Strategic Allocation

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

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 2023, 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 2023 (36.5 years), 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 2022 (55 years).
• Annual median sales prices for new homes during 1963 through 2022 (60 years).

Using these sources and contemporaneous monthly levels of the S&P 500 Index during January 1963 through July 2023, we find that:

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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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A subscriber requested evaluation of a streamlined version of the Simple Asset Class ETF Momentum Strategy (SACEMS) that considers only three exchange-traded funds (ETF):

• SPDR S&P 500 (SPY)
• iShares Barclays 20+ Year Treasury Bond (TLT)
• iShares iBoxx $ Investment Grade Corporate Bond (LQD)

To evaluate, we test a strategy that each month picks the one of these ETFs with the highest total return over a set momentum ranking (lookback) interval. We call the strategy Very Simple SACEMS (VSACEMS) Top 1. We consider lookback intervals of one to 12 months. We then select one of these lookback intervals and generate performance statistics similar to those for SACEMS. We consider three benchmarks:

1. SPY – buy and hold SPY.
2. SPY:SMA10 Cash – Hold SPY (3-month U.S. Treasury bills) when SPY is above (below) its 10-month simple moving average (SMA10) at the end of the prior month.
3. SPY:SMA10 TLT – Hold SPY (TLT) when SPY is above (below) its SMA10 at the end of the prior month.

Using monthly dividend-adjusted prices for the above three assets during July 2002 (limited by TLT and LQD) through May 2023, we find that: Keep Reading