Strategic Allocation

“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 January 2025, we find that:

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Does the conventional rule (inferred from 1926-1992 U.S. stocks and bonds data) that retirees can safely withdraw an inflation-adjusted 4% from their retirement accounts annually for at least 30 years hold, after accounting for market frictions? In his February 2025 paper entitled “How the 4% Rule Would Have Failed in the 1960s: Reflections on the Folly of Fixed Rate Withdrawals”, Edward McQuarrie recasts the original study including reasonable frictions/constraints, with focus on those retiring during the 1960s. He generalizes the approach by exploring whether any fixed rate of withdrawal can be sustained across a range of aggressive and conservative asset allocations. Finally, he looks at results thus far for individuals who retired in 2000. Using newly assembled net performance data for mutual funds during 1926 through 2023 and exchange-traded alternatives to such funds during 2000 through 2024, he finds that: Keep Reading

Would using a rolling, rather than an inception-to-date (ITD), lookback window for calibration of the Best Value and Weighted versions of the “Simple Asset Class ETF Value Strategy” (SACEVS) improve their performances? SACEVS allocates funds to 3-month Treasury bills (T-bill or Cash), iShares 20+ Year Treasury Bond ETF (TLT), iShares iBoxx $ Investment Grade Corporate Bond ETF (LQD) and/or SPDR SPDR S&P 500 ETF Trust (SPY) according to current valuations of term, credit and equity risk premiums. Baseline model valuations derive from ITD historical values of these premiums back to March 1989. We compare the ITD approach to two rolling windows that continually add the newest and drop the oldest historical inputs to make SACEVS more adaptive, as follows:

1. Baseline (ITD) – sets current valuations based on the full available history.
2. 20-year Rolling Window – sets current valuations based on the full available history until February 2009 (when 20 years of history becomes available) and then uses only the most recent 20 years of history.
3. 10-year Rolling Window – sets current valuations based only on the most recent 10 years of history.

To compare the three approaches, we focus on gross compound annual growth rate (CAGR), gross annual Sharpe ratio (with average T-bill yield during a year as the risk-free rate for that year) and maximum drawdown (MaxDD). For all variations, we ignore trading (switching/rebalancing) frictions, which are modest for SACEVS, and any tax implications of trading. Using monthly dividend-adjusted closes for the above assets during July 2002 through January 2025, we find that:

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A subscriber requested a test of Ray Dalio‘s All Weather (AW) portfolio with different rebalancing frequencies, allocated to exchange-traded funds (ETF) as asset class proxies as follows:

30% – Vanguard Total Stock Market (VTI)
40% – iShares 20+ Year Treasury (TLT)
15% – iShares 7-10 Year Treasury (IEF)
7.5% – SPDR Gold Shares (GLD)
7.5% – Invesco DB Commodity Tracking (DBC)

To investigate, we test:

• AW portfolios rebalanced monthly (AW-1), quarterly (AW-3), semiannually at the ends of June and December (AW-6) or annually at the end of December (AW-12).
• As a benchmark, a 60%-40% VTI-TLT portfolio rebalanced annually (60/40-12).
• For comparison, the Simple Asset Class ETF Momentum Strategy equally weighted Top 2 portfolio (SACEMS EW Top 2), the Simple Asset Class ETF Value Strategy (SACEVS) Best Value portfolio and a 50%-50% combination of these two portfolios (SACEVS-SACEMS 50-50).

We consider the following gross performance metrics, all based on monthly measurements: average monthly return, standard deviation of monthly returns, compound annual growth rate (CAGR), maximum drawdown (MaxDD) and Sharpe ratio (with the 3-month Treasury bill yield as the risk-free rate). We also compare number of rebalance actions for each portfolio. Using monthly dividend-adjusted returns for the specified assets during February 2006 (limited by DBC) through December 2024, we find that: Keep Reading

How well have popular stock screens worked over the long term and since 2000? In their December 2024 paper entitled “Formula Investing”, Marcel Schwartz and Matthias Hanauer test four popular stock screening formulas with a common portfolio framework over a long period. These formulas focus on certain firm/stock characteristics, implicitly or explicitly translating stock market anomalies into investable strategies. Specifically:

1. Piotroski’s F-Score sorts stocks on the sum of nine binary signals that distinguish financially weak from financially strong firms among value stocks.
2. Magic Formula sorts stocks on firm return on capital and stock earnings yield to discriminate based on quality and value. 
3. Acquirer’s Multiple sorts stocks solely on enterprise multiple.
4. Conservative Formula sorts stocks on volatility, momentum and net payout yield.

For each formula each quarter, they sort stocks into value-weighted or equal-weighted tenths (deciles) and examine its performance across deciles, focusing on extreme top and bottom deciles. They also regress top-minus-bottom decile returns on widely used asset pricing factors to attribute formula performance to factor exposures. They further evaluate each formula since 2000 using a long-only portfolio of the 40 top-ranked stocks. To ensure liquidity, they exclude stocks with market capitalizations below the 20th percentile of NYSE stocks. While using the latest prices, they lag other firm/stock characteristics by six months to ensure data availability. Using inputs as available to compute the four formulas for a broad sample of U.S. stocks during July 1963 through December 2022, they find that:

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One concern about simple momentum strategies is data snooping bias impounded in selection of the lookback interval(s) used to measure asset momentum. To circumvent this concern, we consider the following argument:

• The CBOE Volatility Index (VIX) broadly indicates the level of financial markets distress and thereby the tendency of investors to act complacently (when VIX is low) or to act in panic (when VIX is high).
• Complacency translates to resistance in changing market outlook (long memory and lookback intervals), while panic translates to rapid changes of mind (short memory and short lookback intervals).
• The inverse of VIX is therefore indicative of the actual aggregate current lookback interval affecting investor actions.

We test this argument by:

• Setting a range for VIX using monthly historical closes from January 1990 through July 2002, before the sample period used for any tests of the Simple Asset Class ETF Momentum Strategy (SACEMS).
• Applying buffer factors to the bottom (0.9) and top (1.1) of this actual inverse VIX range to recognize that it could break above or below the historical range in the future.
• Segmenting the buffer-extended inverse VIX range into 12 equal increments and mapping these increments by rounding into momentum lookback intervals of 1 month (lowest segment) to 12 months (highest segment).
• Applying this same method to future end-of-month inverse VIX levels to select the SACEMS lookback interval for the next month.

We test the top one (Top 1), the equal-weighted top two (EW Top 2) and the equal-weighted top three (EW Top 3) SACEMS portfolios. We focus on compound annual growth rate (CAGR), maximum drawdown based on monthly measurements, annual returns and Sharpe ratio as key performance statistics. To calculate excess annual returns for the Sharpe ratio, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Benchmarks are these same statistics for tracked (baseline) SACEMS. Using monthly levels of VIX since inception in January 1990 and monthly dividend-adjusted prices of SACEMS assets since February 2006 (initial availability of a commodities ETF), all through November 2024, we find that: Keep Reading

How sensitive is performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) to choice of momentum calculation lookback interval, and what interval works best? To investigate, we generate gross compound annual growth rates (CAGR) and maximum drawdowns (MaxDD) for SACEMS Top 1, equally weighted (EW) EW Top 2 and EW Top 3 portfolios over lookback intervals ranging from one to 12 months. All calculations start at the end of February 2007 based on inception of the commodities exchange-traded fund and the longest lookback interval. Using end-of-month total (dividend-adjusted) returns for the SACEMS asset universe during February 2006 through November 2024, we find that: Keep Reading

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