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

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) through August 2023, we find that:

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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

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 2023, we find 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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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 and top 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 equally weighted top two (EW Top 2) and the equally 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 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 July 2023, we find that: Keep Reading

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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The four iShares Core Asset Allocation exchange-traded funds (ETF) offer exposures to U.S. stocks, global stocks and bonds semiannually rebalanced to fixed weights, as follows.

1. iShares Core Conservative Allocation (AOK) – 30% stocks and 70% bonds (30-70).
2. iShares Core Moderate Allocation (AOM) – 40% stocks and 60% bonds (40-60).
3. iShares Core Growth Allocation (AOR) – 60% stocks and 40% bonds (60-40).
4. iShares Core Aggressive Allocation (AOA) – 80% stocks and 20% bonds (80-20).

Each fund holds a portfolio of seven iShares Core stocks and bonds ETFs, thereby compounding management costs and fees. Do these funds of funds offer attractive performance? To investigate, we compare performance statistics for these funds with those for comparably weighted and rebalanced combinations of SPDR S&P 500 Trust (SPY) and iShares 20+ Year Treasury Bond (TLT), or SPY and iShares iBoxx $ Investment Grade Corporate Bond (LQD). We start tests at the end of December 2008 (about a month after inception of the asset allocation ETFs). We ignore semiannual rebalancing frictions for the SPY-TLT and SPY-LQD comparison strategies. Using semiannual dividend-adjusted prices for all specified funds during December 2008 through June 2023, we find that: Keep Reading