June 2024, with release lag about two months.
• Annual median sales prices for existing homes pieced from from RealEstateABC.com [no longer available], National Association of Realtors and Federal Reserve Bank of St. Louis during 1968 through 2023.
• Annual median sales prices for new homes during 1963 through 2023.

Using these sources and contemporaneous monthly levels of the S&P 500 Index during January 1963 through June 2024, we find that:

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“SACEVS and SACEMS Strategy Momentum?” finds support for belief that a strategy exploiting the relative performance of Simple Asset Class ETF Value Strategy (SACEVS) Best Value and Simple Asset Class ETF Momentum Strategy (SACEMS) Equal-Weighted (EW) Top 2 boosts performance, with focus on a 60%-40% tilt toward the strategy with the stronger past returns. It also considers a full tilt (100%-0%) toward the stronger strategy for one lookback interval. Here, we examine sensitivity of the performance of the full tilt alternative (SACEVS-SACEMS Momentum) across lookback intervals ranging from one to 12 months. This alternative holds either SACEVS Best Value or SACEMS EW Top 2 according to which has the higher past return. We focus on gross compound annual growth rate (CAGR) and gross maximum drawdown (MaxDD) as essential performance metrics. As a benchmark, we use the monthly rebalanced SACEVS Best Value-SACEMS EW Top 2 50%-50% baseline (SACEVS-SACEMS 50-50 Baseline). Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 during July 2006 through July 2024, we find that:

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Do alternative “Cash” (deemed risk-free) instruments materially affect performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? Changing the proxy for Cash can affect how often the model selects Cash, as well as the return on Cash when selected. To investigate, we test separately each of the following yield and exchange-traded funds (ETF) as the risk-free asset:

• 3-month Treasury bills (Cash), a proxy for the money market as in base SACEMS
• SPDR Bloomberg Barclays 1-3 Month T-Bill (BIL)
• iShares 1-3 Year Treasury Bond (SHY)
• iShares 7-10 Year Treasury Bond (IEF)
• Vanguard Short-Term Inflation-Protected Securities Index Fund (VTIP)
• iShares TIPS Bond (TIP)

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance metrics and consider Top 1, equally weighted (EW) EW Top 2 and EW Top 3 SACEMS portfolios. Using end-of-month total (dividend-adjusted) returns for the specified assets during February 2006 (except May 2007 for BIL and October 2012 for VTIP) through July 2024, we find that:

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Are the “Simple Asset Class ETF Value Strategy” (SACEVS) and the “Simple Asset Class ETF Momentum Strategy” (SACEMS) mutually diversifying. To check, based on feedback from subscribers about combinations of interest, we look at three equal-weighted (50-50) combinations of the two strategies, rebalanced monthly:

1. 50-50 Best Value – EW Top 2: SACEVS Best Value paired with SACEMS Equally Weighted (EW) Top 2 (aggressive value and somewhat aggressive momentum).
2. 50-50 Best Value – EW Top 3: SACEVS Best Value paired with SACEMS EW Top 3 (aggressive value and diversified momentum).
3. 50-50 Weighted – EW Top 3: SACEVS Weighted paired with SACEMS EW Top 3 (diversified value and diversified momentum).

We consider as a benchmark a simple technical strategy (SPY:SMA10) that holds SPDR S&P 500 ETF Trust (SPY) when the S&P 500 Index is above its 10-month simple moving average and 3-month U.S. Treasury bills (Cash, or T-bills) when below. We also test sensitivity of results to deviating from equal SACEVS-SACEMS weights. Using monthly gross returns for SACEVS, SACEMS, SPY and T-bills during July 2006 through July 2024, we find that: Keep Reading

A subscriber observed and asked: “Many Bogleheads at Vanguard prefer a buy and hold portfolio of 50% SPY, 25% each BND and EFA [rebalanced annually]. How does this compare to your current strategy? And how does it compare to buy and hold QQQ?” To compare, we compute gross monthly returns for the following six strategies:

1. SPY-EFA-BND Portfolio: the specified combination of SPDR S&P 500 ETF Trust (SPY), iShares MSCI EAFE ETF (EFA) and Vanguard Total Bond Market Index Fund (BND), rebalanced at the end of each June.
2. Simple Asset Class ETF Value Strategy (SACEVS) Best Value: the best SACEVS asset, reselected monthly.
3. SACEVS Weighted: the weighted combination of undervalued SACEVS assets, rebalanced monthly (probably the most closely related to SPY-EFA-BND Portfolio)..
4. Simple Asset Class ETF Momentum Strategy (SACEMS) EW Top 2: the equal-weighted combination of the top two SACEMS assets, reformed monthly.
5. Best Value – EW Top 2: the monthly rebalanced combination of 50% SACEVS Best Value and 50% SACEMS EW Top 2.
6. Buy-and-hold QQQ: buy and hold Invesco QQQ Trust (QQQ).

We focus on compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as essential performance statistics. Using monthly dividend-adjusted prices for SPY, EFA, BND and QQQ and monthly gross returns for SACEVS and SACEMS during April 2007 (limited by inception of BND) through June 2024, we find that: Keep Reading