Objective research to aid investing decisions

Value Investing Strategy (Strategy Overview)

Allocations for April 2024 (Final)
Cash TLT LQD SPY

Momentum Investing Strategy (Strategy Overview)

Allocations for April 2024 (Final)
1st ETF 2nd ETF 3rd ETF

Strategic Allocation

Is there a best way to select and weight asset classes for long-term diversification benefits? These blog entries address this strategic allocation question.

Cheap Options for Stock Market Crash Protection

Does the difference in individual stock/market return relationships between good times (relatively low correlations) and bad times (relatively high correlations) present an easy and efficient way to hedge against stock market crashes (tail risk)? In their March 2023 paper entitled “Tail Risk Hedging: The Search for Cheap Options”, Poh Ling Neo and Chyng Wen Tee test the ability of a portfolio of liquid but cheap put options on individual stocks to protect against equity market crashes. They reason that:

  • These options are inexpensive compared to equity index put options.
  • During good times, the relatively low return correlations across stocks limit option portfolio drag.
  • During market crashes, the spike in these correlations confers on the option portfolio tail risk protection comparable to that of equity index put options.

Their tests encompass three stock market regimes: (1) up months have positive monthly returns and no daily return less than -5%; (2) down months have negative monthly returns but no daily return less than -5%; and, (3) tail risk months have at least daily return less than -5%. At the end of each month, they construct a crash protection put option portfolio as follows:

  • Select an out-of-the-money put option for each optionable stock with delta closest to -10% and six months to a year until expiration.
  • Exclude those with ex-dividend dates prior to expiration.
  • Exclude those with bid-ask spreads over 50% of the bid-ask midpoint.
  • Allocate 2% of the value of the S&P 500 Index position equally to each of the cheapest 20% of remaining put options.

Most analyses assume option buys and sells occur at bid-ask midpoints (no frictions), but they do look at impacts of effective bid-ask spreads up to 50% of the quoted spread. Using daily returns for the S&P 500 Index, S&P 500 Index put options and individual U.S. stock put options during January 1996 through December 2020, they find that: Keep Reading

Conditionally Substitute SSO for SPY in SACEVS and SACEMS?

A subscriber asked about boosting the performance of the Simple Asset Class ETF Value Strategy (SACEVS) and the Simple Asset Class ETF Momentum Strategy (SACEMS), and thereby the Combined Value-Momentum Strategy (SACEVS-SACEMS), by substituting ProShares Ultra S&P500 (SSO) for SPDR S&P 500 ETF Trust (SPY) in these strategies whenever:

  1. SPY is above its 200-day simple moving average (SMA200); and,
  2. The CBOE Volatility Index (VIX) SMA200 is below 18.

Substitution of SSO for SPY applies to portfolio holdings, but not SACEMS asset ranking calculations. To investigate, we test all versions of SACEVS, SACEMS and monthly rebalanced 50% SACEVS-50% SACEMS (50-50) combinations. We limit SPY SMA200 and VIX SMA200 conditions to month ends as signals for next-month actions (no intra-month changes). We consider baseline SACEVS and SACEMS (holding SPY as indicated) and versions of SACEVS and SACEMS that always hold SSO instead of SPY as benchmarks. We look at average gross monthly return, standard deviation of monthly returns, monthly gross reward/risk (average monthly return divided by standard deviation), gross compound annual growth rate (CAGR), maximum drawdown (MaxDD) and gross annual Sharpe ratio as key performance metrics. In Sharpe ratio calculations, we employ the average monthly yield on 3-month U.S. Treasury bills during a year as the risk-free rate for that year. Using daily unadjusted SPY and VIX values for SMA200 calculations since early September 2005 and monthly total returns for SSO since inception in June 2006 to modify SACEVS and SACEMS inputs, all through February 2023, we find that: Keep Reading

Machine Learning Applied to U.S. Sector Rotation

Can machine learning perfect equity sector rotation? In the January 2023 version of their paper entitled “Deep Sector Rotation Swing Trading”, flagged by a subscriber, Joel Bock and Akhilesh Maewal present a sector rotation strategy guided by multiple-input, multiple output deep learning model. The strategy chooses weekly from among 11 U.S. sectors using exchange-traded fund (ETF) proxies. Specifically, each week during each year, they:

  • Train the machine learning model on the last two years of weekly (Friday close) historical sector ETF prices and volumes and sometimes auxiliary economic data (10-year U.S. Treasury yield, USD currency index, crude oil proxy and stock market volatility) to predict next-week opening and closing prices for each ETF.
  • Compare the predicted return estimate for each ETF to a dynamically updated threshold return to screen for potential buys.
  • Apply additional filters to screen out potential buys with unusual past losses to accommodate investor loss aversion.
  • At the next-week open, allocate available capital to surviving sector ETFs based on respective past win rate (profitable trade) and respective past sector trade momentum.
  • Liquidate all positions just prior to the next-week close.

Their benchmark is buying and holding the S&P 500 Index with reinvested dividends. Using weekly inputs as described during January 2012 through December 2022, they find that:

Keep Reading

Add REITs to SACEVS?

What happens if we extend the “Simple Asset Class ETF Value Strategy” (SACEVS) with a real estate risk premium, derived from the yield on equity Real Estate Investment Trusts (REIT), represented by the FTSE NAREIT Equity REITs Index? To investigate, we apply the SACEVS methodology to the following asset class exchange-traded funds (ETF), plus cash:

3-month Treasury bills (Cash)
iShares 20+ Year Treasury Bond (TLT)
iShares iBoxx $ Investment Grade Corporate Bond (LQD)
SPDR Dow Jones REIT (RWR) through September 2004 dovetailed with Vanguard REIT ETF (VNQ) thereafter
SPDR S&P 500 (SPY)

This set of ETFs relates to four risk premiums, as specified below: (1) term; (2) credit (default); (3) real estate; and, (4) equity. We focus on effects of adding the real estate risk premium on gross compound annual growth rates (CAGR), maximum drawdowns (MaxDD) and annual Sharpe ratios of the Best Value (picking the most undervalued premium) and Weighted (weighting all undervalued premiums according to degree of undervaluation) versions of SACEVS. Using lagged quarterly S&P 500 earnings, monthly S&P 500 Index levels and monthly yields for 3-month U.S. Treasury bill (T-bill), the 10-year Constant Maturity U.S. Treasury note (T-note), Moody’s Seasoned Baa Corporate Bonds and FTSE NAREIT Equity REITs Index since March 1989 (limited by availability of earnings data), and monthly dividend-adjusted closing prices for the above asset class ETFs since July 2002, all through February 2023, we find that: Keep Reading

TIP as Return Predictor Across Asset Classes

“Simplified Offensive, Defensive and Risk Mode Identification Momentum Strategy” describes a strategy that each month holds offensive (defensive) assets when average return on iShares TIPS Bond ETF (TIP) over the past 1, 3, 6 and 12 months is positive (negative). Is past return of TIP, which purely impounds investor expectations for U.S. inflation, a reliable indicator of future asset class returns? To investigate, we relate TIP returns to future returns for each of the following exchange-traded fund (ETF) asset class proxies:

  • Equities:
    • SPDR S&P 500 (SPY)
    • iShares Russell 2000 Index (IWM)
    • iShares MSCI EAFE Index (EFA)
    • iShares MSCI Emerging Markets Index (EEM)
  • Bonds:
    • iShares Barclays 20+ Year Treasury Bond (TLT)
    • iShares iBoxx $ Investment Grade Corporate Bond (LQD)
    • iShares JPMorgan Emerging Markets Bond Fund (EMB)
  • Real assets:
    • Vanguard REIT ETF (VNQ)
    • SPDR Gold Shares (GLD)
    • Invesco DB Commodity Index Tracking (DBC)

We consider both linear correlation and non-linear ranking tests. We look at TIP returns over the past 1, 3, 6 and 12 months separately, and as an average of these past returns (average past TIP return). Using monthly dividend-adjusted returns for TIP and the above asset class proxies as available during December 2003 (limited by TIP) through February 2023, we find that: Keep Reading

Risk Metric That Combines Drawdown and Recovery Time

Is portfolio downside risk better manageable by combining drawdown and recovery into a single “submergence” metric? In their February 2023 paper entitled “Submergence = Drawdown Plus Recovery”, Dane Rook, Dan Golosovker and Ashby Monk present submergence density as a new risk metric to help investors analyze asset/portfolio drawdown and recovery jointly. They define submergence (s) of an asset/portfolio as percentage of current value below its past highwater mark. They define submergence density (d) during a given sample period as the κ-root of the sum of measurement interval submergences each raised to the κ-power during the sample period, as follows:

When κ=1,  submergence density is arithmetic average drawdown. When κ=∞ (or a very high number), submergence density is maximum drawdown. They use κ = 5 in examples to represent typical investor drawdown sensitivity. They define submergence risk-adjusted return as excess return (relative to the risk-free rate) minus a fraction (Θ) of submergence density, using the range 0.2 to 0.5 as reasonable for Θ. They apply submergence to several market indexes and discuss portfolio diversification and rebalancing in the context of reducing submergence overlaps. Using monthly excess returns for U.S. stocks, corporate bonds and Treasuries (S&P 500 Index, ICE BoA Corporate Bond Index and Bloomberg Treasuries Index, respectively) during 1979 through 2022, they find that:

Keep Reading

Simplified Offensive, Defensive and Risk Mode Identification Momentum Strategy

Can investors achieve attractive asset class momentum strategy performance by applying mixed-lookback interval momentum to different risk-on (offensive) and risk-off (defensive) sets of exchange-traded funds (ETF), and to a separate risk mode identification ETF? In their February 2023 paper entitled “Dual and Canary Momentum with Rising Yields/Inflation: Hybrid Asset Allocation (HAA)”, Wouter Keller and Jan Willem Keuning present a simplification of the prior Bold Asset Allocation strategy. This Hybrid Asset Allocation strategy consists of the following baseline asset universes and rules, with a single asset momentum metric (equal-weighted average return over the past 1, 3, 6 and 12 months):

  • When TIP momentum is positive (negative), use the offensive (defensive) mode.
  • When in offensive mode, hold the equal-weighted four of SPY, IWM, VWO, VEA, VNQ, DBC, IEF and TLT with the strongest momentum, except replace any of the top four with non-positive momentum by the one of BIL and IEF with the strongest momentum for crash protection.
  • When in defensive mode, hold the one of BIL and IEF with the strongest momentum.

They reform the portfolio monthly, assuming constant 0.1% 1-way trading frictions. Using modeled monthly total returns prior to ETF inception and actual monthly total returns after inception for each specified ETF during December 1970 through December 2022, they find that: Keep Reading

Review of the Golden Butterfly Portfolio

A subscriber requested review of the Golden Butterfly (GB) portfolio, which assigns equal weights to the total stock market, small-capitalization value stocks, long-term government bonds, short-term government bonds and gold. To investigate, we use the following exchange-traded funds (ETF) as asset class proxies, respectively:

  • Vanguard Total Stock Market Index Fund (VTI)
  • iShares S&P Small-Cap 600 Value Fund (IJS)
  • iShares Barclays 20+ Year Treasury Bond (TLT)
  • iShares 1-3 Year Treasury Bond (SHY)
  • SPDR Gold Shares (GLD)

We consider either monthly or annual rebalancings to equal weight, ignoring associated trading frictions. Using monthly dividend-adjusted prices for the five ETFs during November 2004 (limited by GLD) through December 2022, we find that: Keep Reading

Testing SACEMS with Different Bull-Bear Lookback Intervals

Referring to “Asset Class Momentum Faster During Bear Markets?”, a subscriber asked about performance of a modification of the equal-weighted top three (EW Top 3) version of the “Simple Asset Class ETF Momentum Strategy” (SACEMS) which uses the baseline momentum ranking (lookback) interval when the S&P 500 Index is above its 10-month simple moving average (SMA10) and a shorter lookback interval when the index is below its SMA10. To investigate, we look at average monthly return, standard deviation of monthly returns, monthly reward/risk (average divided by standard deviation), compound annual growth rate (CAGR) and maximum drawdown (MaxDD) as key performance statistics. Using monthly SACEMS returns for different lookback intervals since July 2006 and monthly levels of the S&P 500 Index since September 2005, all through December 2022, we find that: Keep Reading

Long Run Performance of Currently Popular Allocation Strategies

How would currently popular asset allocation strategies have performed back to the end of 1925? In their January 2023 paper entitled “A Century of Asset Allocation Crash Risk”, Mikhail Samonov and Nonna Sorokina test the following seven widely used asset allocation strategies back to 1926 using a combination of actual and modeled returns for many asset sub-classes and factors:

  1. U.S. 60/40 – 60% U.S. large caps and 40% U.S. aggregate bonds
  2. Global 60/40 – 60% global stocks and 40% global aggregate bonds
  3. Diversified Multi-Asset – 60% to equity asset classes (15% U.S. large caps, 5% U.S. small caps, 5% U.S. growth, 5% U.S. value, 10% U.S. REITs, 10% international developed markets, 10% emerging markets); 34% to fixed income (8% 10-year U.S. Treasuries, 8% U.S. municipal bonds, 8% U.S. investment grade corporate bonds, 5% international bonds, 5% emerging market bonds); and, 6% to commodities.
  4. Risk Parity – 33% of portfolio risk to each of U.S. large caps, 10-year U.S. Treasuries and commodities, leveraged to 11.4% target volatility (the retrospective volatility of U.S. 60/40).
  5. Endowment – per the 2020 National Association of College and University Business Officers report, 13% U.S. public equities, 13% non-U.S. public equities, 7.3% global equities, 13.5% private equity, 9.3% venture capital, 20% hedge funds, 12.3% fixed income and 11.1% real estate (with further breakdowns to sub-classes within these classes).
  6. Factor Investment – 70% U.S. 60/40 and 30% equally to 15 factor premiums.
  7. Dynamic Asset Allocation – either U.S. large cap stocks or U.S. aggregate bonds based on higher trailing 11-month returns, leveraged to 11.4% targeted volatility (retrospective volatility of U.S. 60/40) using 5-year rolling volatility (as in “Momentum in a Mean-variance Optimization Framework”).

They gather commercially and academically available asset return series. They extend series for 23 sub-asset classes and 15 long-short factor portfolios back to the end of 1925 based on similarity arguments and known-versus-extended return/volatility/correlation comparisons. They update all portfolio allocations monthly. They ignore rebalancing frictions, shorting costs, leverage costs, other costs of complex portfolio maintenance and administrative/management fees. Testing the seven strategies on actual and modeled data during December 1925 through December 2020, they find that:

Keep Reading

Login
Daily Email Updates
Filter Research
  • Research Categories (select one or more)