Strategic Allocation

Can expected trading frictions, as derived from trading volume forecasts, materially improve active stock portfolio net performance? In the May 2024 version of their paper entitled “Trading Volume Alpha”, flagged by a subscriber, Ruslan Goyenko, Bryan Kelly, Tobias Moskowitz, Yinan Su and Chao Zhang explore optimization of net stock portfolio performance by accounting for expected trading frictions as implied by stock trading volume forecasts. They apply neural networks to forecast stock trading volumes based on past returns/volumes, firm characteristics and various events associated with volume fluctuations (such as earnings releases). They then run experiments that use volume forecasts to quantify expected portfolio-level costs and benefits of trading. For example, they test the net benefit (trading volume alpha) of accounting for expected trading volumes/frictions within each of 153 factor portfolios. Using the specified data for an average 3,500 stocks per day during 2018 through 2022 (a 3-year neural network training subsample and a 2-year testing subsample), they find that: Keep Reading

A subscriber asked about an update of “Simplest Asset Class ETF Momentum Strategy?”, which each month holds SPDR S&P 500 ETF Trust (SPY) or iShares 20+ Year Treasury Bond (TLT) depending on which has the higher total return over the last three months, including a direct comparison to 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 June 2006, limited by SACEMS inputs. We ignore monthly switching frictions for both strategies. Using monthly dividend-adjusted prices for SPY and TLT starting March 2006 and monthly gross returns for 50-50 SACEVS Best Value and SACEMS EW Top 2 starting July 2006, all through November 2024, we find that: Keep Reading

A subscriber wondered whether choosing the fourth place asset class exchange-traded fund (ETF) rather than the third place class ETF for monthly reformation of the Simple Asset Class ETF Momentum Strategy (SACEMS) would matter if the difference in respective past returns over the ranking interval is less than 0.5%. To investigate, we take a broad, systematic approach and test the following two scenarios:

1. Impose a buffer of -0.5% when reforming the SACEMS portfolio. Specifically, each month subtract 0.5% from the past returns of the first, second and third places of last month before reranking. This test captures the subscriber question as a subset, but tends to increase trading due to small ranking return differences.
2. Impose a buffer of 0.5% when reforming the SACEMS portfolio. Specifically, each month add 0.5% to the past returns of ETFs for the first, second and third places of last month before reranking. This test tends to suppress trading due to small ranking return differences. 

For the second scenario, we also look at effects of buffers larger than 0.5% for the Equal-Weighted (EW) Top 2 SACEMS portfolio. Using monthly SACEMS outputs during June 2006 through November 2024, we find that: Keep Reading

We have updated monthly allocations and performance data for the Simple Asset Class ETF Momentum Strategy (SACEMS) and the Simple Asset Class ETF Value Strategy (SACEVS). We have also updated performance data for the Combined Value-Momentum Strategy.

SACEMS rankings this month are so close that post-close ETF price adjustments could affect them. If so, we will revise the winners to reflect the adjustments for consistency with backtest data.

We have updated the Trading Calendar to incorporate data for November 2024.

The home page, Simple Asset Class ETF Momentum Strategy (SACEMS) and Simple Asset Class ETF Value Strategy (SACEVS) now show preliminary positions for December 2024. SACEMS rankings are close and could change by the early close. SACEVS allocations are unlikely to change by the early close.

A subscriber requested review of “Accelerating Dual Momentum [ADM] Investing”, which allocates all funds to U.S. stocks, international (ex-U.S.) small-capitalization stocks or long-term U.S. Treasury bonds, as follows:

1. Each month, calculate for each of the two equity assets the sum of its 1-month, 3-month and 6-month past returns.
2. If both sums are negative, buy U.S. Treasury bonds.
3. If both sums are not negative, buy the equity asset with the higher sum.

To investigate, we apply these rules to three exchange-traded funds (ETF):

• SPDR S&P 500 (SPY) to represent U.S. stocks.
• iShares MSCI EAFE Small-Cap ETF (SCZ) to represent international small stocks.
• iShares 20+ Year Treasury Bond (TLT) to represent long-term U.S. Treasury bonds.

Using end-of-month dividend-adjusted prices of these ETFs during December 2007 (limited by SCZ) through October 2024, we find that: Keep Reading

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:

Keep Reading

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