Strategic Allocation

Is leveraging with margin a good way to boost the performance of the “Simple Asset Class ETF Momentum Strategy” (SACEMS)? To investigate effects of margin, we augment SACEMS by: (1) initially applying 2X leverage via margin (limited by Federal Reserve Regulation T); (2) for each month with a positive portfolio return, adding margin at the end of the month to restore 2X leverage; and, (3) for each month with a negative portfolio return, liquidating shares at the end of the month to pay down margin and restore 2X leverage. Margin rebalancings are concurrent with portfolio reformations. We focus on gross monthly Sharpe ratio, compound annual growth rate (CAGR) and maximum drawdown (MaxDD) for committed capital as key performance statistics for the Top 1, equally weighted (EW) Top 2 and EW Top 3 portfolios of monthly winners. We use the 3-month Treasury bill (T-bill) yield as the risk-free rate and consider a range of margin interest rates as increments to this yield. Using monthly gross total returns for SACEMS and monthly T-bill yields during July 2006 through October 2023, we find that:

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A subscriber requested testing of a strategy that holds a combination of 50% Simple Asset Class ETF Value Strategy (SACEVS) Best Value and 50% Simple Asset Class ETF Momentum Strategy (SACEMS) equal-weighted (EW) Top 2 strategies during November through April and idle cash during May through October. We consider three strategies:

1. Best Value – EW Top 2 – hold Best Value-EW Top 2 during all months.
2. Best Value – EW Top 2 Seasonal (Idle Cash) – hold Best Value-EW Top 2 during November through April and idle cash during May through October, as requested.
3. Best Value – EW Top 2 Seasonal (6-month T-bill) – hold Best Value-EW Top 2 during November through April and 6-month U.S. Treasury bills (T-bill) bought at the beginning May each year during May through October.

We run annual statistics for each variation as in “Combined Value-Momentum Strategy (SACEVS-SACEMS)”. Annualized returns are compound annual growth rates. Maximum drawdown is the deepest peak-to-trough drawdown for these strategies based on monthly measurements over the sample period. For Sharpe ratio, to calculate excess annual return, we use average monthly yield on 3-month Treasury bills during a year as the risk-free rate for that year. Using monthly returns for SACEVS Best Value and SACEMS EW Top 2 and the specified T-bill yield during July 2006 through October 2023, we find that: Keep Reading

Is the the conventional retirement portfolio glidepath as recommended by many financial advisors, away from stocks and toward bonds over time, really optimal? In their October 2023 paper entitled “Beyond the Status Quo: A Critical Assessment of Lifecycle Investment Advice”, Aizhan Anarkulova, Scott Cederburg and Michael O’Doherty present a lifecycle income/wealth model using stationary block bootstrap simulations (average block length 120 months to preserve long-term behaviors) with labor income uncertainty, Social Security income, longevity uncertainty and historical monthly returns for stock indexes, government bonds and government bills across developed countries. They apply this model to estimate outcomes for several age-dependent, monthly rebalanced portfolios of stocks and bonds, including a representative target-date fund (TDF), as well as some fixed-percentage allocation strategies. They focus on a U.S. couple (a female and a male) who save during working years starting at age 25 and consume Social Security income and savings starting at age 65 with constant real 4% annual withdrawals. They evaluate four outcomes: (1) wealth at retirement; (2) retirement income; (3) conservation of savings; and, (4) bequest at death. Using monthly (local) real returns for domestic stock indexes, international stock indexes, government bonds and government bills as available for 38 developed countries during 1890 through 2019, they find that:

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Does machine learning reliably offer better risk-adjusted portfolio performance than traditional modern portfolio theory (MPT)? In their August 2023 paper entitled “Comparing Deep RL and Traditional Financial Portfolio Methods”, Eric Benhamou, Jean-Jacques Ohana, Beatrice Guez, David Saltiel, Rida Laraki and Jamal Atif compare principles, methodologies and risk-adjusted performances of dynamic deep reinforcement learning (DRL) and MPT. The DRL approach seeks long-only allocations that maximize Sharpe ratio (calculated assuming a zero risk-free rate). DRL training data includes individual asset returns, portfolio drawdown and contextual variables including U.S. and European interest rates, the CBOE volatility index (VIX), credit default swap prices, currency rates (U.S. dollar index), GDP and CPI forecasts, crude oil/gold/copper inventories and global, U.S., European, Japanese and emerging markets economic surprise indexes. DRL training employs an expanding window, each year training on available historical data and testing on the next year. They consider three MPT portfolios also using expanding window of historical data to estimate inputs: (1) full MPT (Markowitz); (2) minimum variance; and, (3) risk parity. Their global test data consists of daily returns of 11 futures contract series for four major equity indexes, four major bond indexes and three major commodity indexes. They assume trading frictions of 0.02% of value traded. Using the specified (groomed) data during 2000 through mid-2023, they find that: Keep Reading

Alternative assets (private equity, private market real estate, hedge funds and other assets apart from stocks and bonds) constitute approximately 30% of U.S. public pension fund portfolios and 60% of large U.S. endowment portfolios. Are they beneficial? In his August 2023 paper entitled “Have Alternative Investments Helped or Hurt?”, Richard Ennis examines impacts of alternative assets on 59 pension fund portfolios, individually and in equal-weighted composite. His key performance metric is alpha relative to static allocations to a mix of stock and bond indexes selected to match the style of each pension fund (or composite of funds) by statistical returns fitting. The stock and bond index choices are Russell 3000 stock index, MSCI ACWI ex-US stock index (hedged and unhedged) and Bloomberg US Aggregate bond index. He thereby creates a unique benchmark for each fund with which to measure its alpha. Using returns and allocations for 59 large U.S. public pension funds with a common June 30 year-end and returns for the benchmarking stock and bond indexes during 2009 through 2021, he finds that: Keep Reading

A subscriber requested comparison of four variations of an “Ivy 5” asset class allocation strategy, as follows:

1. Ivy 5 EW: Assign equal weight (EW), meaning 20%, to each of the five positions and rebalance annually.
2. Ivy 5 EW + SMA10: Same as Ivy 5 EW, but take to cash any position for which the asset is below its 10-month simple moving average (SMA10).
3. Ivy 5 Volatility Cap: Allocate to each position a percentage up to 20% such that the position has an expected annualized volatility of no more than 10% based on daily volatility over the past month, recalculated monthly. If under 20%, allocate the balance of the position to cash.
4. Ivy 5 Volatility Cap + SMA10: Same as Ivy 5 Volatility Cap, but take completely to cash any position for which the asset is below its SMA10.

To perform the tests, we employ the following five asset class proxies:

iShares 7-10 Year Treasury Bond ETF (IEF)
SPDR S&P 500 ETF Trust (SPY)
Vanguard Real Estate Index Fund (VNQ)
iShares MSCI EAFE ETF (EFA)
Invesco DB Commodity Index Tracking Fund (DBC)

We consider monthly performance statistics, annual performance statistics, and full-sample compound annual growth rate (CAGR) and maximum drawdown (MaxDD). Annual Sharpe ratio uses average monthly yield on 3-month U.S. Treasury bills (T-bills) as the risk-free rate. The DBC series in combination with the SMA10 rule are limiting with respect to sample start date and the first return calculations. Using daily and monthly dividend-adjusted closing prices for the five asset class proxies and T-bill yield as return on cash during February 2006 through July 2023, we find that:

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What conditions affect the correlation between stock and bond returns, a critical input to asset allocation decisions? In their July 2023 paper entitled “Empirical Evidence on the Stock-Bond Correlation”, Roderick Molenaar, Edouard Senechal, Laurens Swinkels and Zhenping Wang relate changes in this correlation to economic variables and analyze the implications of such changes for stock-bond portfolios. They employ rolling 36-month Spearman rank correlations for stock market and 10-year government bond returns to detect correlation changes. While considering longer periods, they focus on post-1952 monthly and post-1978 daily U.S. data (after Federal Reserve independence) as most representative of the future. Using stock and bond returns and economic data starting 1875 for the U.S., 1801 for the UK, 1871 in France and 1987 for Canada, Germany, Italy and Japan, all through 2021, they find that:

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Subscribers asked why the Simple Asset Class ETF Value Strategy (SACEVS) signaled an apparently dramatic change in allocations at the end of June. SACEVS seeks a monthly tactical edge from timing three risk premiums associated with U.S. Treasury notes, corporate bonds and stocks:

1. Term – monthly difference between the 10-year Constant Maturity U.S. Treasury note (T-note) yield and the 3-month Constant Maturity U.S. Treasury bill (T-bill) yield.
2. Credit – monthly difference between the  Moody’s Seasoned Baa Corporate Bonds yield and the T-note yield.
3. Equity – monthly difference between S&P 500 operating earnings yield and the T-note yield.

At the end of each month, the Best Value version of SACEVS picks the most undervalued premium (if any). The Weighted version of SACEVS weights all undervalued premiums (if any) according to degree of undervaluation. Using monthly SACEVS inputs during March 1989 through June 2023, we find that: Keep Reading

A subscriber asked about the performance of a strategy that each month rebalances to 60% international equities and 40% international corporate bonds (both non-U.S.), and how this performance compares to that of 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. To investigate, we use:

• iShares MSCI ACWI ex U.S. ETF (ACWX) to represent international equities.
• SPDR Bloomberg International Corporate Bond ETF (IBND) to represent international corporate bonds.

We begin the test at the end of May 2010, limited by IBND inception. We ignore monthly rebalancing frictions for both strategies. Using monthly dividend-adjusted prices for ACWX and IBND starting May 2010 and monthly gross returns for SACEVS Best Value-SACEMS EW Top 2 50-50 starting June 2010, all through May 2023, we find that: Keep Reading

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 May 2023, we find that:

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